Feature selection based on Rough set and modified genetic algorithm for intrusion detection

Guo, Yuteng; Wang, Beizhan; Zhao, Xinxing; Xie, Xiaolin; Lin, Lida; Zhou, Qingda

doi:10.1109/iccse.2010.5593765

Cited by 18 publications

(8 citation statements)

References 4 publications

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“…Wang et al [77] were able to achieve satisfactory results by obtaining an optimal number of features and quicker execution time by integrating particle swarm optimisation (PSO) with ant colony system. Guo et al [78] integrated genetic algorithm (GA) with rough sets for optimal features selection. SVM was used for finding out the performance of selected features.…”

Section: Categorical Classification Of Algorithmic Techniquesmentioning

confidence: 99%

Review of image processing approaches for detecting plant diseases

Sinha

Shekhawat

2020

IET Image Processing

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Section: Categorical Classification Of Algorithmic Techniquesmentioning

confidence: 99%

Review of image processing approaches for detecting plant diseases

Sinha

Shekhawat

2020

IET Image Processing

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“…2. To compute the dominating feature subset, we use a rough set and a modified genetic algorithm-based method described in [38].…”

Section: Cluster Labeling Techniquementioning

confidence: 99%

MLH-IDS: A Multi-Level Hybrid Intrusion Detection Method

Gogoi

Bhattacharyya

Borah

et al. 2013

The Computer Journal

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With the growth of networked computers and associated applications, intrusion detection has become essential to keeping networks secure. A number of intrusion detection methods have been developed for protecting computers and networks using conventional statistical methods as well as data mining methods. Data mining methods for misuse and anomaly-based intrusion detection, usually encompass supervised, unsupervised and outlier methods. It is necessary that the capabilities of intrusion detection methods be updated with the creation of new attacks. This paper proposes a multi-level hybrid intrusion detection method that uses a combination of supervised, unsupervised and outlierbased methods for improving the efficiency of detection of new and old attacks. The method is evaluated with a captured real-time flow and packet dataset called the Tezpur University intrusion detection system (TUIDS) dataset, a distributed denial of service dataset, and the benchmark intrusion dataset called the knowledge discovery and data mining Cup 1999 dataset and the new version of KDD (NSL-KDD) dataset. Experimental results are compared with existing multi-level intrusion detection methods and other classifiers. The performance of our method is very good.

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“…A new feature selection algorithm combining a rough sets and genetic algorithms on the basis of clustering was proposed by Guo et al [57]. Firstly, it uses the rough set theory to process selection and then uses the improved genetic algorithm based on clustering to find the optimal subset in the remaining subset.…”

Section: Rough Setsmentioning

confidence: 99%

Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

Ganapathy

Kanagasabai

Muthurajkumar

et al. 2013

J Wireless Com Network

169

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Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.Keywords: Survey; Intrusion detection system; Neural networks; Fuzzy systems; Swarm intelligence; Particle swarm intelligence Review Intrusion detection systemsRecently, Internet has become a part and parcel of daily life. The current internet-based information processing systems are prone to different kinds of threats which lead to various types of damages resulting in significant losses. Therefore, the importance of information security is evolving quickly. The most basic goal of information security is to develop defensive information systems which are secure from unauthorized access, use, disclosure, disruption, modification, or destruction. Moreover, information security minimizes the risks related to the three main security goals namely confidentiality, integrity, and availability.Various systems have been designed in the past to identify and block the Internet-based attacks. The most important systems among them are intrusion detection systems (IDS) since they resist external attacks effectively. Moreover, IDSs provide a wall of defense which overcomes the attack of computer systems on the Internet. IDS could be used to detect different types of attacks on network communications and computer system usage where the traditional firewall cannot perform well. Intrusion detection is based on an assumption that the behavior of intruders differ from a legal user [1]. Generally, IDSs are broadly classified into two categories namely anomaly and misuse detection systems based on their detection approaches [2,3]. Anomaly intrusion detection determines whether deviation from the established normal usage patterns can be flagged as intrusions. On the other hand, misuse detection systems detect the violations of permissions effectively. Intrusion detection systems can be built by u...

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Feature selection based on Rough set and modified genetic algorithm for intrusion detection

Cited by 18 publications

References 4 publications

Review of image processing approaches for detecting plant diseases

Review of image processing approaches for detecting plant diseases

MLH-IDS: A Multi-Level Hybrid Intrusion Detection Method

Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

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