Adversarial machine learning in Network Intrusion Detection Systems

Alhajjar, Elie; Maxwell, Paul; Bastian, Nathaniel D.

doi:10.1016/j.eswa.2021.115782

Cited by 116 publications

(28 citation statements)

References 18 publications

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“…Over the years, computer networks have grown swiftly, contributing greatly to social and economic progress. However, compared to other sectors, network security applications of ML confront a significant concern regarding active adversarial attacks [21,22]. This happens due to the adversarial nature of ML applications in network security.…”

Section: Motivationmentioning

confidence: 99%

Mitigation of Black-Box Attacks on Intrusion Detection Systems-Based ML

et al. 2022

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Intrusion detection systems (IDS) are a very vital part of network security, as they can be used to protect the network from illegal intrusions and communications. To detect malicious network traffic, several IDS based on machine learning (ML) methods have been developed in the literature. Machine learning models, on the other hand, have recently been proved to be effective, since they are vulnerable to adversarial perturbations, which allows the opponent to crash the system while performing network queries. This motivated us to present a defensive model that uses adversarial training based on generative adversarial networks (GANs) as a defense strategy to offer better protection for the system against adversarial perturbations. The experiment was carried out using random forest as a classifier. In addition, both principal component analysis (PCA) and recursive features elimination (Rfe) techniques were leveraged as a feature selection to diminish the dimensionality of the dataset, and this led to enhancing the performance of the model significantly. The proposal was tested on a realistic and recent public network dataset: CSE-CICIDS2018. The simulation results showed that GAN-based adversarial training enhanced the resilience of the IDS model and mitigated the severity of the black-box attack.

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Section: Motivationmentioning

confidence: 99%

Mitigation of Black-Box Attacks on Intrusion Detection Systems-Based ML

et al. 2022

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“…Alhajjar et al 85 explored the AL in network intrusion detection system. They analyze the behavior of the adversarial assaults in NIDS, which adds methods to create adversarial samples which can avoid the various ML models.…”

Section: Al Techniques For Security and Privacy Preservationmentioning

confidence: 99%

Adversarial learning techniques for security and privacy preservation: A comprehensive review

Hathaliya

Tanwar

Sharma

2022

Security and Privacy

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In recent years, the use of smart devices has increased exponentially, resulting in massive amounts of data. To handle this data, effective data storage and management has required. Cloud computing (CC) is a promising solution to deal with this huge amount of data. Electronic devices are collecting real‐time data from sensors and applications through a wireless communication channel in the digital era. In some cases, CC cannot protect against various malicious attacks in the wireless communication channel. To address this issue, we have used machine learning (ML) and deep learning (DL) techniques for attack detection in a wireless channel on an early basis. It trains a model to predict malicious activities of attackers, which aids in the security of CC's sensitive data. We employed adversarial learning techniques (AL) to add fake data into the model to ensure that the trained model was correct. The trained model can distinguish between the fake and real data from the training samples and improve the training samples' performance. AL provides different defense mechanisms to preserve the privacy of ML‐ and DL‐based model but does not ensure the system's robustness. To improve the system's robustness, we have used federated learning with blockchain technology to make a system more robust, reliable, accurate, and transparent. This integration aids in providing high‐graded security against adversarial attacks. This paper presents a comprehensive review to highlight the recent improvements in AL techniques. Moreover, we explored the various AL applications in security and privacy preservation. Finally, open research issues and future directions are discussed to show future research avenues.

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“…They calculated the impact of various adversarial samples on the ML-NIDS. Alhajjar et al [27] proposed a method for generating adversarial samples using the heuristic algorithm and tested them on two public datasets NSL-KDD and UNSW-NB15. The adversarial samples generated by PSO and genetic algorithm (GA) have a higher bypass rate than optimization algorithms.…”

Section: Adversarial Sample Attacksmentioning

confidence: 99%

VulnerGAN: a backdoor attack through vulnerability amplification against machine learning-based network intrusion detection systems

Liu

Zhang

et al. 2022

Sci. China Inf. Sci.

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Machine learning-based network intrusion detection systems (ML-NIDS) are extensively used for network security against unknown attacks. Existing intrusion detection systems can effectively defend traditional network attacks, however, they face AI based threats. The current known AI attacks cannot balance the escape rate and attack effectiveness. In addition, the time cost of existing AI attacks is very high. In this paper, we propose a backdoor attack called VulnerGAN, which features high concealment, high aggressiveness, and high timeliness. The backdoor can make the specific attack traffic bypass the detection of ML-NIDS without affecting the performance of ML-NIDS in identifying other attack traffic. VulnerGAN uses generative adversarial networks (GAN) to calculate poisoning and adversarial samples based on machine learning model vulnerabilities. It can make traditional network attack traffic escape black-box online ML-NIDS. At the same time, model extraction and fuzzing test are used to enhance the convergence of VulnerGAN. Compared with the state-of-the-art algorithms, the VulnerGAN backdoor attack increases 33.28% in concealment, 18.48% in aggressiveness, and 46.32% in timeliness.

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Adversarial machine learning in Network Intrusion Detection Systems

Cited by 116 publications

References 18 publications

Mitigation of Black-Box Attacks on Intrusion Detection Systems-Based ML

Mitigation of Black-Box Attacks on Intrusion Detection Systems-Based ML

Adversarial learning techniques for security and privacy preservation: A comprehensive review

VulnerGAN: a backdoor attack through vulnerability amplification against machine learning-based network intrusion detection systems

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