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 201–279 of 279
Somma, M. (2025) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm67658.2025.11297553 · Cited by 1
“…P J415, P J14, P J422, F V2, S V2 Edge 3 L T5, L T6, L T7, F PU8, S PU8, F PU9, S PU9, P J302, P J306, F PU10, S PU10, F PU11, S PU11, P J307, P J317 In order to compare results to literature, some additional performance metrics proposed specifically for the BATADAL challenge [22] were adopted.”
Kravchenko, O. V., Arhatenko, T., Potapenko, S., & Kuba, T. (2025) · Energy Technologies & Resource Saving · doi:10.33070/etars.4.2025.04 · Cited by 1
Dynamic stealthy backdoor attack against anomaly detectors in industrial control systems
Jiang, Q., Zu, Y., Zhu, Z., Zhong, W., Xu, Y., Qian, Z., & Zhang, X. (2025) · Information Sciences · doi:10.1016/j.ins.2025.123066 · Cited by 1
GraphAE: Plant-informed Graph Autoencoder for ICS Anomaly Detection with SHAP-based Explanations
Heydari, V., & Nyarko, K. (2026) · IEEE International Conference on AI in Cybersecurity (ICAIC) · doi:10.1109/icaic67076.2026.11395672 · Cited by 1
“Public ICS testbeds and datasets—SWaT [1], WADI [2], and BATADAL [3]—enable reproducible comparisons.”
Xu, J., Hu, C., Yan, X., & Gong, W. (2026) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70661 · Cited by 2
“Asignificant breakthrough in the field was the organization of the BATADAL competition [7].”
InfluentialWDSEventDB: A Real-Time Benchmark Dataset for Event Diagnosis in Water Distribution Networks
Raza, N., Iwakin, O. M., Villacres, D., Putri, S. A., & Moazeni, F. K. (2026) · World Environmental and Water Resources Congress (EWRI) · doi:10.1061/9780784486931.069 · Cited by 1
Naqvi, S. S. A., Zhou, C., Xu, P., Li, Y., & Uzair, M. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3691338 · Cited by 1
“For baseline comparisons on BATADAL, we note the results reported by participants in the original competition [36] and recent research [37], which show detection accuracies around 87%–91% for the best models.”
InfluentialOberascher, M., Brentan, B., Menapace, A., Herrera, M., Fu, G., Taormina, R., & Sitzenfrei, R. (2026) · Water Research X · doi:10.1016/j.wroa.2026.100569 · Cited by 2
Self-citationIntelligent Security Hardening of SCADA Systems Using Machine Learning Algorithms
Atıcı, S., & Tuna, G. (2026) · International Electronic Conference on Machines and Applications (IECMA) · doi:10.3390/engproc2026145005 · Cited by 1
Akinsowon, T., Jinad, R., Rasheed, A. A., et al. (2026) · Applied Sciences · doi:10.3390/app16168005 · Cited by 1
Lenzi, S., Terenghi, G., Meacci, D., Fernandez-de-Leceta, A. M., & Ciuccarelli, P. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1254678 · Cited by 1
Raza, N., Moazeni, F. K., & Khazaei, J. (2024) · World Environmental and Water Resources Congress (EWRI) · doi:10.1061/9780784485477.096 · Cited by 1
Raza, N., Moazeni, F. K., & Khazaei, J. (2024) · World Environmental and Water Resources Congress (EWRI) · doi:10.1061/9780784485477.099 · Cited by 1
Lachure, J., & Doriya, R. (2025) · SN Computer Science · doi:10.1007/s42979-025-04028-8 · Cited by 1
“Traditional detection methods lack the robustness to address these challenges, necessitating the development of more sophisticated and adaptive ML models [5].”
InfluentialRENEWABLE ENERGY INFRASTRUCTURE: VULNERABILITY MAPPING AND PREDICTIVE RISK MODELING
Varbanov, V., & Atanasova, T. V. (2025) · International Multidisciplinary Scientific GeoConference SGEM ... · doi:10.5593/sgem2025/4.1/s16.18 · Cited by 1
Abnormal Traffic Detection of Industrial Edge Network Based on Deep Nature Learning
Liu, Q., Zhang, B., Zhao, J., Zang, C., Wang, X., & Li, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78612-0_50 · Cited by 1
Malware Detection in Industrial Scenarios Using Machine Learning and Deep Learning Techniques
Gómez, Á. L. P., Maimó, L. F., Celdrán, A. H., & Clemente, F. J. G. (2021) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-7998-7789-9.ch005 · Cited by 1
Liu, X., Kong, X.-M., Zhang, X.-N., Luan, H., Yan, Y. G., Sha, Y. D., Li, K.-L., Cao, X., & Chen, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/9248267 · Cited by 1
“-ere were 11 data sets used in experiment, including Batadal data set [40], CICIDS 2017 [28], Mississippi data set [27], Oil Depot data set (self-build data set), Csgo data set [30], Mail data set [41], Water Quality data set [42], Wine data set [29], Mobile Phone Price data set [43], Mnist in Csv data set [44], and M…”
MethodRetractedKravchik, M., & Shabtai, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02741 · Cited by 1
Toward Efficient Protecting Cyber-Physical Systems with Cyber Threat Hunting and IntelligencePreprint
Khordadpour, P. (2023) · doi:10.36227/techrxiv.22009226 · Cited by 1
Proceedings of the 9th ACM Cyber-Physical System Security Workshop
(2023) · doi:10.1145/3592538 · Cited by 1
Intrusion Detection Datasets for Cyber-Physical Systems: Taxonomy, Challenges, and OpportunitiesPreprint
Quincozes, V. E., Quincozes, S. E., Neves, C. D. A. V., Passos, D. G., & Mossé, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5247519 · Cited by 1
Min, K. W., Choi, Y. H., & Kim, J. H. (2019) · Proceedings of the IAHR World Congress · doi:10.3850/38wc092019-1795
“…simulate these Cyber-attack model have been developed (Taormina et al. 2017), Extensions to these studies have developed algorithms that can be detected when cyber-attacked (Taormina et al. 2018) and technique to detect water quality abnormalities in WDSs has been developed (Perelman et al. 2012).”
MethodMin, K. W., Choi, Y. H., & Kim, J. H. (2020) · Korean Society of Hazard Mitigation · doi:10.9798/kosham.2020.20.5.47
“388개의 수요 절점, 429개의 관로, 7개의 탱크, 11개의 펌프, 4개의 밸브, 9개의 PLC와 1개의 SCADA로 구성된 C-town 관망은 The BATtle of the Attack Detection Algorithms (BATADAL) 에서 소개되었다(Taormina et al., 2018).”
Concluding Remarks and Future Outlook
Ferrari, R. (2021) · Lecture notes in control and information sciences · doi:10.1007/978-3-030-65048-3_16
Abokifa, A. A., Maheshwari, A., Gudi, R. D., et al. (2021) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001451
Self-citationSha, Y. D., Chen, J., Gan, J., Yan, Y. G., Liu, X., & Wang, H. (2022) · Mobile Information Systems · doi:10.1155/2022/1148518
Anomaly detection in multivariate time series using ensemble method
Liu, Y. (2021) · doi:10.32657/10356/155731 · Cited by 1
Cyber Situational Awareness for Industrial Control Systems (ICSs) Deployed in Smart City
Neshenko, N., Bou‐Harb, E., & Furht, B. (2022) · Springer eBooks · doi:10.1007/978-3-031-18464-2_4
MAD-SGS: Multivariate Anomaly Detection with Multi-scale Self-learned Graph Structures
Tang, J., Li, D., & Zheng, Z. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-2275-4_2
Topological Data Analysis-Based Replay Attack Detection for Water Networks
Kuşkonmaz, B., Wiśniewski, R., & Kallesøe, C. S. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.07.199 · Cited by 4
“To prevent the effects of the cyber-attacks in water network, a competition called the BATADAL was initiated (Taormina et al., 2018).”
BackgroundMultivariate Deep Neural Networks for Anomaly Detection in Water Distribution Systems
Tharsanee, R. M., Soundariya, R. S., Vishnupriya, B., Gopal, S. M. N., & Aswini, K. R. N. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-8476-9_16
Diagnosis test selection for distributed systems under communication and privacy constraints
Sztyber, A., Chanthery, É., Travé-Massuyès, L., & Pérez-Zúñiga, G. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06543-w
Du, X., Zhou, C., Tian, Y., & Xie, J. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02700-2
“BATADAL dataset [55], publicly accessible, is specifically tailored for research in cyber-physical system security, with a particular emphasis on water distribution networks.”
A survey of ML Resources for IEC 61850 in Power Grid Security
Baranwal, K., & Dagale, H. (2025) · doi:10.1109/ids66066.2025.00009
Chandran, P. S., & Sunil, K. S. (2025) · doi:10.1109/ictest64710.2025.11042542
“Similarly, Taormina and Galelli [26] explored using deep neural networks for real-time anomaly detection in water networks, but their model’s performance was hindered by noisy operational data.”
InfluentialNagendra, P., & Humerakhanam, M. (2025) · AIP conference proceedings · doi:10.1063/5.0279422
Federated Learning-Driven Intrusion Detection for Cybersecurity in Smart Distribution system
Gupta, P., Sengupta, B., & Nandi, S. (2024) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshp64532.2024.11101136 · Cited by 1
“FL-DIDS’s efficacy was evaluated using the BATADAL Dataset, designed to test intrusion detection in water distribution networks across scenarios like denial-of-service (DoS) attacks, false data injection, and sensor manipulation [21].”
Deconstructing predictability: A granular approach to input selection for water demand forecastingPreprint
Zanfei, A., Herrera, M., Brentan, B., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5721403
Self-citationAssessing Water Distribution Systems' Vulnerability to False Data Injection Attacks
Raza, N., & Moazeni, F. K. (2025) · American Water Works Association · doi:10.1002/awwa.70009
Flowing Secure: Proactive AI for Cyberattack Detection in Water Systems
AktaŞ, A., & Kılınçer, İ. F. (2025) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap68205.2025.11222329
WDSEventDB: A real-time benchmark dataset for event diagnosis in water distribution networks
Raza, N., Iwakin, O. M., Daniela, V., et al. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.17547954
Physics-Augmented Autoencoder-Based Cyber-Attack Detection for Critical Water Infrastructure
Petrovic, K., Stojanovic, B., & Saukh, O. (2025) · International Conference on the Internet of Things (IoT) · doi:10.1145/3770501.3770507
Virtual Real Fusion in ICS: A Seven-Tuple Representation and Shadow to Real Promotion
Diao, C., Zhou, G., Xu, L., et al. (2025) · doi:10.1109/icbctis66509.2025.11387043
“Representative examples include SWaT [4] and WADI [5], [6] in the water-treatment domain, both of which provide replayable datasets and controllable attack scenarios that serve as standard benchmarks for security research.”
IMADP: Imputation-Based Anomaly Detection in SCADA Systems via Adversarial Diffusion Process
Li, W., Yao, Y., Zhang, N., Sheng, C., Ran, Z., & Yang, W. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3670062
“Water Distribution : The WADI (W) [41] and BATADAL (B) [42] datasets, derived from the SWaT testbed, employ distinct attack strategies.”
Chandran, P., Sunil, K., Remesh, A., et al. (2026) · doi:10.1109/i5cps67958.2026.11452466
“WDS can be naturally represented as graphs where junctions, tanks, reservoirs, and control points correspond to nodes, while pipes, valves, and pumps form edges [4].”
Statistical Contrastive Learning for Spatio-Temporal Anomaly Detection
Zhen, Z., Chen, Y., & Gel, Y. R. (2026) · Data Science in Science · doi:10.1080/26941899.2026.2648108
Timed automata learning from time series
Cornanguer, L. (2023) · doi:10.70675/2921b612z4885z4ca2za9adz5337eb033d46
Zhou, X., Cheng, Z., Wang, C., Wang, S., Tao, C., Zhou, Z., Chen, X., Luo, J., & Wang, D. Q. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11487720
“Battle of the Attack Detection Algorithms (BATADAL) dataset [9] only has simulated records and cannot reflect real network traffic.”
A Survey on Self-Supervised Learning in Cybersecurity: Network Intrusion and Malware Detection
Almaraz-Rivera, J. G., Cantoral-Ceballos, J. A., & Botero, J. F. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8050121 · Cited by 1
“For training, the BATADAL dataset [144] was used, where data collected from different devices, such as water level sensors, are available.”
Poghosyan, N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6816219
Ghosh, M., Meshram, A., Karch, M., Haas, C., Zhang, X., & Singh, M. (2026) · ACM Workshop on Cyber-Physical System Security (CPSS) · doi:10.1145/3775042.3807884
“Several publicly available datasets have been developed to support research in ICS security, including SWaT [18], WADI [1], BATADAL [50], Gas Pipeline [38], and Power Grid [40] datasets.”
Edge-Centric Anomaly Detection Framework for Industrial Automation System
Rajesh, T., Muthuswamy, S., Pillai, A. S., et al. (2026) · IEEE Dallas Circuits and Systems Conference (DCAS) · doi:10.1109/dcas69364.2026.11544971
“This facilities will enable more effective data-driven decision making processes through continuous monitoring and entire automation [2].”
Large Models in Smart Water Systems
Liang, L., Gu, X., Lu, W., Cao, Y., & Yang, B. (2026) · International Conference on Artificial Intelligence and Advanced Autonomous Systems · doi:10.1145/3813723.3813731
“BATADAL(Battle of the Attack Detection Algorithms) dataset is a public dataset specifically designed for research on cybersecurity and anomaly detection in water supply systems [28].”
D-RGATDiff: Unified Spatiotemporal Diffusion for IoT Network Time Series Anomaly Detection
Zou, C., Ruan, Y., Yang, L. T., Deng, X., Liu, S., Wang, S. Y., & Hou, R. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3709053
“BATADAL [75] and WADI [76] are water distribution cyber-physical system datasets.”
cAPTure dataset: How fast can you detect APT threats?
Puccetti, T., Vivo, S. D., Zhang, D., Liguori, P., Natella, R., & Ceccarelli, A. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112570
Concept drift handling in security applications: A survey across unimodal to multimodal data
Li, C., Wang, X., & Song, L. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134490 · Cited by 1
GAEloc: Graph Autoencoder for Attack Localization in Cyber-Physical Water Distribution Systems
Ahasan, M. R., Alhendi, M. N., Atat, R., et al. (2026) · International Conference on Communications · doi:10.1109/icc59461.2026.11587100
“…for cyberattack detection in WDS. a) Shallow Models: Linear discriminant analysis and support vector machine (SVM) have shown limited effectiveness, achieving detection rates (DRs) of 40% and 46% , respectively [7], against the battle of the attack detection algorithms (BATADAL) C-Town dataset [8].”
Sankuru, R. P., & Maiti, S. (2026) · doi:10.1109/icicgr68236.2026.11600133
“A sophisticated form of False Data Injection (FDI) happens when an adversary manipulates sensor readings to trick operators or an automated control system into perceiving the system as operating normally, while covertly inducing operational instability (Taormina et al [1], Taormina et al [2]).”
Chandran, P., Jyothish, K., & Sunil, K. (2026) · International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS) · doi:10.1109/ic3sis69949.2026.11608329
“This study uses the publicly available Battle of the Attack Detection Algorithms (BATADAL) dataset [1], a well-established benchmark designed to assess cyberattack detection algorithms for WDS.”
InfluentialDC-IRS: A Dual-Constraint Intrusion Response System via Lexicographic Policy Optimization
Zhao, P., Lai, Y., Xie, B., et al. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3721250
“To further evaluate the generalizability of DC-IRS beyond the TEP benchmark, we conduct an additional evaluation on the C-Town water-distribution system [40].”
Explainable contextualized anomaly detection for heterogeneous time series
Giannoulidis, A., & Gounaris, A. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01087-y
Islam, M. B., Tarek, J. H., Paran, M. S., et al. (2026) · doi:10.1109/iccsdfai70505.2026.11648044
“4) Transparent trade-off analysis: A full ablation quantifies the aggregate vs. minority-class trade-off, supported by SHAP explainability and an architectural transferability demonstration on the BATADAL benchmark [9].”
Urs, P. G., & Mureşan, V. (2026) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp6050158
“Dataset-based analyses—represented by the SWaT [12], BATADAL [13], and HAI [56] benchmark studies—support reproducible quantitative evaluation but reflect idealised attack conditions that may not correspond to adversary behaviour against real operational infrastructure.”
InfluentialEpistemic Edge: Subjective Logic Guardrails for LLM-Driven IoT Actuation
Syed, M. M., & Silaghi, M. (2026) · IEEE International Conference on Information Reuse and Integration for Data Science (IRI) · doi:10.1109/iri69576.2026.00017
“…models (1-bit Bonsai, 4-bit GGUF, and a reasoning model) that isolates each tier’s contribution, and a real-world validation on the BATADAL [6] water-distribution attack benchmark (4,177 hours, 5 embedded attacks) testing whether the uncertainty quantification detects anomalies on real data.”
InfluentialAI-Assisted Resilience Monitoring for Industrial Control System Security
Mittal, S., Polurouthu, V. K., & Gupta, A. (2026) · IEEE Access · doi:10.1109/access.2026.3732974
“Public testbeds including SWaT, WADI, BATADAL, Tennessee Eastman, and HAI enabled reproducible cyber-physical anomaly research [5], [6], [7], [8], [9], [10], [11], [12].”
Machaka, V., Arrizabalaga, S., Elduayen-Echave, B., Paz, A. D., & Hernantes, J. (2026) · Water Research · doi:10.1016/j.watres.2026.126960
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“In addition, to evaluate the generalization capability of the proposed method, we also conduct related experiments on two publicly available datasets: the Secure water treatment (SWaT) [29] dataset and the BATADAL dataset [30].”
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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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