Intrusion detection using a fuzzy genetics-based learning algorithm

Abadeh, Mohammad Saniee; Habibi, Jafar; Lucas, Caro

doi:10.1016/j.jnca.2005.05.002

Cited by 98 publications

(21 citation statements)

References 28 publications

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“…Another good way to exploit this problem is to utilize a distributed environment. Folin et al [97] and Abadeh et al [7] both examined distributed intrusion detection models, where each node was only assigned part of the data. An ensemble method was used to fuse decisions.…”

Section: Discussionmentioning

confidence: 99%

The use of computational intelligence in intrusion detection systems: A review

Banzhaf

2010

Applied Soft Computing

551

243

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Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will be on core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.

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Section: Discussionmentioning

confidence: 99%

The use of computational intelligence in intrusion detection systems: A review

Banzhaf

2010

Applied Soft Computing

551

243

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“…Abadeh, et al, proposed a fuzzy genetics-based learning algorithm that could be used as a network intrusion detection system [6]. Wang et al, defined a method combining artificial neural networks and fuzzy clustering to improve the capabilities of previously proposed systems to detect low-occurrence attacks [7].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy zero day exploits detector system

Shaout

Smyth²

2017

IJACR

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“…Fuzzy logic has been originally proposed by Zadeh as a tool for dealing with linguistic uncertainty and vagueness ubiquitous in the imprecise meaning of words [38]. Fuzzy systems have demonstrated their ability to solve different kinds of problems in various applications domains [39]. A fuzzy-based inference mechanism is used to infer a soft boundary between anomalous and normal behaviour, which is otherwise very difficult to determine when they overlap or are very close [43].…”

Section: Decision Making Based On Fuzzy Logicmentioning

confidence: 99%

A Hybrid Data Mining based Intrusion Detection System for Wireless Local Area Networks

Moorthy¹,

Sathiyabama²

2012

IJCA

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The exponential growth in wireless network faults, vulnerabilities, and attacks make the WLAN security management a challenging research area [29]. Data mining applied to intrusion detection is an active area of research. The main reason for using data mining techniques for intrusion detection systems is due to the enormous volume of existing and newly appearing network data that require processing. Data mining follows anomaly based intrusion detection. The drawback of the anomaly based intrusion detection in a wireless network is the high rate of false positive. This can be solved by a designing a hybrid intrusion detection system by connecting a misuse detection module to the anomaly detection module. In this paper, we propose to develop a hybrid intrusion detection system for wireless local area networks, based on Fuzzy logic. In this Hybrid Intrusion Detection system, anomaly detection is performed using the Bayesian network technique and misuse detection is performed using the Support Vector Machine (SVM) technique. The overall decision of system is performed by the fuzzy logic. For anomaly detection using Bayesian network, each node has a monitoring agent and a classifier within it for its detection and a mobile agent for information collection. The anomaly is measured based on the naïve Bayesian technique. For misuse detection using SVM, all the data that lie within the hyper plane are considered to be normal whereas the data that lie outside the hyper plane are considered to be intrusive. The outputs of both anomaly detection and misuse detection modules are applied by the fuzzy decision rules to perform the final decision making. Hybrid detection system improves the detection performance by combining the advantages of the misuse and anomaly detection [33].

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Intrusion detection using a fuzzy genetics-based learning algorithm

Cited by 98 publications

References 28 publications

The use of computational intelligence in intrusion detection systems: A review

The use of computational intelligence in intrusion detection systems: A review

Fuzzy zero day exploits detector system

A Hybrid Data Mining based Intrusion Detection System for Wireless Local Area Networks

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