SRI: A Simple Rule Induction Method for improving resiliency of DNN based IDS against adversarial and zero-day attacks
Abstract
Adversarial machine learning (ML) has demonstrated vulnerabilities of neural network methods against well-crafted perturbations when added to perfectly acceptable samples. These vulnerabilities get exacerbated when neural network methods are deployed as anomaly or intrusion detectors in cyber-physical systems (CPS). Due to this, mounting of zero-day attacks became much easier against neural network-based intrusion detection systems (IDS) as adversarial samples are similar to zero-day attack vectors. To alleviate some of these problems of neural network (NN) based IDSs, we propose a new rule induction method, known as simple rule induction (SRI), for classification. SRI is capable of extracting control logic in the form of threshold-based rules from CPS's historical operational data. Later, control logic was utilized to generate adversarial samples that could evade detection by a baseline neural network-based IDS. To improve the NN-based detector's resiliency against such adversarial attacks, we retrain the detectors with previously generated adversarial samples. In a specific case, retraining has improved accuracy against adversarial samples from 9% to 99%, as demonstrated by our experiments. Moreover, it was found in our experiments that adversarial training is able to improve the F1_Score of zero day attack detection method from 0.06 to 0.53.
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Because operational-consistency-based detectors focus on physical and logical feasibility rather than specific attack signatures, they can flag previously unseen attacks that violate process interlocks, causal relationships, or power-balance constraints.
“Because they focus on physical and logical feasibility rather than on specific attack signatures, they can flag previously unseen attacks that nevertheless violate process interlocks, causal relationships, or power-balance constraints [113,114,117,118].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [117]
Hybrid operational-consistency frameworks combine rule-based consistency checks with supervised learners (e.g., decision trees, XGBoost, CNN-LSTM, DNNs), using operational rules either as a first-stage filter or as additional features to improve the robustness of data-driven classifiers.
“On the other side, hybrid frameworks combine these rule-based consistency checks with supervised learners such as decision trees, XGBoost, CNN-LSTM, and DNNs, using operational rules either as a first-stage filter or as additional features to improve the robustness of data-driven classifiers [112-114,117,118].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [117]
By checking whether actuator commands, sensor responses, and event sequences are physically feasible and logically consistent, operational-consistency-based methods can detect cyber-physical attacks and stealthy manipulations that only slightly perturb statistical patterns but induce clear inconsistencies with process and control laws (e.g., false data injection, command and control tampering, re
“By checking whether actuator commands, sensor responses, and event sequences are physically feasible and logically consistent. they can detect cyber-physical attacks and stealthy manipulations that only slightly perturb statistical patterns but induce clear inconsistencies with process and control laws-such as false data injection, command and control tampering, replay, ramp-style stealth attacks,”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [117]
The dataset distribution for operational-consistency-oriented methods shows evaluation across a relatively diverse set of CPS domains, including water-treatment testbeds (SWaT, WADI), vehicle OBD-II datasets, and power-system simulations.
“Finally, the dataset distribution shows that operational-consistency-oriented methods are evaluated across a relatively diverse set of CPS domains, including water-treatment testbeds (SWaT, WADI), vehicle OBD-II datasets, and power-system simulations [111-118].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [117]
Rule-centric operational-consistency approaches (e.g., Bayesian-network/FSM models, PLC-command validation tools, process-monitoring modules with explicit sensor/actuator rules) leverage hand-crafted or automatically extracted constraints to reject logically impossible operations and abnormal event sequences.
“On one side, Bayesian-network/FSM models, PLC-command validation tools, and process-monitoring modules with explicit sensor/actuator rules leverage hand-crafted or automatically extracted constraints to reject logically impossible operations and abnormal event sequences [111-113,117].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [117]
Operational-consistency-based methods explicitly encode process constraints, control logic, and admissible state transitions, and treat violations of such rules as primary anomaly cues, unlike purely data-driven prediction-residual or reconstruction-error approaches.
“First, unlike purely data-driven prediction-residual or reconstruction-error approaches, these methods explicitly encode process constraints, control logic, and admissible state transitions, and treat violations of such rules as primary anomaly cues [111-118].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [117]
Several studies emphasize the importance of automatic rule extraction, adaptive updating of operational profiles, and careful coordination between rule-based and ML-based components.
“Several studies therefore emphasize the importance of automatic rule extraction, adaptive updating of operational profiles, and careful coordination between rule-based and ML-based components [113,114,117].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [117]
Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works
Practical Black-Box Attacks against Machine Learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2017) · Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security · doi:10.1145/3052973.3053009 · Cited by 3,495 · see citing works
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Fayyad, U. M., & Irani, K. B. (1993) · International Joint Conference on Artificial Intelligence · Cited by 2,870 · see citing works
2011 International Joint Conference on Neural Networks
Boubacar, H. A., Lecoeuche, S., & Maouche, S. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2063350 · Cited by 1,481 · see citing works
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Deng, A., & Hooi, B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16523 · Cited by 1,413 · see citing works
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., & Ng, S.-K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30490-4_56 · Cited by 1,323 · see citing works
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder
Park, D., Hoshi, Y., & Kemp, C. C. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2801475 · Cited by 1,131 · see citing works
Attacks against process control systems: risk assessment, detection, and response
Cárdenas, A. A., Amin, S., Lin, Z.-S., Huang, Y.-L., Huang, C.-Y., & Sastry, S. (2011) · Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security · doi:10.1145/1966913.1966959 · Cited by 699 · see citing works
DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems
Li, B., Wu, Y., Song, J., Lu, R., Li, T., & Zhao, L. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3023430 · Cited by 623 · see citing works
Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT
Chen, Z., Chen, D., Zhang, X., Yuan, Z., & Cheng, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3100509 · Cited by 613 · see citing works
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving
Cao, Y., Xiao, C., Cyr, B., Zhou, Y., Park, W., Rampazzi, S., Chen, Q. A., Fu, K. E., & Mao, Z. M. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3339815 · Cited by 577 · see citing works
Zhang, G., Yan, C., Ji, X., Zhang, T., Zhang, T., & Xu, W. (2017) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3133956.3134052 · Cited by 570 · see citing works
Stuxnet worm impact on industrial cyber-physical system security
Karnouskos, S. (2011) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2011.6120048 · Cited by 542 · see citing works
Integrity Data Attacks in Power Market Operations
Xie, L., Mo, Y., & Sinopoli, B. (2011) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2011.2161892 · Cited by 491 · see citing works
Anomaly Detection in Cyber Physical Systems Using Recurrent Neural Networks
Goh, J., Adepu, S., Tan, M., & Lee, Z. S. (2017) · IEEE International Symposium on High Assurance Systems Engineering (HASE) · doi:10.1109/hase.2017.36 · Cited by 447 · see citing works
An implementation of logical analysis of data
Boros, E., Hammer, P. L., Ibaraki, T., Kogan, A., Mayoraz, E., & Muchnik, I. B. (2000) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.842268 · Cited by 406 · see citing works
Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks
Kravchik, M., & Shabtai, A. (2018) · Workshop on Cyber-Physical Systems Security and PrivaCy (CPS-SPC) · doi:10.1145/3264888.3264896 · Cited by 393 · see citing works
False Data Injection Attacks in Electricity Markets
Xie, L., Mo, Y., & Sinopoli, B. (2010) · IEEE International Conference on Smart Grid Communications (SmartGridComm) · doi:10.1109/smartgrid.2010.5622048 · Cited by 391 · see citing works
Zhang, X., Han, Q., Ge, X., & Ding, L. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2956137 · Cited by 373 · see citing works
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
Inoue, J., Yamagata, Y., Chen, Y., POSKITT, C. M., & Sun, J. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.149 · Cited by 372 · see citing works
Deep Learning-based Anomaly Detection in Cyber-physical Systems
Luo, Y., Xiao, Y., Cheng, L., Peng, G., & Yao, D. D. (2021) · ACM Computing Surveys · doi:10.1145/3453155 · Cited by 332 · see citing works
False data injection attacks with incomplete information against smart power grids
Rahman, M. A., & Mohsenian‐Rad, H. (2012) · Globecom · doi:10.1109/glocom.2012.6503599 · Cited by 323 · see citing works
Kim, J.-Y., Bu, S.-J., & Cho, S.-B. (2018) · Information Sciences · doi:10.1016/j.ins.2018.04.092 · Cited by 256 · see citing works
Resilient Model Predictive Control of Cyber–Physical Systems Under DoS Attacks
Sun, Q., Zhang, K., & Shi, Y. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2963294 · Cited by 251 · see citing works
Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids
Hao, J., Piechocki, R. J., Kaleshi, D., Chin, W. H., & Fan, Z. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2475695 · Cited by 233 · see citing works
A Survey on Cyber–Physical Systems Security
Yu, Z., Gao, H., Cong, X., Wu, N., & Song, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3289625 · Cited by 219 · see citing works
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network
Shen, L., Li, Z., & Kwok, J. T.-Y. (2020) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 218 · see citing works
Lin, Q., Adepu, S., Verwer, S., & Mathur, A. P. (2018) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3196494.3196546 · Cited by 157 · see citing works
False Data Injection Attacks Against State Estimation Without Knowledge of Estimators
Lu, A., & Yang, G. (2022) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2022.3161259 · Cited by 126 · see citing works
Anomaly detection in Industrial Control Systems using Logical Analysis of Data
Das, T. K., Adepu, S., & Zhou, J. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101935 · Cited by 124 · see citing works
Self-citationAdversarial Attacks and Defenses on Cyber–Physical Systems: A Survey
Li, J., Liu, Y., Chen, T., Xiao, Z., Li, Z., & Wang, J. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2975654 · Cited by 87 · see citing works
Detection of SQL Injection Attacks: A Machine Learning Approach
Hasan, M., Balbahaith, Z., & Tarique, M. (2019) · International Conference on Electrical and Computing Technologies and Applications (ICECTA) · doi:10.1109/icecta48151.2019.8959617 · Cited by 73 · see citing works
Car Hacking and Defense Competition on In-Vehicle Network
Kang, H., Kwak, B. I., Lee, Y. H., Lee, H., Lee, H., & Kim, H. K. (2021) · International Workshop on Automotive and Autonomous Vehicle Security (AutoSec) · doi:10.14722/autosec.2021.23035 · Cited by 71 · see citing works
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems
Li, J., Yang, Y., Sun, J., Tomsovic, K. L., & Qi, H. (2021) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3433210.3437513 · Cited by 67 · see citing works
Security Against Network Attacks in Supervisory Control Systems
Lima, P. M., Alves, M. V. S., Carvalho, L. K., & Moreira, M. V. D. B. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.2161 · Cited by 64 · see citing works
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios
Choi, J. I., & Tian, Q. (2022) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv51971.2022.9827222 · Cited by 57 · see citing works
Defending collaborative false data injection attacks in wireless sensor networks
Wang, J., Liu, Z., Zhang, S., & Zhang, X. (2013) · Information Sciences · doi:10.1016/j.ins.2013.08.019 · Cited by 51 · see citing works
Panthi, M., & Das, T. K. (2022) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2022.100567 · Cited by 47 · see citing works
Self-citationActive eavesdropping via spoofing relay attack
Zeng, Y., & Zhang, R. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472059 · Cited by 42 · see citing works
Design and Evaluation of Feature Distributed Malware Attacks against the Internet of Things (IoT)
Min, B., & Varadharajan, V. (2015) · International Conference on Engineering of Complex Computer Systems · doi:10.1109/iceccs.2015.19 · Cited by 29 · see citing works
Kumar, A., & Das, T. K. (2023) · Vehicular Communications · doi:10.1016/j.vehcom.2023.100652 · Cited by 24 · see citing works
Self-citationYadegar, M., Meskin, N., & Haddad, W. M. (2019) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.3001 · Cited by 21 · see citing works
Design of a FDIA Resilient Protection Scheme for Power Networks by Securing Minimal Sensor Set
Das, T. K., Ghosh, S., Koley, E., & Zhou, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29729-9_9 · Cited by 4 · see citing works
Self-citation
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Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15 · see citing works
ORADS: One Class Rule-Based Anomaly Detection System in Autonomous Vehicles
Kumar, A., & Das, T. K. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3523345 · Cited by 5 · see citing works
Self-citationAdaptive Defense Mechanisms Against Zero-Day Attacks in Wireless Sensor Networks
Chaturvedi, A., Kukreti, S., Bhavani, A. D., Nizampatnam, V. N. R. K., Kaur, D., & Natrayan, L. (2024) · International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC) · doi:10.1109/icdscnc62492.2024.10939775 · Cited by 1 · see citing works
Pandey, R. K., & Das, T. K. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02775-8
Self-citationHierarchical Deep Learning with Nature-Inspired Optimization for Robust Network Intrusion Detection
Li, H. (2026) · Information Technology And Control · doi:10.5755/j01.itc.55.1.42179
- Authors
- Kumar, A., Das, T. K., & Pandey, R. K.
- Affiliations
- National Institute of Technology Raipur (IN)
- Venue
- Proceedings of the 10th ACM Cyber-Physical System Security Workshop
- Publisher
- ACM
- Published
- 2024-07
- Pages
- 24-35
- OpenAlex
- W4398133954
- Cited by
- 4
- References
- 43
- Citations by year
- 2025: 3 · 2024: 1
- FWCI
- 1.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Workshop on Cyber-Physical System Security (CPSS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- adversarial learning, zero-day attacks, intrusion detection systems, adversarial training, adversarial examples, cyber-physical systems, neural network robustness, anomaly detection
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