Fuzzy min–max neural network and particle swarm optimization based intrusion detection system

Azad, Chandrashekhar; Jha, Vijay Kumar

doi:10.1007/s00542-016-2873-8

Cited by 31 publications

(7 citation statements)

References 37 publications

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“…This IDS was tested using KDD '99. A year later, Azad and Jha [89] proposed another IDS utilizing a fuzzy min-max neural network as a classifier and particle swarm for optimization, again testing it with KDD '99.…”

Section: E Cybercrime Detection Using Fuzzy Logic Neural Networkmentioning

confidence: 99%

Comprehensive Review of Cybercrime Detection Techniques

et al. 2020

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Cybercrimes describe cases of indictable offences and misdemeanours in which computer or any communication tools are involved as targets, commission instruments, incidental to, or that cases are associated with the prevalence of computer technology. Common forms of cybercrimes could be child pornography, cyberstalking, identity theft, cyber laundering, credit card theft, cyber terrorism, drug sale, data leakage, sexually explicit content, phishing and other cyber hacking. These kinds of cybercrimes are mostly leading to breaching users' privacy, security violation, business loss, financial fraud, or damage in public as well as government properties. Hence, this paper intensively reviews cybercrime detection and prevention methods. It first explores the different types of cybercrimes then discusses their threats against privacy and security in computer systems. It also describes the strategies that cybercriminals might utilize in committing these crimes against individuals, organizations, and societies. The paper then reviews the existing techniques of cybercrime detection and prevention. It objectively discusses the strengths and critically analyses the vulnerabilities of each technique. As a future study, the paper provides recommendations for the development of cybercrime detection model in which it is capable to effectively detect cybercrime in comparison to the existing techniques.

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Section: E Cybercrime Detection Using Fuzzy Logic Neural Networkmentioning

confidence: 99%

Comprehensive Review of Cybercrime Detection Techniques

et al. 2020

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“…Salunkhe and Mali [45] proposed ensemble classifier, combined different base classifiers, the data detection model 1 extracted original dataset and created data subsets model 2 extracting original training data feature subset and created feature subset, then combined outputs of two models obtained the final prediction by ensemble classifier whether intrusion exist or not. This technique evaluated on KDD CUP 1999 dataset, performance tested for more number of attack categories.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive survey of Machine Learning for Intrusion Detection

Rao¹,

Rao²,

P.V.G.D³

2019

IJRAT

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With the massive usage of the internet, the vulnerability of network security becomes an important issue. Intrusion detection System (IDS) is one of the major research problems in network security. IDSs are developed to detect known and unknown attacks of both Computer and computer networks. This paper presents different methods used in IDS for protecting computers and networks for over a decade. This study analyzes different machine learning methods in IDS. It also reviews related studies in the period between 2008 and 2017 focusing on single, hybrid, and ensemble classifiers with relevant datasets.

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“…Kudłacik et al [10] presented an intrusion detection method based on a fuzzy approach. Azad et al [11] introduced an intrusion detection system which is based on the fuzzy min, fuzzy max neural network and the particle swarm optimization. Ramakrishnan et al [12] proposed an entropy-based feature selection to select the important features, layered fuzzy control language to generate fuzzy rules, and layered classifier to detect various network attacks namely neptune, smurf, back, and mailbomb.…”

Section: Related Workmentioning

confidence: 99%