A Comprehensive Survey for Machine Learning and Deep Learning Applications for Detecting Intrusion Detection

Surakhi, Ola; García, Antonio; Jamoos, Mohammed; AlKhanafseh, Mohammad

doi:10.1109/acit53391.2021.9677375

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…The studies [8][9][10][11][12] illustrate the comparative analysis of ML and DL approaches in intrusion detection. In articles [13][14][15][16], the authors comprehensively explain about the ML algorithms for intrusion detection along with the detailed explanation of the datasets and challenges for modern scenario. Thakkar and Lohiya [17] thoroughly described the taxonomy of IDS along with the techniques used for the evaluation of IDS.…”

Section: Introductionmentioning

confidence: 99%

A Systematic and Comprehensive Survey of Recent Advances in Intrusion Detection Systems Using Machine Learning: Deep Learning, Datasets, and Attack Taxonomy

2023

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Recently, intrusion detection systems (IDS) have become an essential part of most organisations’ security architecture due to the rise in frequency and severity of network attacks. To identify a security breach, the target machine or network must be watched and analysed for signs of an intrusion. It is defined as efforts to compromise the confidentiality, integrity, or availability of a computer or network or to circumvent its security mechanisms. Several IDS have been proposed in the literature to efficiently detect such attempts exploiting different characteristics of cyberattacks. These systems can provide with timely sensing the network intrusions and, subsequently, notifying the manager or the responsible person in an organisation. Important actions are then carried out to reduce the degree of damage caused by the intrusion. Organisations use such techniques to defend their systems from the network disconnectivity and increase reliance on the information systems by employing intrusion detection. This paper presents a detailed summary of recent advances in IDS from the literature. Nevertheless, a review of future research directions for detecting malicious operations and launching different attacks on systems is discussed and highlighted. Furthermore, this study presents detailed description of well-known publicly available datasets and a variety of strategies developed for dealing with intrusions.

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Section: Introductionmentioning

confidence: 99%

A Systematic and Comprehensive Survey of Recent Advances in Intrusion Detection Systems Using Machine Learning: Deep Learning, Datasets, and Attack Taxonomy

2023

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“…One application where machine learning methods are widely used is intrusion detection systems (IDSs) [1,2]. IDS is employed to monitor the network traffic and identify any unauthorized efforts to access that network through the analysis of incoming and outgoing actions, with the aim of detecting indications of potentially harmful actions [3].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, this has given rise to significant security threats and underscored the urgency to enhance network security. As a result, numerous researchers have focused their efforts on enhancing intrusion detection systems (IDSs) by improving the detection rate for both novel and known attacks, while concurrently reducing the occurrence of false alarms (false alarm rate or FAR) [1]. Unsupervised intrusion detection techniques have emerged as a solution that eliminates the need for labeled data [4].…”

Section: Introductionmentioning

confidence: 99%

A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System

et al. 2023

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An intrusion detection system (IDS) plays a critical role in maintaining network security by continuously monitoring network traffic and host systems to detect any potential security breaches or suspicious activities. With the recent surge in cyberattacks, there is a growing need for automated and intelligent IDSs. Many of these systems are designed to learn the normal patterns of network traffic, enabling them to identify any deviations from the norm, which can be indicative of anomalous or malicious behavior. Machine learning methods have proven to be effective in detecting malicious payloads in network traffic. However, the increasing volume of data generated by IDSs poses significant security risks and emphasizes the need for stronger network security measures. The performance of traditional machine learning methods heavily relies on the dataset and its balanced distribution. Unfortunately, many IDS datasets suffer from imbalanced class distributions, which hampers the effectiveness of machine learning techniques and leads to missed detection and false alarms in conventional IDSs. To address this challenge, this paper proposes a novel model-based generative adversarial network (GAN) called TDCGAN, which aims to improve the detection rate of the minority class in imbalanced datasets while maintaining efficiency. The TDCGAN model comprises a generator and three discriminators, with an election layer incorporated at the end of the architecture. This allows for the selection of the optimal outcome from the discriminators’ outputs. The UGR’16 dataset is employed for evaluation and benchmarking purposes. Various machine learning algorithms are used for comparison to demonstrate the efficacy of the proposed TDCGAN model. Experimental results reveal that TDCGAN offers an effective solution for addressing imbalanced intrusion detection and outperforms other traditionally used oversampling techniques. By leveraging the power of GANs and incorporating an election layer, TDCGAN demonstrates superior performance in detecting security threats in imbalanced IDS datasets.

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Performance Evaluation of Intrusion Detection System Using Machine Learning and Deep Learning Algorithms

Hossain,

Ghose,

Partho

et al. 2023

2023 4th International Conference on Big Data Analytics and Practices (IBDAP)

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A Comprehensive Survey for Machine Learning and Deep Learning Applications for Detecting Intrusion Detection

Cited by 5 publications

References 52 publications

A Systematic and Comprehensive Survey of Recent Advances in Intrusion Detection Systems Using Machine Learning: Deep Learning, Datasets, and Attack Taxonomy

A Systematic and Comprehensive Survey of Recent Advances in Intrusion Detection Systems Using Machine Learning: Deep Learning, Datasets, and Attack Taxonomy

A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System

Performance Evaluation of Intrusion Detection System Using Machine Learning and Deep Learning Algorithms

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