A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems

Eesa, Adel Sabry; Orman, Zeynep; Brifcani, Adnan Mohsin Abdulazeez

doi:10.1016/j.eswa.2014.11.009

Cited by 308 publications

(149 citation statements)

References 14 publications

(8 reference statements)

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“…Among these works, we can mention the work of Gao et al 15 where ACO algorithm and SVM classifier were combined to find the most discriminative features in intrusion detection dataset. In the work of Eesa et al 16 a new algorithm named CuttleFish (CFA) was applied to the FS problem.…”

Section: Overview Of Feature Selection Methodsmentioning

confidence: 99%

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GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

Guendouzi¹,

Boukra²

2017

IJCIS

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Feature selection is used as a preprocessing step in the resolution of many problems using machine learning. It aims to improve the classification accuracy, speed up the model generation process, reduce the model complexity and reduce the required storage space. Feature selection is an NP-hard combinatorial optimization problem. It is the process of selecting a subset of relevant, non-redundant features from the original ones. Among the works that are proposed to solve this problem, few are dedicated for intrusion detection. This paper presents a new feature selection approach for intrusion detection, using the Biogeography Based Optimization (BBO) algorithm. The approach which is named Guided Adaptive Binary Biogeography Based Optimization (GAB-BBO) uses the evolutionary state estimation (ESE) approach and a new migration and mutation operators. The ESE approach we propose in this paper uses the Hamming distance between the binary solutions to calculate an evolutionary factor f which determines the population diversity. During this process, fuzzy logic is used through a fuzzy classification method, to perform the transition between the numerical f value and four evolutionary states which are : convergence, exploration, exploitation and jumping out. According to the state identified, GAB-BBO adapts the algorithm behavior using a new adaptive strategy. The performances of GAB-BBO are evaluated on benchmark functions and the Kdd'99 intrusion detection dataset. In addition, we use other different datasets for further validation. Comparative study with other algorithms is performed and the results show the effectiveness of the proposed approach.

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Section: Overview Of Feature Selection Methodsmentioning

confidence: 99%

“…We choose Kdd'99 because it is the largest publicly available and the widest used dataset by the researchers in the intrusion detection field 16,14 .…”

Section: Kdd'99 Datasetmentioning

confidence: 99%

GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

Guendouzi¹,

Boukra²

2017

IJCIS

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“…Cuttlefish Algorithm (CFA) is implemented for feature selection in intrusion detection system. Decision tree is taken as a classifier [51]. In Autism dataset, eight feature selection methods are applied and finally a model is developed for fusion of these results.…”

Section: ____________________________________________________________mentioning

confidence: 99%

A Survey on Feature Selection Algorithms

Kumar¹

2015

IJRITCC

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Abstract-One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and fe ature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations.

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“…This method achieves better performance in comparison to the K-Means, ID3, Naïve Bayes, K-NN, and SVM. Adel Sabry Eesa et al [22] represented A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Cuttlefish.…”

Section: Previous Researchsmentioning

confidence: 99%

An Improved Intrusion Detection System based on Combined Cluster Centers and Nearest Neighbors

Shapoorifard¹,

Shamsinejad²

2017

IJCA

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In order to make computer systems completely secure, in addition to firewalls and other intrusion protection devices, other systems called intrusion detection systems (IDS) are needed to detect intrusion and provide solutions to counter the intruder if he penetrated through firewall, antivirus and other security devices. Many IDS have been developed based on machine learning techniques. Specifically, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning techniques. This paper proposes an improvement for a feature representation approach, namely the cluster center and nearest neighbor (CANN) approach.

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A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems

Cited by 308 publications

References 14 publications

GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

A Survey on Feature Selection Algorithms

An Improved Intrusion Detection System based on Combined Cluster Centers and Nearest Neighbors

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