Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms

Haque, Md. Enamul; Al-Kharobi, Talal

doi:10.7763/ijmlc.2015.v5.476

Cited by 20 publications

(3 citation statements)

References 28 publications

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“…In [15,16], an improved IDS system with the application of Snort rules to detect the network probe attacks was proposed. The authors devised a novel method to enhance the rules of snort IDS to effectively detect the network probe attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization and C4.5 for Network Intrusion Detection and Prevention System

Kumar,

Udaya Suriya Rajkumar,

Viswanath

et al. 2024

IJC

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Network Intrusion Detection and Prevention systems (NIDPS) ensure network security and used to effectively detect various attacks and completely stop them from intruding over a network. Since, securing sensitive information carried by various organizations is much more significant, developing enhanced security models become inevitable. To meet the growing demand in safeguarding the network from various known and unknown attacks. In this paper, a Hybrid Particle Swarm Optimization and C4.5 (HPSOCM) method is proposed to network based intrusion prevention system to detect unknown attacks and a signature based SNORT method to detect the known attacks in NIDS. In the hybrid method, we use data mining approach to mine the unknown attacks. Hence, we develop an anomalous detection model and train it using the data mining rules. The trained network is capable of detecting various unknown attacks. The conventional signature based SNORT method detects the known attacks by matching the detected threats from the KDD99 dataset. The proposed HPSOCM method is demonstrated using simulation and the performances were evaluated in terms of Accuracy, Specificity, Detection Rate and False Alarm Rate. The proposed method had produced better efficiency compared to various other existing methods.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization and C4.5 for Network Intrusion Detection and Prevention System

Kumar,

Udaya Suriya Rajkumar,

Viswanath

et al. 2024

IJC

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show abstract

“…In literature, several approaches for classifiers combination proposed. [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21], [22,23,24,25,26,27,28,29,30,31]…”

Section: Hybrid and Ensemble Pattern Recognitionmentioning

confidence: 99%

Review of IDS Develepment Methods in Machine Learning

Aburomman

Reaz

2016

IJECE

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<p>Due to the rapid advancement of knowledge and technologies, the problem of decision making is getting more sophisticated to address, therefore the inventing of new methods to solve it is very important. One of the promising directions in machine learning and data mining is classifier combination. The popularity of this approach is confirmed by the still growing number of publications. This review paper focuses mainly on classifier combination known also as combined classifier, multiple classifier systems, or classifier ensemble. Eventually, recommendations and suggestions have also included.</p>

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“…Researches on user authentication can be divided into two types: one-time certification and sustainable certification [2]. The former can be classified into following methods: Traditional account, password authentication [3]- [5], Smart card-based authentication [6], the authentication based on biological and behavioral characteristics (e.g. fingerprint, users' habits of using the mouse [7], [8] and keyboard input [9], [10]).…”

Section: Introductionmentioning

confidence: 99%

A Kind of Authentication Method based on User Web Browsing Features

Ji¹,

Zhao²,

Wang³

et al. 2017

IJMLC

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Abstract-Traditional authentication methods based on user browsing behaviors consider relatively one-snidely on user browsing habits. They mainly research on the relationships between the sequences of websites or contents without considering user habits comprehensively. So the accuracy when they distinguish different users' web browsing behaviors cannot ensure enough safety, which can be further optimized. This paper introduces a new method which studies from favorite websites, contents and periods of browsing time. It uses Apriori algorithm to mine user's frequent itemsets along with the text classification method and normal distribution to calculate access periods of time. Logic regression algorithm is applied onto user authentication. Experiment shows that detection rate can reach 92.7% while false alarm rate is 6.4%.Index Terms-Identity authentication, user behavior, web browsing features, frequent itemset.

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Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms

Cited by 20 publications

References 28 publications

A Hybrid Particle Swarm Optimization and C4.5 for Network Intrusion Detection and Prevention System

A Hybrid Particle Swarm Optimization and C4.5 for Network Intrusion Detection and Prevention System

Review of IDS Develepment Methods in Machine Learning

A Kind of Authentication Method based on User Web Browsing Features

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