Anomaly Detection in Industrial IoT Using Distributional Reinforcement Learning and Generative Adversarial Networks

Benaddi, Hafsa; Jouhari, Mohammed; Ibrahimi, Khalil; Othman, Jalel Ben Ben; Amhoud, El Mehdi

doi:10.3390/s22218085

Cited by 29 publications

(11 citation statements)

References 46 publications

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“…adv. trained anomaly detection for CRNs [101] Hierarchical anomaly detection for data aggregation & privacy preservation [102] Anomaly detection by learning minority data classes and data augmentation [103] Efficient and robust intrusion detection for IIoT [104] Cognitive Radio Networks…”

Section: Pla and Adversarial Trainingmentioning

confidence: 99%

“…By learning minority data classes and generating augmented data, their proposed models outperformed other anomaly detection models in various performance metrics. Likewise, Benaddi et al [104] suggested a mechanism to improve the efficiency and robustness of intrusion detection systems in Industrial IoT networks using distributed RL and GANs. Their proposed models outperformed the standard RL models in various performance metrics.…”

Section: Anomaly and Attack Detectionmentioning

confidence: 99%

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At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

Celik,

Eltawil

2024

IEEE Open J. Commun. Soc.

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As we transition from the 5G epoch, a new horizon beckons with the advent of 6G, seeking a profound fusion with novel communication paradigms and emerging technological trends, bringing once-futuristic visions to life along with added technical intricacies. Although analytical models lay the foundations and offer systematic insights, we have recently witnessed a noticeable surge in research suggesting machine learning (ML) and artificial intelligence (AI) can efficiently deal with complex problems by complementing or replacing model-based approaches. The majority of data-driven wireless research leans heavily on discriminative AI (DAI) that requires vast real-world datasets. Unlike the DAI, Generative AI (GenAI) pertains to generative models (GMs) capable of discerning the underlying data distribution, patterns, and features of the input data. This makes GenAI a crucial asset in wireless domain wherein real-world data is often scarce, incomplete, costly to acquire, and hard to model or comprehend. With these appealing attributes, GenAI can replace or supplement DAI methods in various capacities.Accordingly, this combined tutorial-survey paper commences with preliminaries of 6G and wireless intelligence by outlining candidate 6G applications and services, presenting a taxonomy of state-of-theart DAI models, exemplifying prominent DAI use cases, and elucidating the multifaceted ways through which GenAI enhances DAI. Subsequently, we present a tutorial on GMs by spotlighting seminal examples such as generative adversarial networks, variational autoencoders, flow-based GMs, diffusion-based GMs, generative transformers, large language models, autoregressive GMs, to name a few. Contrary to the prevailing belief that GenAI is a nascent trend, our exhaustive review of approximately 120 technical papers demonstrates the scope of research across core wireless research areas, including 1) physical layer design; 2) network optimization, organization, and management; 3) network traffic analytics; 4) crosslayer network security; and 5) localization & positioning. Furthermore, we outline the central role of GMs in pioneering areas of 6G network research, including semantic communications, integrated sensing and communications, THz communications, extremely large antenna arrays, near-field communications, digital twins, AI-generated content services, mobile edge computing and edge AI, adversarial ML, and trustworthy AI. Lastly, we shed light on the multifarious challenges ahead, suggesting potential strategies and promising remedies. Given its depth and breadth, we are confident that this tutorial-cum-survey will serve as a pivotal reference for researchers and professionals delving into this dynamic and promising domain.

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Section: Pla and Adversarial Trainingmentioning

confidence: 99%

Section: Anomaly and Attack Detectionmentioning

confidence: 99%

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

Celik,

Eltawil

2024

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…The researchers of [27] used dynamic features to produce adversarial attacks against black-box malware classifiers and compared the results of using RL and GAN techniques independently. In the field of industrial IoT, Benaddi et al [43] focused on anomaly detection in Intrusion Detection Systems (IDS) using Distributional Reinforcement Learning and GAN. Phan et al [28] proposed an evasion method for black-box malware detectors to evaluate the effectiveness of RL and its combination with GAN, however the focus of their research work is only the comparison between both approaches and did not consider recent state-of-the-art evasion methods in their result analysis.…”

Section: B Malware Evasion Techniquesmentioning

confidence: 99%

A Deep Reinforcement Learning Framework to Evade Black-Box Machine Learning Based IoT Malware Detectors Using GAN-Generated Influential Features

Arif,

Aslam,

Al-Otaibi

et al. 2023

IEEE Access

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In the internet of things (IoT) networks, machine learning (ML) is significantly used for malware and adversary detection. Recently, research has shown that adversarial attacks have put ML-based models at risk. This problem is exacerbated in an IoT environment because of the absence of adequate security measures. Consequently, it is crucial to evaluate the strength of such malware detectors using powerful adversarial samples. The existing adversarial sample generation strategies either rely on high-level image features or an unfiltered feature set, making it challenging to determine which feature modifications are crucial in evading malware detection systems, without compromising the malware functionality. This encourages us to propose an evasion framework named IF-MalEvade, based on Generative Adversarial Network (GAN) and Deep Reinforcement Learning (DRL) that effectively generates fully-working, malware samples with several effective perturbations such as header section manipulation and benign bytes insertion. The DRL framework selects a few suitable action sequences to change malicious samples, thus allowing our malware samples to bypass various black-box ML based malware detectors and the detection search engines of VirusTotal, while maintaining the executability and malicious behavior of the original malware samples. The neural networks of GAN take in the unfiltered feature set of malware dataset and using minimax objective function yields a set of useful features that are subsequently used by the DRL agent to make effective changes. Experimental results illustrated that by utilizing the influential features in sequence of transformations, the adversarial samples generated by our model outperformed the state-of-the-art evasion models with an impressive evasion rate. Additionally, the detection rate of well-known machine learning models was also brought down to upto 97%. Furthermore, when the machine learning models were retrained using adversarial samples, a 35% increase in detection accuracy was observed.

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“…The IoT is a network of sensors and computational devices that work together to solve problems and provide innovative features [1]. The IoT is a collection of physical objects-"things"-embedded with sensors, apps, and other technology for connecting and transmitting data with other devices and systems through the internet [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Improving Reliability for Detecting Anomalies in the MQTT Network by Applying Correlation Analysis for Feature Selection Using Machine Learning Techniques

et al. 2023

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Anomaly detection (AD) has captured a significant amount of focus from the research field in recent years, with the rise of the Internet of Things (IoT) application. Anomalies, often known as outliers, are defined as the discovery of anomalous occurrences or observations that differ considerably from the mainstream of the data. The IoT which is described as a network of Internet-based digital sensors that continuously generate massive volumes of data and use to communicate with one another theMessage Queuing Telemetry Transport (MQTT) protocol. Brute-force, Denial-of-Service (DoS), Malformed, Flood, and Slowite attacks are the most common in theMQTT network. One of the significant factors in IoT AD is the time consumed to predict an attack and take preemptive measures. For instance, if an attack is detected late, the loss of attack is irreversible. This paper investigates the time to detect an attack using machine learning approaches and proposes a novel approach that applies correlation analysis to reduce the training and testing time of these algorithms. The new approach has been evaluated on Random Forest, Decision Tree, Naïve Bayes, Multi-Layer Perceptron, Artificial Neural Network, Logistic Regression, and Gradient Boost. The findings indicate that the correlation analysis is significantly beneficial in the process of feature engineering, primarily to determine the most relevant features in the MQTT dataset. This is, to the best of our knowledge, the first study on MQTTset that reduces the prediction time for DoS 0.92 (95% CI −0.378, 2.22) reduced to 0.77 (95% CI −0.414, 1.97) and for Malformed 2.92 (95% CI −2.6, 8.44) reduced to 0.49 (95% CI −0.273, 1.25).

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Anomaly Detection in Industrial IoT Using Distributional Reinforcement Learning and Generative Adversarial Networks

Cited by 29 publications

References 46 publications

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

A Deep Reinforcement Learning Framework to Evade Black-Box Machine Learning Based IoT Malware Detectors Using GAN-Generated Influential Features

Improving Reliability for Detecting Anomalies in the MQTT Network by Applying Correlation Analysis for Feature Selection Using Machine Learning Techniques

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