Evaluation of GAN Applicability for Intrusion Detection in Self-Organizing Networks of Cyber Physical Systems

Belenko, Viacheslav; Chernenko, Valery; Kalinin, Maxim O.; Krundyshev, Vasiliy

doi:10.1109/rusautocon.2018.8501783

Cited by 46 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering that large volumes of multidimensional data are generated in 6G IIoT, the authors in [14] designed an autoregressive exogenous model (ARX) for eliminating the noise in data for anomaly detection, and a multidimensional data relationship diagram is creatively used to characterize the spatiotemporal correlations among heterogeneous data. The authors in [73] applied CGANs to search for security anomalies, noting that the discriminator needs to be trained for more steps than the generator to ensure that their loss curves converge.…”

Section: A Anomaly Detectionmentioning

confidence: 99%

Deep Generative Models in the Industrial Internet of Things: A Survey

Bermúdez-Edo

et al. 2022

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

Advances in communication technologies and artificial intelligence are accelerating the paradigm of industrial Internet of Things (IIoT). With IIoT enabling continuous integration of sensors and controllers with the network, intelligent analysis of the generated Big Data is a critical requirement. Although IIoT is considered a subset of IoT, it has its own peculiarities in terms of higher levels of safety, security, and low-latency communication in an environment of critical real-time operations. Under these circumstances, discriminative deep learning (DL) algorithms are unsuitable due to their need for large amounts of labeled and balanced training data, uncertainty of inputs, etc. To overcome these issues, researchers have started using deep generative models (DGMs), which combine the flexibility of DL with the inference power of probabilistic modeling. In this article, we review the state of the art of DGMs and their applicability to IIoT, classifying the reviewed works into the IIoT application areas of anomaly detection, trust-boundary protection, network traffic prediction, and platform monitoring. Following an analysis of existing IIoT DGM implementations, we identify challenges (i.e., weak discriminative capability, insufficient interpretability, lack of generalization ability, generated data vulnerability, privacy concern, and data complexity) that need to be investigated in order to accelerate the adoption of DGMs in IIoT and also propose some potential research directions.

show abstract

Section: A Anomaly Detectionmentioning

confidence: 99%

Deep Generative Models in the Industrial Internet of Things: A Survey

Bermúdez-Edo

et al. 2022

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

show abstract

“…Belenko et al [22] examined the GAN's applicability for intrusion detection and found it to be more promising than a traditional ANN for dealing with real-world problems. They came to the conclusion that GAN-based networks may be used to look for security anomalies and cyber hazards, as well as to generate more anomalies to improve the quality of the flagged data samples.…”

Section: Related Workmentioning

confidence: 99%

An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices

Idrissi¹,

Azizi²,

Moussaoui³

2022

IJEECS

View full text Add to dashboard Cite

Machine learning (ML) and deep learning (DL) have achieved amazing progress in diverse disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of algorithms for analyzing and clustering unlabeled data; it allows identifying hidden patterns or performing data clustering over provided data without the need for human involvement. There is no prior knowledge of actual abnormalities when using UL methods in anomaly detection (AD); hence, a DL-intrusion detection system (IDS)- based on AD depends intensely on their assumption about the distribution of anomalies. In this paper, we propose a novel unsupervised AD Host-IDS for internet of things (IoT) based on adversarial training architecture using the generative adversarial network (GAN). Our proposed IDS, called “EdgeIDS”, targets mostly IoT devices because of their limited functionality; IoT devices send and receive only specific data, not like traditional devices, such as servers or computers that exchange a wide range of data. We benchmarked our proposed “EdgeIDS” on the message queuing telemetry transport (MQTTset) dataset with five attack types, and our obtained results are promising, up to 0.99 in the ROC-AUC metric, and to just 0.035 in the ROC-EER metric. Our proposed technique could be a solution for detecting cyber abnormalities in the IoT.

show abstract

“…The authors, after testing their approach on the SWaT and Water Distribution (WADI) datasets concluded that their method is effective in detective anomalies caused by cyber attacks in CPS. The use of GAN was also proposed for identifying security anomalies and cyber threats in the self-organizing networks of CPS [105]. The authors as part of their future works intend to use the proposed model to secure a self-learning VANET/MANET.…”

Section: B Generative Adversarial Network (Gan) For Resilient Cpsmentioning

confidence: 99%

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Olowononi

Rawat

Liu

2021

IEEE Commun. Surv. Tutorials

132

View full text Add to dashboard Cite

Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

show abstract

Evaluation of GAN Applicability for Intrusion Detection in Self-Organizing Networks of Cyber Physical Systems

Cited by 46 publications

References 7 publications

Deep Generative Models in the Industrial Internet of Things: A Survey

Deep Generative Models in the Industrial Internet of Things: A Survey

An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Contact Info

Product

Resources

About