Design and analysis of genetic fuzzy systems for intrusion detection in computer networks

Abadeh, Mohammad Saniee; Mohamadi, Hamid; Habibi, Jafar

doi:10.1016/j.eswa.2010.12.006

Cited by 67 publications

(24 citation statements)

References 45 publications

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“…(Gomez et al, 2002) [18] first demonstrated the work of fuzzy classifiers on intrusion detection system. Various GA models such as Michigan, Pittsburgh and Iterative rule learning approaches along with different swarm intelligence techniques have been applied by [19].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks

Chadha

Jain

2015

Intelligent Distributed Computing

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Abstract.With the drastic increase in internet usage, various categories of attacks have also evolved. Conventional intrusion detection techniques to counter these attacks have failed and thus substantial systems are needed to eliminate these attacks before they inflict huge damage. With the ability of computational intelligence systems to adapt, exhibit fault tolerance, high computational speed and error resilience against noisy information, a hybrid genetic fuzzy rule based inference engine has been designed in this paper. The fuzzy logic constructs precise and flexible patterns while the genetic algorithm based on evolutionary computation helps in attaining an optimal solution, thus their collaboration will increase the robustness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal behavior as well as anomalies in the network. Detailed analysis has been done on DARPA-KDD99 dataset to specify the behavior of each connection.

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Section: Related Workmentioning

confidence: 99%

Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks

Chadha

Jain

2015

Intelligent Distributed Computing

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“…is a feature vector set of s dimensional pattern space, according to some similarity measure, the set is aggregated into C sub set 12 , , , n X X X , 2 cn . The C subset is a fuzzy partition of the feature vector set X, ik  indicates that the feature vector i x belongs to the membership degree of the subset k X , thus the fuzzy classification can be got.…”

Section: Fuzzy C-mean Methodsmentioning

confidence: 99%

“…But it needs to match the network data with all the rules, when the network traffic is very large, it is inevitable that there will be a phenomenon of data packets missing. [12] …”

Section: Intrusion Detection Systemmentioning

confidence: 99%

“…[12] Abnormal intrusion detection refers to a behavior standard and it is the standard, when the behavior and this standard is not the same time to issue a warning. The key technology of anomaly intrusion detection system is to select the appropriate measure to make the correct judgment between the abnormal behavior and the intrusion behavior.…”

Section: Intrusion Detection Systemmentioning

confidence: 99%

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Research on Data Intrusion Detection Technology based on Fuzzy Algorithmn

Zhao¹,

Han²,

Shi³

2016

IJSIA

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“…Abadeh M S et al . [58] has proposed a design and analysis of genetic fuzzy systems for intrusion detection in computer networks. Performance of the proposed method is evaluated using detection rate and false positive rate parameters.…”

Section: Misuse Detectionmentioning

confidence: 99%

Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets

Azad

Jha²

2013

IJITCS

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Abstract-In the era of info rmation and communicat ion technology, Security is an important issue. A lot of effort and finance are being invested in this sector. Intrusion detection is one of the mos t prominent fields in this area. Data min ing in network intrusion detection can automate the network intrusion detection field with a greater efficiency. This paper presents a literature survey on intrusion detection system. The research papers taken in this literature survey are published fro m 2000 to 2012. We can see that almost 67 % of the research papers are focused on anomaly detection, 23 % on both anomaly and misuse detection and 10 % on misuse detection. In this literature survey statistics shows that 42 % KDD cup dataset, 20 % DA RPA dataset and 38 % other datasets are used by the different researchers for testing the effectiveness of their proposed method for misuse detection, anomaly detection or both.

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Design and analysis of genetic fuzzy systems for intrusion detection in computer networks

Cited by 67 publications

References 45 publications

Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks

Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks

Research on Data Intrusion Detection Technology based on Fuzzy Algorithmn

Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets

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