G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System

Shahriar, Hasan; Haque, Nur Imtiazul; Rahman, Mohammad Ashiqur; Alonso, Miguel Ángel Verdugo

doi:10.1109/compsac48688.2020.0-218

Cited by 100 publications

(42 citation statements)

References 30 publications

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“…Shahriar et al [26] proposed a G-IDS framework that included 4 segments: database module, IDS module, controller module and synthesizer module. The database module collects real intrusion detection data as well as synthesized data from the GAN/synthesizer module, each with a flag to distinguish the data sources.…”

Section: Related Workmentioning

confidence: 99%

Generative Deep Learning to Detect Cyberattacks for the IoT-23 Dataset

et al. 2022

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The rapid growth of Internet of Things (IoT) is expected to add billions of IoT devices connected to the Internet. These devices represent a vast attack surface for cyberattacks. For example, these IoT devices can be infected with botnets to enable Distributed Denial of Service (DDoS) attacks. Signaturebased intrusion detection systems are traditional countermeasures for such attacks. However, these methods rely on human experts and are time-consuming in terms of updates and may not exhaust all attack types especially zero-day attacks. Deep learning has shown some promise in intrusion detection. This paper shows that it is possible to use generative deep learning methods like Adversarial Autoencoders (AAE) and Bidirectional Generative Adversarial Networks (BiGAN) to detect intruders based on an analysis of the network data. The recently posted full IoT-23 dataset based on Somfy door lock, Philips Hue and Amazon Echo devices was used to train generative deep learning models to detect a variety of attacks like DDoS, and various botnets like Mirai, Okiruk and Torii. Over 1.8 million network flows were used to train the various models. The resulting generative models outperform traditional machine learning techniques like Random Forests. Both AAE and BiGAN-based models were able to achieve an F1-Score of 0.99. A BiGAN to detect unknown attacks was also trained to detect novel zero-day attacks with an F1-Score from 0.85 to 1.

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Section: Related Workmentioning

confidence: 99%

Generative Deep Learning to Detect Cyberattacks for the IoT-23 Dataset

et al. 2022

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“…Shahriar et al [26] proposed a GAN-assisted IDS that outperforms a standalone IDS for an unbalanced dataset or any developing domain of cyber-physical systems with limited data for model training. They tested the model on the NSL KDD'99 benchmark dataset.…”

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

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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.

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“…The experiment results obtained by the model are competitive with 100% for CICIDS2017 and 99% for UNSW-15 in terms of precision, recall, and F-score. Shahriar et al [55] also addressed the imbalanced issue in IoT intrusion detection systems. They used a generative adversarial network (GAN) as a model to solve the difficulties of imbalanced classes.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

et al. 2021

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Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from researchers and practitioners since the security of IoT networks is crucial. Both supervised and unsupervised learning methods are used for intrusion detection of IoT networks. This paper proposes an approach of three stages considering a clustering with reduction stage, an oversampling stage, and a classification by a Single Hidden Layer Feed-Forward Neural Network (SLFN) stage. The novelty of the paper resides in the technique of data reduction and data oversampling for generating useful and balanced training data and the hybrid consideration of the unsupervised and supervised methods for detecting the intrusion activities. The experiments were evaluated in terms of accuracy, precision, recall, and G-mean and divided into four steps: measuring the effect of the data reduction with clustering, the evaluation of the framework with basic classifiers, the effect of the oversampling technique, and a comparison with basic classifiers. The results show that SLFN classification technique and the choice of Support Vector Machine and Synthetic Minority Oversampling Technique (SVM-SMOTE) with a ratio of 0.9 and the k value of 3 for k-means++ clustering technique give better results than other values and other classification techniques.

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G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System

Cited by 100 publications

References 30 publications

Generative Deep Learning to Detect Cyberattacks for the IoT-23 Dataset

Generative Deep Learning to Detect Cyberattacks for the IoT-23 Dataset

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

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

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