The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index; none of the 34 sources its claims cite is checked for support yet.
Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks
Open accessAbstract
The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis Symposium. The goal of the battle was to compare the performance of algorithms for the detection of cyber-physical attacks, whose frequency has increased in the last few years along with the adoption of smart water technologies. The design challenge was set for the C-Town network, a real-world, medium-sized water distribution system operated through programmable logic controllers and a supervisory control and data acquisition (SCADA) system. Participants were provided with data sets containing (simulated) SCADA observations, and challenged to design an attack detection algorithm. The effectiveness of all submitted algorithms was evaluated in terms of time-to-detection and classification accuracy. Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking. Results were presented at the Water Distribution Systems Analysis Symposium (World Environmental and Water Resources Congress) in Sacramento, California on May 21–25, 2017. This paper summarizes the BATADAL problem, proposed algorithms, results, and future research directions.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
BATADAL was a competition comparing seven attack-detection algorithms on simulated SCADA data from the C-Town water distribution network under cyber-physical attacks. The winning model-based approach by Housh and Ohar achieved the best overall ranking score (S = 0.970), while most algorithms outperformed naïve detection baselines.
Key findings
- Seven teams participated; all but one achieved a ranking score S > 0.75, better than naïve detection mechanisms.
- Housh and Ohar ranked first with S = 0.970, S_TTD = 0.965, S_CLF = 0.975, detecting all 7 test attacks; Abokifa et al. ranked second with S = 0.949.
- The only model-based approach won: estimating water demands, simulating system dynamics with EPANET, and measuring errors against SCADA readings proved successful.
- Aghashahi et al. ranked last with S = 0.534 and detected only 3 attacks; Chandy et al. was over-sensitive with many false alarms, while Pasha et al. lacked sensitivity.
Methods
- Design challenge used the C-Town network: 429 pipes, 388 junctions, 7 storage tanks, 11 pumps, 5 valves, 1 reservoir, and 9 PLCs coordinated by SCADA.
- Participants received three simulated SCADA datasets: Training dataset 1 with no attacks over 365 days at hourly time steps; Training dataset 2 with seven attacks over 492 hourly time steps; and a Test dataset with seven attacks over 407 hourly time steps.
- Algorithms were evaluated by time-to-detection (S_TTD), classification performance (S_CLF = mean of TPR and TNR), and overall ranking score S = 0.5·S_TTD + 0.5·S_CLF.
Limitations
- The paper states the evaluation was deterministic on three specific datasets representing only a small portion of possible cyber-attacks; robustness would require stochastic scenarios with varying hydraulic conditions and multiple attack sequences.
- The paper states a major limitation is the absence of detailed real SCADA data on cyber attacks to water utilities, and that BATADAL relied on data generated with a demand-driven engine.
- Evident from the text: participants were given the same computational model of C-Town used to generate the SCADA data, and demand patterns were fairly regular and consistent across datasets, which may limit real-world applicability.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Verification
Claims the paper makes
22 No full text of the sources · 14 Own interpretation · 8 No basis given · 5 Sources not found · 4 Own method · 3 Own result · 2 Not checked yet
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Interestingly, the BATADAL was won by the only model-based approach.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“With a value of S equal to 0.896, the algorithm proposed by Brentan et al. (2017) can also be regarded as a strong performer.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Algorithms performance
What the sources say
- Not checked — no full text of the sourcecites Brentan et al. (2017) source
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In this regard, it is important to note that BATADAL demand patterns were fairly regular and consistent across the three datasets.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that a naïve detection mechanism that predicts the system to be always in safe conditions gets a score S equal to 0.25 (S T T D = 0, S CLF = 0.5).”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Ranking score
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The algorithm ofAbokifa et al. (2017) comes a close second, with S equal to 0.949.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > SUPPLEMENTAL DATA
What the sources say
- Not checked — no full text of the sourcecites Abokifa et al. (2017) source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“With the exception of Brentan et al. (2017) and Pasha et al. (2017), there appears to be a strong correlation between these two metrics (see Fig -ure 3).”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
What the sources say
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“The group is led by the algorithm proposed byHoush and Ohar (2017b), which shows the best overall performance (S = 0.970).”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > SUPPLEMENTAL DATA
What the sources say
- Not matchedcites Housh and Ohar (2017b)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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- Not matched
In the paper
“As shown in Figure 4(b), Abokifa et al. (2017) algorithm disclosed Attack #10 and #11 as a single continuous episode, erroneously flagging the system as under attack for the period in between.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Algorithms performance
What the sources say
- Not checked — no full text of the sourcecites Abokifa et al. (2017) source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Only a few algorithms provided information on the attacked devices.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
plain- Asserted: passes — The authors put it forward as true.
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A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Among these, the algorithms proposed by Brentan et al. (2017) and Giacomoni et al. (2017) were the most accurate.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- It may state two separate claims as one. 20
- Probably not a substantial claim. 20
In the paper
“As shown in Figure 4(d), this algorithm was able to consistently and accurately detect most of the attacks, but it failed to identify the last one.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Algorithms performance
What the sources say
- Not checked — no full text of the sourcecites Brentan et al. (2017) source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“However, Giacomoni et al. (2017) algorithm is less sensitive, resulting in a higher number of false negatives and minor timing errors (see Figure 4(c)) that lead to a score S equal to 0.927.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Algorithms performance
What the sources say
- Not checked — no full text of the sourcecites Giacomoni et al. (2017) source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Their score S is equal to 0.802 and 0.773, respectively.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Algorithms performance
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“On the other hand, the algorithm of Pasha et al. (2017) issued just a few false alarms, but it lacked sensitivity, thus failing to flag the system as under attack for the entire duration of the events.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Algorithms performance
What the sources say
- Not checked — no full text of the sourcecites Pasha et al. (2017) source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Indeed, the detection trajectory depicted in Figure4(a) shows that all attacks were immediately detected, with the exception of the last one, which was disclosed a few hours after its starting time.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > SUPPLEMENTAL DATA
What the sources say
- Not matchedcites Housh and Ohar (2017b)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“The algorithm proposed by Giacomoni et al. (2017) has the same number of false positives and true negatives as that of Housh and Ohar (2017b)-meaning that both algorithms were the most successful in avoiding false alarms.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Algorithms performance
What the sources say
- Not checked — no full text of the sourcecites Giacomoni et al. (2017) source
- Not matchedcites Housh and Ohar (2017b)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Note that this algorithm is the top scorer in terms of both time-to-detection S T T D and classification score S CLF .”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > SUPPLEMENTAL DATA
What the sources say
- Not matchedcites Housh and Ohar (2017b)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“since the number of attacks in the available data is generally limited.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“The algorithm of Chandy et al. (2017) turned out to be over-sensitive-meaning that it was able to identify most of the attack instances, but at the cost of issuing numerous false alarms.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Algorithms performance
What the sources say
- Not checked — no full text of the sourcecites Chandy et al. (2017) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking.”
- Also stated in: § ATTACK DETECTION ALGORITHMS · plain
“Seven teams participated in BATADAL.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Abstract
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Also stated in: § ATTACK DETECTION ALGORITHMS · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All algorithms but one achieved a ranking score S larger than 0.75, meaning that they performed better than naïve detection mechanisms.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“On the other hand, flagging the system as always under attack yields a value of S equal to 0.75 (S T T D = 1, S CLF = 0.5).”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Ranking score
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“The definition of the cut-off criteria defining outliers regulates the trade-off between True Positive and True Negative Rate for most of the algorithms”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > FUTURE RESEARCH DIRECTIONS
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“Finally, the contribution of Aghashahi et al. (2017) detected only three attacks, leading to a score S equal to 0.534.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Algorithms performance
What the sources say
- Not checked — no full text of the sourcecites Aghashahi et al. (2017) source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Most teams presented multi-stage detection methods.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Protecting water distribution systems from cyber attacks requires (as with other cyberphysical systems) a combination of proactive and reactive mechanisms (Cardenas et al. 2008).”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites Cardenas et al. 2008 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“according to which the number of reported attacks on water infrastructures has been growing steadily (ICS-CERT 2014; ICS-CERT 2015; ICS-CERT 2016)-making them the third highest targeted sector after critical manufacturing and energy (ICS-CERT 2016).”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > INTRODUCTION
What the sources say
- Not matchedcites ICS-CERT 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites ICS-CERT 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites ICS-CERT 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“The adoption of such smart water technologies plays a pivotal role in enhancing the reliability and autonomy of water distribution systems, but simultaneously exposes them to cyber-physical attacks (Rasekh et al. 2016)”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites Rasekh et al. 2016 source
boosteddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that the information provided by SCADA systems may not be fully reliable.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > INTRODUCTION
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- Rests on an assumption: All networked devices, including SCADA systems, represent potential targets.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Comparing and confirming the detection issued by different modules can help decrease classification errors.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We can probably conclude that both model-based and data-driven approaches are suitable for attack detection problems, although their performance would probably vary with the modelling context at hand.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
hedgedconditional- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“While the data and algorithms presented here provide a first step towards an objective comparison of attack detection algorithms for water distribution systems, they do not represent the entire spectrum of modelling contexts that practitioners and researchers would encounter.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > CLOSURE
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since attack detection mechanisms aim to disclose outliers and contextual anomalies in the system behavior, they may accidentally disclose anomalous behaviors that are not necessarily caused by cyber attacks.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > FUTURE RESEARCH DIRECTIONS
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The latter were aimed at manipulating the information sent or received by sensors and PLCs, with the ultimate goal of affecting the operations of an actuator (Urbina et al. 2016).”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Development data
What the sources say
- Not checked — no full text of the sourcecites Urbina et al. 2016 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The evaluation of BATADAL algorithms can be seen as a deterministic analysis carried out on three specific datasets, which represent only a small portion of the entire set of cyber-attacks that could threaten a water distribution system.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > FUTURE RESEARCH DIRECTIONS
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The first two datasets, hereafter named Training dataset 1 and Training dataset 2, were provided at the beginning of the competition, while the third one (Test dataset) was subsequently used to evaluate and rank the attack detection algorithms.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Development data
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both time-to-detection and classification score are important aspects of performance.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is a rather challenging task due to the intrinsic correlation among the hydraulic variables.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > FUTURE RESEARCH DIRECTIONS
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The solutions provided by seven teams suggest that timely and accurate detection can be obtained by both modelbased and data-driven approaches, usually made of multiple sequential stages.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > CLOSURE
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“SCADA concealment was performed by either replacing actual traffic information between PLCs and SCADA with previously-recorded data (replay attacks) or adding an offset to the transmitted sensor readings (Urbina et al. 2016).”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Development data
What the sources say
- Not checked — no full text of the sourcecites Urbina et al. 2016 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“A limitation of this battle is its reliance of data generated with a demand-driven engine (Taormina et al. 2017).”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > FUTURE RESEARCH DIRECTIONS
What the sources say
- Not checked — no full text of the sourcecites Taormina et al. 2017 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It appears that consistency checks and the analysis of control rules should lead to the identification of the simplest attacks.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A key aspect of the dataset is the absence of cyber attacks, which made it suitable for studying the operations of the water distribution system under normal operating conditions.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Development data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The operations of the water system were altered through malicious activation of hydraulic actuators, change of actuator settings, and deception attacks-amongst the most common for cyber-physical systems (Cardenas et al. 2009).”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Development data
What the sources say
- Not matchedcites Cardenas et al. 2009
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The inherent interdependence of the elements in the water network should theoretically allow for the detection of anomalies, even when the adversary tries to conceal his (her) actions by altering the SCADA readings of one or a few deployed sensors.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > General Observations
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
All networked devices, including SCADA systems, represent potential targets.
“Third, all networked devices, including SCADA systems, represent potential targets.”
The paper relies on this without support.
It is not possible to rule out all attacks.
“Since it is not possible to rule out all attacks, cyber-physical systems should also be equipped with intrusion detection schemes that assist with the recovery phase (Anderson 2010).”
The paper relies on this without support.
The simulated SCADA data and attacks in the BATADAL datasets adequately represent real-world cyber-physical attacks on water distribution networks.
“Participants were provided with datasets containing (simulated) SCADA observations, and challenged with the design of an attack detection algorithm.”
“The datasets were generated via simulation with epanetCPA, a Matlab toolbox that allows to design a variety of cyber attacks and simulate, with EPANET2 (version 2.0.12), the hydraulic response of a water distribution network; see Taormina et al. (2017) for further details.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > Algorithms performance
“With a value of S equal to 0.896, the algorithm proposed by Brentan et al. (2017) can also be regarded as a strong performer.”
§ THE BATTLE OF THE ATTACK DETECTION ALGORITHMS 1 > SUPPLEMENTAL DATA
“| 4 | Brentan et al. | 6 | 0.894 0.857 0.931 362 45 1637 45 | | |”
Citations
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Cited by
Sorted by citations
Items 101–200 of 279
Raza, N., & Moazeni, F. K. (2025) · Water Research · doi:10.1016/j.watres.2025.123517 · Cited by 12
Tama, B. A., Lee, S. Y., & Lee, S. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09767-y · Cited by 14
Parajuli, U., & Shin, S. (2024) · AQUA - Water Infrastructure Ecosystems and Society · doi:10.2166/aqua.2024.264 · Cited by 14
Huang, M., Li, W., He, J., Lan, X., Li, T., & Zhang, N. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00498-5 · Cited by 10
“The work uses the mainstream data set BATADAL dataset [43, 44] in the field of industrial cyber-physical system network security.”
STMBAD: Spatio-Temporal Multimodal Behavior Anomaly Detector for Industrial Control Systems
Luo, J., Yan, C. Y., Cai, J., Fang, W., & Zheng, W. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3528559 · Cited by 10
Decision Support System for Cyber Attack Diagnosis in Smart Water Networks
Quiñones-Grueiro, M., Prieto-Moreno, A., Verde, C., & Llanes‐Santiago, O. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.01.024 · Cited by 10
EPASAD: ellipsoid decision boundary based Process-Aware Stealthy Attack Detector
Maurya, V., Agarwal, R., Kumar, S., & Shukla, S. K. (2023) · Cybersecurity · doi:10.1186/s42400-023-00162-z · Cited by 10
“each attack scenario are provided in the paper [26].”
BackgroundMethodUkwuoma, H. C., Dusserre, G., Coatrieux, G., & Vincent, J. (2024) · Data Science and Management · doi:10.1016/j.dsm.2024.04.002 · Cited by 10
A Review of Deep Learning Applications for Sustainable Water Resource Management
Tanchangya, T., Raihan, A., Rahman, J., et al. (2024) · Global Sustainability Research · doi:10.56556/gssr.v3i4.1043 · Cited by 9
“Erba et al. (2020) applied adversarial machine learning to detection systems, however, Taormina et al. (2018) suggest testing on real-world cyber-attack situations.”
Ramachandran, A., Gayathri, K., Alkhayyat, A. H., et al. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.034438 · Cited by 9
Murillo, A., Taormina, R., Tippenhauer, N. O., & Galelli, S. (2023) · Journal of Water Resources Planning and Management · doi:10.1061/jwrmd5.wreng-5854 · Cited by 10
Self-citationMoazeni, F. K., & Khazaei, J. (2022) · Energies · doi:10.3390/en15134832 · Cited by 9
Enhanced Anomaly Detection for Cyber-Attack Detection in Smart Water Distribution Systems
Stojanović, B., Neuschmied, H., Winter, M., & Kleb, U. (2022) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3538969.3543796 · Cited by 10
“This list is extended with the results of the papers that followed this research and used BATADAL dataset for evaluation.”
BackgroundMethodResultInfluential[SoK] Evaluations in Industrial Intrusion Detection Research
Lamberts, O., Wolsing, K., Wagner, E., Pennekamp, J., Bauer, J., Wehrle, K., & Henze, M. (2023) · Journal of Systems Research · doi:10.5070/sr33162445 · Cited by 10
Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges
Kuchař, K., & Fujdiak, R. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3512857 · Cited by 11
Gao, L., Deng, X., & Yang, W.-M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05733-0 · Cited by 8
SMS-I: Intelligent Security for Cyber–Physical Systems
Maia, E., Sousa, N., Oliveira, N. R. B. D., et al. (2022) · Information · doi:10.3390/info13090403 · Cited by 8
Machine learning and user interface for cyber risk management of water infrastructure
Neshenko, N., Bou‐Harb, E., Furht, B., et al. (2023) · Risk Analysis · doi:10.1111/risa.14209 · Cited by 8
“Multivariate perspective : Feature importance algorithms can serve as a medium for anomaly localization for the meth-ods that solely rely on empirical data (Taormina et al., 2018).”
BackgroundAlmazyad, I., Shao, S., Hariri, S., et al. (2023) · ACS/IEEE International Conference on Computer Systems and Applications · doi:10.1109/aiccsa59173.2023.10479312 · Cited by 7
“BATtle of the Attack Detection Algorithms (BATADAL) is a dataset that focuses primarily on protecting drinking water distribution systems [10], [11].”
BackgroundSomma, M., Flatscher, A., & Stojanović, B. (2024) · Telecommunications Forum (TELFOR) · doi:10.1109/telfor63250.2024.10819044 · Cited by 8
SCADANet: A novel dataset for SCADA cybersecurity and intrusion detection
Algül, E., Doğan, F., Ahmad, A. A., et al. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112087 · Cited by 7
Lenzi, S., Terenghi, G., Taormina, R., et al. (2019) · Proceedings of the ... International Conference on Auditory Display · doi:10.21785/icad2019.044 · Cited by 7
“Thirdly, anomaly detection algorithms are still exposed to a certain degree of inaccuracy which results in the production of false alerts, with false positive alerts highly impacting on daily operations [2].”
BackgroundMethodInfluentialSelf-citationCriticality of Isolation Valves in Water Distribution Networks with Hydraulics and Topology
Wéber, R., Huzsvár, T., Déllei, Á., & Hős, C. (2023) · Water Resources Management · doi:10.1007/s11269-023-03488-y · Cited by 7
Systematic and Efficient Anomaly Detection Framework using Machine Learning on Public ICS Datasets
Baptiste, M., Julien, F., & Franck, S. (2021) · 2021 IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr51186.2021.9527911 · Cited by 8
Explainable hybrid intrusion detection for SCADA/ICS: a review and research agenda
Skrodelis, H. K., & Romānovs, A. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1724245 · Cited by 13
Maurya, V., & Shukla, S. K. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104147 · Cited by 6
Fuzzy Bayesian Learning for Cyber Threat Hunting in Industrial Control Systems
Marsh, K., & Gharghasheh, S. E. (2021) · Springer eBooks · doi:10.1007/978-3-030-74753-4_8 · Cited by 6
Rodríguez-Martínez, C., Quiñones-Grueiro, M., & Llanes‐Santiago, O. (2023) · Journal of Water Resources Planning and Management · doi:10.1061/jwrmd5.wreng-5302 · Cited by 6
Moradpoor, N., Maglaras, L. A., Abah, E., et al. (2023) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot58021.2023.00077 · Cited by 6
“For their experiments they focused on two datasets related to water systems: WADI [2] and BATADAL [14].”
MethodBreaking the flow and the bank: Stealthy cyberattacks on water network hydraulics
Albustami, A. A., & Taha, A. F. (2025) · Water Research · doi:10.1016/j.watres.2025.123719 · Cited by 5
“The Battle of the Attack Detection Algorithms (BATADAL) marked a significant milestone in developing and testing effective detection methods for cyber-physical attacks on WDNs [39].”
Self-citationWagner, E., Bader, L., Wolsing, K., et al. (2024) · doi:10.1145/3714393.3726006 · Cited by 5
Systematic review and characterisation of malicious industrial network traffic datasets
Dobler, M., Hellwig, M., Lopes, N., Oakley, K., & Winterburn, M. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01126-9 · Cited by 5
Holasová, E., Fujdiak, R., & Mišurec, J. (2024) · Algorithms · doi:10.3390/a17050208 · Cited by 5
Horvath, A.-S., & Pouliou, P. (2024) · CRC Press eBooks · doi:10.1201/9781003425724-46 · Cited by 7
Pandian, C. S., & Alphonse, P. J. A. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01285-1 · Cited by 5
A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring
Gross, M.-P., Taormina, R., & Cominola, A. (2024) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2024.106247 · Cited by 6
Self-citationGraph Optimization via Decoupled Edge Tuning for Efficient Industrial Anomaly Detection
Wang, K., Lu, S., Liu, Y., & Wang, B. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3567671 · Cited by 5
“The following datasets are used to evaluate the LGSA’s anomalydetectionability:SecureWaterTreatment(SWaT)[46], WAterDIstribution(WADI),CriticalInfrastructureSecurity Showdown (CISS), Battle of the Attack Detection Algorithms [47] and Petrol-Chemical Process (PCP).”
Hammad, E. M., Nag, A. K., Chennamaneni, A., Aghashahi, M., & Doğdu, E. (2021) · Resilience Week (RWS) · doi:10.1109/rws52686.2021.9611790 · Cited by 8
Self-citationSha, Y. D., Chen, Z., Liu, X., Yan, Y. G., Du, C., Liu, J., & Han, R. (2022) · Applied Sciences · doi:10.3390/app12178889 · Cited by 6
A dataset collected in real-world industrial control systems for network attack detection
Zhou, X., Cheng, Z., Wang, C., Wang, S., Tao, C., Zhou, Z., Chen, X., Luo, J., Wang, D., & Zhou, H. (2026) · Scientific Data · doi:10.1038/s41597-026-06738-x · Cited by 8
Flood Risks of Cyber‐Physical Attacks in a Smart Storm Water System
Lin, C., Yang, Y. E., & Moazeni, F. K. (2024) · Water Resources Research · doi:10.1029/2023wr034827 · Cited by 5
“The scope of FDI encompasses the corruption of data at the sensor level or during the transmission phase between sensors and the SCADA system (Taormina et al., 2018).”
BackgroundPereira, T. C., Andrade-Campos, A. G., & Vilanova, R. (2025) · IEEE Access · doi:10.1109/access.2025.3639006 · Cited by 4
“For the application of anomaly and attack detection, it is standard to utilize specialized datasets such as SWaT [81], WADI [82], and BATADAL [83], as well as other testbeds (e.g., [80]).”
Hyperparameter Tuned Cloud Based Cyber Physical Attack Detection using Stacking Ensemble Learning
Sinha, R., Agrawal, P., & Rasool, A. (2024) · IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) · doi:10.1109/sceecs61402.2024.10482067 · Cited by 4
“Three distinct network topologies are available for this technology: inline, via a network tap, and directly linked to the switch-spanning port [9].”
BackgroundSikder, M. N. K., Wang, Y., & Batarseh, F. A. (2025) · IEEE Access · doi:10.1109/access.2025.3577969 · Cited by 4
“To counter such threats, initiatives such as the BATtle of the Attack Detection ALgo-rithms (BATADAL), which employs EPANET 2 [34], [35], [36], have been established.”
Chandran, P. S., & Sunil, K. S. (2025) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2025.108131 · Cited by 4
Hierarchical GNN Message Passing for Node-Level Anomaly Detection in Industrial Control Systems
Lu, S., Wang, K., Liu, Y., & Wang, B. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3623299 · Cited by 4
“…[55], the Critical Infrastructure Security Showdown dataset (CISS), the Water Distribution dataset (WADI: A water distribution testbed for research in the design of secure cyber physical systems), the Battle of Attack Detection Algorithms dataset (BATADAL) [56] and the Car Hacking Dataset (CHD).”
Vialatoux, C. F. , & Parrend, P. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.690 · Cited by 4
Time series processing-based malicious activity detection in SCADA systems
Zaslavski, M., & Kalech, M. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101355 · Cited by 6
Detecting and explaining anomalies in industrial Internet of things systems using an autoencoder
Levshun, D., Levshun, D. S., & Kotenko, I. V. (2025) · Ontology of Designing · doi:10.18287/2223-9537-2025-15-1-96-113 · Cited by 5
“В качестве экспериментальных наборов данных используются BATADAL 6 [38 ] и HAI [39 ].”
InfluentialAkande, A. J., Hóu, Z., Foo, E., & Li, Q. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104455 · Cited by 5
A Novel Classification Technique based on Formal Methods
Canfora, G., Mercaldo, F., & Santone, A. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3592796 · Cited by 4
“With the following case study, we investigate whether the property automatically learned by the proposed method is able to detect the presence of an ongoing attack from the SCADA readings: we experiment with a data-set [17] composed from the water levels of the four tanks (i.”
MethodDesign and Realization of Testbeds for Security Research in the Industrial Internet of Things
Tippenhauer, N. O. (2019) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-030-12330-7_14 · Cited by 4
Self-citationMin, K. W., Choi, Y. H., Al-Shamiri, A. K., et al. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-31967-0_10 · Cited by 4
(2025) · doi:10.1016/j.iot.2026.101938 · Cited by 31
Graph attention networks with physical constraints for anomaly detection
Homaei, M., Khazrak, I., Molano, R., et al. (2026) · ICT Express · doi:10.1016/j.icte.2026.05.004 · Cited by 3
“In recent years, they face more cyber-physical attacks like data manipulation or actuator control [1, 2].”
Software security models and frameworks: an overview and current trends
Korir, F. C. (2023) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2023.8.2.0078 · Cited by 3
Walita, T., Erba, A., Castellanos, J. H., et al. (2023) · doi:10.1145/3592538.3594271 · Cited by 3
“Several autoencoder architectures were proposed in prior work, Long Short Term Memory (LSTM) [16], Fully connected [33] and Convolutional Neural Networks (CNN) [22].”
BackgroundMethodInfluentialSelf-citationAkande, A. J., Hóu, Z., Foo, E., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7584-6_19 · Cited by 3
“After collating the dataset [8], we implement our framework to learn a pattern from the dataset indicative of the attack carried on the system.”
MethodEnhancing Adversarial Robustness of Anomaly Detection-Based IDS in OT Environments
Flatscher, A., Stojanović, B., & Özdenizci, O. (2024) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm62983.2024.10814511 · Cited by 3
“Experimental part of the work is based on The BATtle of the Attack Detection ALgorithms (BATADAL) dataset, originally created for an intrusion detection system challenge [10].”
Intelligent sensor data analysis through hybrid deep hierarchical clustering for anomaly detection
Lachure, J., & Doriya, R. (2024) · Transactions of the Institute of Measurement and Control · doi:10.1177/01423312241299859 · Cited by 3
Reconstruction-based Multi-Scale Anomaly Detection for Cyber-Physical Systems
Dong, Z., Liu, K., Han, D., Cao, Y., & Xia, Y. (2022) · International Conference on Industrial Artificial Intelligence (IAI) · doi:10.1109/iai55780.2022.9976844 · Cited by 5
“The BATADAL dataset is a small-scale simulation dataset with 43 variables generated by the epanetCPA toolbox [25].”
MethodAnomaly Detection in Industrial Control System using FSODCONV Method
Lachure, J., & Doriya, R. (2023) · International Conference on Information Science and Systems (ICISS) · doi:10.1145/3625156.3625191 · Cited by 3
“…collected 14 days of data under regular operation, plus two days of data with BATtle of the Attack Detection ALgorithms (BATADAL): It is a platform to evaluate how well algorithms detect cyber-attacks in WDS. C-Town C-Town Public Utility (CPU) is a water distribution service in C-Town [12].”
MethodStanford, C., & Tanner, A. L. (2023) · doi:10.1109/csce60160.2023.00266 · Cited by 3
“It is important to note that the second dataset was only partially labeled by the authors for more realistic simulation when using supervised learning models [2].”
BackgroundMethodResultInfluentialLi, P., Wu, M., Zhang, Y., Xia, J., & Wang, Q. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3448505 · Cited by 4
“1) Process Miner’s Rare Event Detection Data Set: PMRED is a real data set collected from industrial sensor equipment in an intelligent paper mill, used for predicting rare anomaly events in multivariate time series of industrial equipment.”
IIoT’s Risk Odyssey: Navigating the Risk Propagation of Illegal Information Flows
Anagnostopoulou, A., Mavridis, I. K., Athanasopoulos, M., Mylonas, A., & Gritzalis, D. A. (2025) · IEEE Access · doi:10.1109/access.2025.3555873 · Cited by 3
“Public datasets such as BATADAL [36], SWaT [37], and WADI [38] primarily concentrate on SCADA systems within power grids and fail to provide specific features related to power system measurements, protocols, or new smart devices like smart inverters, IEDs, and communication infrastructure.”
Sharma, N. V., & Ramachandran, R. K. (2019) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip47207.2019.8985806 · Cited by 3
“[25]proposed EPANET simulation model and used modeling framework to analyse simulation based and real time based attack on the SCADA system.”
MethodA Novel Approach for Detection and Location of Cyber-Attacks in Water Distribution Networks
Martínez, C., Quiñones-Grueiro, M., Verde, C., & Llanes‐Santiago, O. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89691-1_9 · Cited by 3
Al-Abassi, A. (2020) · The Atrium (University of Guelph) · Cited by 3
Toward Efficient Protecting Cyber-Physical Systems with Cyber Threat Hunting and IntelligencePreprint
Khordadpour, P. (2023) · doi:10.36227/techrxiv.22009226.v1 · Cited by 3
A Thorough Cybersecurity Dataset for Intrusion Detection in Smart Water Networks
Taormina, R., Murillo, A., Tippenhauer, N. O., & Galelli, S. (2022) · Water Distribution Systems Analysis Conference (WDSA) · doi:10.4995/wdsa-ccwi2022.2022.14793 · Cited by 2
Self-citationBonagura, V., Pisani, J., Ferrato, A., Foglietta, C., Cavone, G., & Pascucci, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-84260-3_20 · Cited by 2
Enhancing Resilience in IoT Water Systems Using Data-Intelligence and Decentralization
Mahmoud, H., Wu, W., Gaber, M. M., et al. (2024) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2300275 · Cited by 2
A deep learning-based adaptive cyber disaster management framework
Neshenko, N., Bou‐Harb, E., Furht, B., & Baghersad, M. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01241-3 · Cited by 3
“Although we considered the proposed method in the context of the water treatment sys-tem, we are now gathering empirical evidence on how it can be applied to tasks within various critical infrastructure systems, including water distribution systems [68].”
Adversarial feature generation for ML-based intrusion detection in the petrochemical industry
Naqvi, S. S. A., Zhou, C., Xu, P., Li, Y., Jiashu, J., & Uzair, M. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104215 · Cited by 2
Koşucu, M. M. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132206 · Cited by 2
Lee, J., Charmchi, A. S. T., Ghobadi, F., & Kim, M. I. (2026) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2026.100697 · Cited by 2
“The BATADAL dataset, developed for an international attack detection competition [12], provides a comprehensive set of WDS data explicitly designed for benchmarking security algorithms.”
Rasheed, I., Mostafa, H., & Mohayudin, G. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112320 · Cited by 2
Machine learning for power-grid cybersecurity: a review
Adewusi, M. A., & Mustafa, M. M. (2026) · Discover Computing · doi:10.1007/s10791-026-10162-5 · Cited by 6
“…researchers sometimes validate methods on publicly released OT testbeds from adjacent critical-infrastructure domains, such as the Secure Water Treatment (SWaT) dataset, the Water Distribution (WADI) dataset, and the BATtle of the Attack Detection ALgorithms (BATADAL) benchmark [12, 13, 14].”
Intrusion Detection Datasets for IIoT and ICS: A Taxonomic Review with a Decision-Aid Scoring Rubric
Termanini, A., Bourdoucen, H., Al‐Abri, D., & Maashri, A. A. (2026) · Sensors · doi:10.3390/s26134099 · Cited by 7
“The strongest choices are Edge-IIoTset [39], X-IIoTID [41], MSU-PWR [29,30], SWaT [19], MSU-GP [27,28], ICS-Flow [11], and BATADAL [25].”
Alharbi, K. (2026) · Sustainability · doi:10.3390/su18147337 · Cited by 2
Radovanović, D., Savanović, N., Janaćković, J., et al. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10090285 · Cited by 2
RemOD: Operational Drift-Adaptive Intrusion Detection
Maurya, V., Rani, N., & Shukla, S. K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22829-2_17 · Cited by 2
Adversarial Attacks on Leakage Detectors in Water Distribution Networks
Stahlhofen, P., Artelt, A., Hermes, L., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43078-7_37 · Cited by 2
EPANET INP Code for Incomplete Mixing Model in Cross Junctions for Water Distribution Networks
Cervantes, D. H., Arciniega-Nevárez, J. A., Ramos, H. M., et al. (2023) · Water · doi:10.3390/w15244253 · Cited by 2
Obaid, A., & Mubarak, M. A. (2024) · Studies in systems, decision and control · doi:10.1007/978-3-031-62106-2_6 · Cited by 2
Advanced Detection of Cyber-Physical Attacks in Manufacturing Using LSTM-KNN
Penchalaiah, N., Kumar, A., Chaithrashree., A., Saivaraju, A., Bhoopathy, V., & Lamba, A. K. (2024) · International Conference on Data Science and Network Security (ICDSNS) · doi:10.1109/icdsns62112.2024.10690961 · Cited by 3
“Despite SCADA's many benefits to WDS, the centralized nature of its operations management, data storage, and analysis makes it susceptible to cyber-physical threats [7].”
BackgroundOn Practical Realization of Evasion Attacks for Industrial Control Systems
Erba, A., Murillo, A., Taormina, R., Galelli, S., & Tippenhauer, N. O. (2023) · doi:10.1145/3689930.3695213 · Cited by 2
“The physical anomalies collected are inspired by the attacks contained in the BATADAL dataset [46] (we use the same attack identifiers for the physical anomalies).”
InfluentialSelf-citationMirzaie, S., & Bushehrian, O. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06716-3 · Cited by 2
“Daniel et al. [22] used the full label BATADAL [23] dataset to identify anomalies in WDNs by applying several traditional anomaly detection approaches and then, proposed an ensemble technique.”
Toward Identifying Cyber Dependencies in Water Distribution Systems Using Causal AI
Sobien, D., Kulkarni, A., & Batarseh, F. A. (2024) · Journal of Water Resources Planning and Management · doi:10.1061/jwrmd5.wreng-6488 · Cited by 2
Integrating Self-Organizing Map and Graph Neural Networks to Detect Anomalies in Time-Series Data
Su, P., Yuan, H., Lu, Z., & Chen, D. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3509632 · Cited by 2
“We use three public datasets SKAB [34], iTrsut-SWAT [35], and BATADAL [36] to assess the proposed framework.”
Modelling Smart City Cyber-Physical Water Supply Systems: Vulnerabilities, Threats and Risks
Fomin, N. A., & Meshcheryakov, R. V. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-1480-4_15 · Cited by 2
Model Checking for Real-Time Attack Detection in Water Distribution Systems
Mercaldo, F., Martinelli, F., & Santone, A. (2022) · Informatics and Automation · doi:10.15622/ia.21.2.1 · Cited by 2
Attack Sample Generation Algorithm Based on Dual Discriminant Model in Industrial Control System
Zhou, W., Cao, X., Li, X., Sha, Y. D., Chen, J., Yan, Y. G., Liu, X., & Kong, X.-M. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1828/1/012123 · Cited by 2
Cyber-Attack Detection and Localization for SCADA system of CPSs
Li, D., Tang, J., Wu, S., Zheng, Z., & Ng, S.-K. (2025) · 2025 IEEE/ACM Second International Conference on AI Foundation Models and Software Engineering (Forge) · doi:10.1109/forge66646.2025.00038 · Cited by 1
Bashir, H., & Rathi, S. (2025) · ISH Journal of Hydraulic Engineering · doi:10.1080/09715010.2024.2440721 · Cited by 1
Intelligent Data Refinement and Analysis of Real-World Cyber Attacks on SCADA Systems
Zhang, W., Dong, N., Choi, T., et al. (2025) · doi:10.1145/3679240.3734654 · Cited by 1
“Notable examples include water distribution datasets such as the Secure Water Treatment (SWaT) dataset [8], BATADAL [23] and WADI dataset [1], as well as smart grid dataset from Petr et al. [19] and gas pipeline dataset from Morris et al. [16].”
Rustam, F., Jurcut, A. D., & Salauddin, M. (2025) · IEEE Access · doi:10.1109/access.2025.3598996 · Cited by 1
“In this study, we use the BATADAL dataset, which is generated through a simulation [35].”
Restoring missing gaps and intervals via global consistency and local coherence
Hu, R., Li, D., Lou, J., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129862 · Cited by 1
Graph Transfer Learning-Based Attack Detection in Cyber-Physical Water Distribution Systems
Ahasan, M. R., Joad, F., Atat, R., Thompson, C., Serpedin, E., & Takiddin, A. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226298 · Cited by 2
“Specifically, we adopt the C-Town WDS benchmark dataset [14] from BATADAL [15], which is widely used for evaluating cyber-attack detection in WDSs.”
Bridging the Gap Between Model Assumptions and Realities in Leak Localization for Water Networks
Cognata, R. L., Piazza, S., & Freni, G. (2025) · Water · doi:10.3390/w17243502 · Cited by 1
Metadata
- Authors
- Taormina, R., Galelli, S., Tippenhauer, N. O., Salomons, E., Ostfeld, A., Eliades, D. G., Aghashahi, M., Sundararajan, R., Pourahmadi, M., Banks, M. K., Brentan, B. M., Campbell, E., Lima, G., Manzi, D., Ayala-Cabrera, D., Herrera, M., Montalvo, I., Izquierdo, J., Luvizotto, E., Chandy, S. E., Rasekh, A., Barker, Z. A., Campbell, B., Shafiee, M. E., Giacomoni, M., Gatsis, N., Taha, A., Abokifa, A. A., Haddad, K., Lo, C. S., Biswas, P., Pasha, M. F. K., Kc, B., Somasundaram, S. L., Housh, M., & Ohar, Z.
- Affiliations
- Singapore University of Technology and Design (SG)
- Independent Water Resources Consultant, OptiWater, 6 Amikam Israel St., Haifa 3438561, Israel.
- Technion – Israel Institute of Technology (IL)
- University of Cyprus (CY)
- Texas A&M University (US)
- CRAN (FR)
- Berliner Wasserbetriebe (Germany) (DE)
- Universidade Estadual de Campinas (UNICAMP) (BR)
- Research Fellow, Dept. of Water, Irstea, BP 5095, 34196 Cestas, France.
- University of Bath (GB)
- Research Engineer, Ingeniousware GmbH, 76137 Karlsruhe, Germany.
- Universitat Politècnica de València (ES)
- Data Scientist, Sensus, Inc., 8601 Six Forks Rd., Suite 700, Raleigh, NC 27615.
- Lead R&D Engineer, Sensus, Inc., 8601 Six Forks Rd., Suite 700, Raleigh, NC 27615.
- Water Resources Engineer, Sensus, Inc., 8601 Six Forks Rd., Suite 700, Raleigh, NC 27615.
- Chief Data Scientist, Sensus, Inc., 8601 Six Forks Rd., Suite 700, Raleigh, NC 27615.
- The University of Texas at San Antonio (US)
- Washington University in St. Louis (US)
- California State University, Fresno (US)
- University of Haifa (IL)
- Corresponding author
- Galelli, Stefano
- ORCID
- Taormina, Riccardo
- Galelli, Stefano
- Tippenhauer, Nils Ole
- Salomons, Elad
- Ostfeld, Avi
- Eliades, Demetrios G.
- Sundararajan, Raanju
- Brentan, B. M.
- Campbell, Enrique
- Herrera, M.
- Montalvo, I.
- Izquierdo, J.
- Luvizotto, E.
- Rasekh, Amin
- Campbell, Bruce
- Shafiee, M. Ehsan
- Giacomoni, Marcio
- Gatsis, Nikolaos
- Taha, Ahmad
- Abokifa, Ahmed A.
- Lo, Cynthia S.
- Biswas, Pratim
- Pasha, M. Fayzul K.
- Kc, Bijay
- Housh, Mashor
- Venue
- Journal of Water Resources Planning and Management
- Published
- 2018-08
- Volume
- 144
- Issue
- 8
- ISSN
- 0733-9496 (ISSN-L)
- 1943-5452
- OpenAlex
- W2807219356
- Semantic Scholar
- 64815d88bf9bfb49e6e9e2bb9be6eac3d56ea7f6
- Cited by
- 277
- References
- 33
- Citations by year
- 2026: 49 · 2025: 45 · 2024: 38 · 2023: 31 · 2022: 29 · 2021: 36 · 2020: 25 · 2019: 19 · 2018: 5
- FWCI
- 13.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Water Resources Planning and Management (publishedVersion)
- Pure (University of Bath) (submittedVersion, other-oa)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Greater South Information System
- Funders
- Washington University in St. Louis
- European Commission (739551, HORIZON2020)
- National Research Foundation (NRF2014NCR-NCR001-40)
- Fonds National de la Recherche Luxembourg
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Qatar National Research Fund (NPRP8-1292-2-548)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Engineering › Civil and Structural Engineering › Water Systems and Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- cyberattack detection, water distribution systems, cyber-physical attacks, SCADA, programmable logic controller, attack detection algorithm, algorithm comparison, detection time, classification accuracy
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