TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems

Debicha, Islam; Bauwens, Richard; Debatty, Thibault; Dricot, Jean‐Michel; Kenaza, Tayeb; Mees, Wim

doi:10.1016/j.future.2022.08.011

Cited by 32 publications

(13 citation statements)

References 34 publications

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“…Malicious parties have sought to steal, alter, or erase this information for a long time. Hackers and other hostile actors have developed, exploited, and enhanced various cyberattacks to accomplish these objectives [1].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Fatani

Dahou

Elaziz

et al. 2023

Sensors

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Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.

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Section: Introductionmentioning

confidence: 99%

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Fatani

Dahou

Elaziz

et al. 2023

Sensors

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“…Consequently, this implies that adversarial training could not be as effective against zero-day attacks, which are one of the key issues facing the intrusion detection sector. Moreover, the IDS may perform worse in its original classification job as a result of being retrained with adversarial instances [38].…”

Section: Related Workmentioning

confidence: 99%

“…Adversarial attacks can be performed in either white-box or black-box settings. Many white-box adversarial attacks, originally developed for computer vision applications [14,15,16], have been applied directly to network traffic without addressing domain constraints properly [17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Adv-Bot: Realistic adversarial botnet attacks against network intrusion detection systems

Debicha

Cochez

Kenaza

et al. 2023

Computers & Security

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“…A high rate of intrusion detection was attained with an accuracy of 97.24%. Debichaa et al 31 had developed an efficient transfer learning-based intrusion detection used with various adversarial detection strategies.…”

Section: Introductionmentioning

confidence: 99%

Hybrid one‐dimensional residual autoencoder and ensemble of gradient boosting for cloud IDS

Chauhan,

Dwivedi

2024

Concurrency and Computation

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SummaryThe distributed and decentralized architecture of cloud computing is important for a number of industries, including business, government, entertainment, education, and information technology. It facilitates a wide aspect of information technology, where the computing model is vulnerable to attacks or intrusion. For detecting malicious activities, a novel intrusion detection system (IDS) is required to be developed. In this paper, an enhanced synthetic minority oversampling technique (SMOTE) with a hybrid one‐dimensional residual auto encoder and the Ensemble of Gradient Boosting (1D‐RAE‐EGB) models are proposed for cloud intrusion detection. The proposed IDS resolves the class imbalance problem through enhanced SMOTE. In addition, noise reduction is accomplished with ID‐RAE, which minimizes the data dimension. Finally, the soft voting‐based ensemble classification model is used for attack detection. The ensemble gradient boosting model comprises categorical boosting (CatBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The ensemble model is fine‐tuned by reducing the number of parameters under fitting conditions since it is not compulsory to re‐adjust the weight values in a backpropagation process. The proposed IDS is implemented in Python using the NSL‐KDD dataset. The accuracy, precision, recall, f1‐score, false positive rate (FPR), false negative rate (FNR), specificity, and Kappa measure obtained for the proposed IDS are 99.98%, 99.3%, 98.5%, 99.95%, 0.723, 0.46, 99.98%, and 99.97%, respectively.

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TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems

Cited by 32 publications

References 34 publications

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Adv-Bot: Realistic adversarial botnet attacks against network intrusion detection systems

Hybrid one‐dimensional residual autoencoder and ensemble of gradient boosting for cloud IDS

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