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Enhancing Cyber-Physical System Security with CGAN in Fog Environment
Abstract
The capacity to identify abnormalities, such as fraudulent control, espionage, and other risks inside an IoT-based network, is becoming more important as demand for IoT-based services rises. The battery life, memory capacity, and computing capabilities limitations of most IoT-based networks preclude the usage of conventional Intrusion Detection Systems (IDSs). Several IDSs have been presented in the literature as potential solutions to these problems. The majority of IDSs, however, have issues with a substantial false alarm rate and a lack of precision in the anomaly detection process. Rather of depending only on a single central cloud architecture, we describe an anomaly-based intrusion detection system that disperses security functionalities over dispersed fog nodes. Generative adversarial networks (GANs), which model the framework implicitly, provide a promising unsupervised method for detecting cyberattacks. Because they must be terminated when a network is compromised, attacks with CPSs have stringent latency requirements. The purpose of this research was to develop FID-GAN, an unsupervised IDS that operates in the fog and is tailored specifically for CPSs. A fog architecture should take use of the IDS since it brings computation closer to the end nodes, making it possible to achieve low-latency requirements. The suggested architecture uses a reconstruction loss determined by projecting a sample of recovered information into the latent space to increase detection rates. Due to the time needed to calculate the reconstruction loss, such study is unfeasible for latency-critical applications. We train an Encoder to speed up the calculation of the reconstruction loss and thereby solve this issue. The suggested solution outperforms the baseline method in all three datasets, and the experiments demonstrate that it is a minimum of 5.5 times quicker.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The method models multivariate time-series collected from sensors and actuators using LSTM-RNN-based generator-discriminator pairs, learning the temporal dependency of normal traffic. A BiGAN-style encoder maps observed sequences directly into the latent space, and anomalies are detected by computing reconstruction loss between the input and the generator-reconstructed sequence.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [38]
In the citing paper
“By explicitly modeling packet or flow sequences as time series, the proposed models can detect not only high-rate attacks but also low-rate or long-duration anomalies that gradually distort periodic traffic patterns in CPS/IIoT environments [38,40,44].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [38]
In the citing paper
“Using the CIC-IDS2017 and UNSW-NB15 datasets, Pathak et al. [38] proposed a CGANbased unsupervised anomaly detection model deployable within Fog architectures to satisfy latency constraints of CPS environments.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [38]
In the citing paper
“First, this data type is particularly effective for attacks that explicitly disturb temporal patterns of communication, such as DoS/DDoS, flooding, scanning/probing, and botnet-driven bursts, where anomalies manifest as abrupt changes in flow-arrival rates, inter-arrival times, and session durations [35,38,40,44,45].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [38]
In the citing paper
“Unsupervised and generative approaches such as FID-GAN and autoencoder-based frameworks target zero-day or previously unseen attacks by learning normal temporal patterns and flagging deviations via reconstruction- or residual-based scores [38,42,44].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [38]
In the citing paper
“While statistical and entropy-based approaches treat each flow or aggregate independently, time-series dependency-based methods incorporate sequential context-such as burstiness, periodicity, and long-range dependencies-via LSTM, BLSTM, GRU, SARIMA, or hybrid architectures [33,38-40,44,45]. This allows them to capture more subtle deviations in normal traffic dynamics, at the cost of higher model c”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [38]
In the citing paper
“Third, in terms of model behavior and failure modes, the surveyed work shows that deep sequence models (LSTM, BLSTM-GRU, CNN + Bi-GRU) and generative architectures (FID-GAN, VAE-based digital twins) are able to exploit complex temporal patterns and achieve strong detection or classification performance on benchmark datasets [35,38-40,42,44,45].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [38]
In the citing paper
“Fourth, the dataset distribution again reveals a concentration on a limited set of benchmarks. CIC-IDS2017, UNSW-NB15, IoT-23, LITNET-2020, NetML-2020, CIC-DDoS2019, and N-BaIoT are widely used to evaluate time-series dependency-based techniques, especially for botnet, DDoS, and generic network intrusions [35,38,40,43]. Only a subset of studies leverage ICS/CPS-specific traces such as Alstom TCMS”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [38]
Citations
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References 13 · Cited by 2
Research works printed but not linked: 9
- dimf, 2023, An efficient power theft detection using modified deep artificial neural network (MDANN), International Journal of Intelligent Systems and Applications in Engineering, 11, 1
- 2021, A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems
- diwan, 2023, Implementation patterns of natural language processing using pre-trained deep learning models, International Journal of Intelligent Systems and Applications in Engineering, 11, 33
- despa, 2023, Online fuzzy logic prediction of electrical load based on real-time measurements during the covid-19 pandemic, International Journal of Intelligent Systems and Applications in Engineering, 11, 1
- deeptha, 2023, Secured data transmission for smart grid system using blockchain technology with a trusted gaming token honeypot algorithm, International Journal of Intelligent Systems and Applications in Engineering, 11, 527
- babu, 2023, Heart disease prognosis and quick access to medical data record using data lake with deep learning approaches, International Journal of Intelligent Systems and Applications in Engineering, 11, 292
- devi, 2023, DCNMAF: Dilated convolution neural network model with mixed activation functions for image denoising, International Journal of Intelligent Systems and Applications in Engineering, 11, 552
- dash, 2023, Are ChatGPT and Deepfake Algorithms Endangering the Cybersecurity Industry? A Review, International Journal of Engineering and Applied Sciences, 10
- shahriar, 0, GIDS: Generative Adversarial Networks Assisted Intrusion Detection System, 2020 IEEE 44th Annual Computers Software and Applications Conference (COMPSAC), 376
Metadata
- Authors
- Pathak, P., Singh, D., Saxena, A., Kumar, K., Singh Dari, S., & Dhabliya, D.
- Affiliations
- IIMT University (IN)
- School of Engineering and Computer Dev Bhoomi Uttarakhand University,Uttarakhand,India
- Invertis University (IN)
- KR Mangalam University (IN)
- Symbiosis Law School Symbiosis International (Deemed University),Pune,India
- VIIT,Department of IT,Pune,India
- Venue
- 2023 International Conference on Data Science and Network Security (ICDSNS)
- Publisher
- IEEE
- Published
- 2023-07-28
- Pages
- 01-08
- OpenAlex
- W4386920371
- Cited by
- 2
- References
- 13
- Citations by year
- 2026: 1 · 2025: 1
- FWCI
- 0.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Data Science and Network Security (ICDSNS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- cyber-physical security, fog computing, generative adversarial networks, intrusion detection, anomaly detection, unsupervised learning, Internet of Things security, low-latency detection, false alarm reduction
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- CoChat. (2026). Citation check: Enhancing Cyber-Physical System Security with CGAN in Fog Environment. https://app.cochat.ai/papers/10.1109/icdsns58469.2023.10245323
- The paper
- Pathak, P., Singh, D., Saxena, A., Kumar, K., Singh Dari, S., & Dhabliya, D. (2023). Enhancing Cyber-Physical System Security with CGAN in Fog Environment. 2023 International Conference on Data Science and Network Security (ICDSNS). https://doi.org/10.1109/icdsns58469.2023.10245323
