Review on intrusion detection using feature selection with machine learning techniques

Kalimuthan, C.; Renjit, J. Arokia

doi:10.1016/j.matpr.2020.06.218

Cited by 34 publications

(13 citation statements)

References 37 publications

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“…Most of the papers on heuristic intrusion detection have focused on machine learning [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. The authors of this paper consider a different approach to intrusion detection: packet scoring.…”

Section: Related Workmentioning

confidence: 99%

Multivariable Heuristic Approach to Intrusion Detection in Network Environments

Niemiec

Kościej

Gdowski

2021

Entropy

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The Internet is an inseparable part of our contemporary lives. This means that protection against threats and attacks is crucial for major companies and for individual users. There is a demand for the ongoing development of methods for ensuring security in cyberspace. A crucial cybersecurity solution is intrusion detection systems, which detect attacks in network environments and responds appropriately. This article presents a new multivariable heuristic intrusion detection algorithm based on different types of flags and values of entropy. The data is shared by organisations to help increase the effectiveness of intrusion detection. The authors also propose default values for parameters of a heuristic algorithm and values regarding detection thresholds. This solution has been implemented in a well-known, open-source system and verified with a series of tests. Additionally, the authors investigated how updating the variables affects the intrusion detection process. The results confirmed the effectiveness of the proposed approach and heuristic algorithm.

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Section: Related Workmentioning

confidence: 99%

Multivariable Heuristic Approach to Intrusion Detection in Network Environments

Niemiec

Kościej

Gdowski

2021

Entropy

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“…NIDS analyses network traffic passing through computer environments [14,3]. Its role is mainly to monitor the network events against suspicious activities that may violate or bypass security policies of security components such as firewalls, Web Application Firewalls and proxies.…”

Section: B Intrusion Detection Systemsmentioning

confidence: 99%

“…The Global Internet Usage Statistics report confirms a growth of 1,114% and more than 2 quintillion bytes of data are generated every day. Along with this growth, cybercrime is becoming more sophisticated and continues to grow day by day [1,2,3]. As a result, the risks of being attacked and targeted by the hacker community remain more likely and could be costly for victims of cyber-attacks.…”

Section: Introductionmentioning

confidence: 99%

A Multiagent and Machine Learning based Hybrid NIDS for Known and Unknown Cyber-attacks

Ouiazzane¹,

Addou²,

Barramou³

2021

IJACSA

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The objective of this paper is to propose a hybrid Network Intrusion Detection System (NIDS) for the detection of cyber-attacks that may target modern computer networks. Indeed, in the era of technological evolution that the world is currently experiencing, hackers are constantly inventing new attack mechanisms that can bypass traditional security systems. Thus, NIDS are now an essential security brick to be deployed in corporate networks to detect known and zero-day attacks. In this research work, we propose a hybrid NIDS model based on the use of both a signature-based NIDS and an anomaly detection NIDS. The proposed system is based on agent technology, SNORT signature-based NIDS, machine learning techniques and the CICIDS2017 dataset is used for training and evaluation purposes. Thus, the CICIDS2017 dataset has undergone several pre-processing actions, namely, dataset cleaning, and dataset balancing as well as reducing the number of attributes (from 79 to 33 attributes). In addition, a set of machine learning algorithms are used, such as decision tree, random forest, Naive Bayes and multilayer perceptron, and are evaluated using some metrics, such as recall, precision, F-measure and accuracy. The detection methods used give very satisfactory results in terms of modeling benign network traffic and the accuracy reaches 99.9% for some algorithms.

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“…Numerous machine-learning-based detection approaches are studied, for example, detecting multi-type attacks in the cloud [36,37], cyber-aggressive comments [38], early fault in predictive maintenance [39], brain tumor [40], voice activity [41], plant diseases [42]. Related to trending topics detection, the existing event detection algorithms can be divided into three main approaches based on textual content of the news, namely document pivot, feature pivot, and probabilistic topic model [43,44].…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy

et al. 2020

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More and more people are involved in sustainability-related activities through social network to support/protect their idea or motivation for sustainable development. Understanding the variety of issues of social pulsation is crucial in development of social sustainability. However, issues in social media generally change overtime. Issues not identified in advance may soon become popular topics discussed in society, particularly controversial issues. Previous studies have focused on the detection of hot topics and discussion of controversial issues, rather than the identification of potential controversial issues, which truly require paying attention to social sustainability. Furthermore, previous studies have focused on issue detection and tracking based on historical data. However, not all controversial issues are related to historical data to foster the cases. To avoid the above-mentioned research gap, Artificial Intelligence (AI) plays an essential role in issue detection in the early stage. In this study, an AI-based solution approach is proposed to resolve two practical problems in social media: (1) the impact caused by the number of fan pages from Facebook and (2) awareness of the levels for an issue. The proposed solution approach to detect potential issues is based on the popularity of public opinion in social media using a Web crawler to collect daily posts related to issues in social media under a big data environment. Some analytical findings are carried out via the congregational rules proposed in this research, and the solution approach detects the attentive subjects in the early stages. A comparison of the proposed method to the traditional methods are illustrated in the domain of green energy. The computational results demonstrate that the proposed approach is accurate and effective and therefore it provides significant contribution to upsurge green energy deployment.

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Review on intrusion detection using feature selection with machine learning techniques

Cited by 34 publications

References 37 publications

Multivariable Heuristic Approach to Intrusion Detection in Network Environments

Multivariable Heuristic Approach to Intrusion Detection in Network Environments

A Multiagent and Machine Learning based Hybrid NIDS for Known and Unknown Cyber-attacks

Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy

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