Swarm intelligence in intrusion detection: A survey

Kolias, Constantinos; Kambourakis, Georgios; Μaragoudakis, Manolis

doi:10.1016/j.cose.2011.08.009

Cited by 192 publications

(112 citation statements)

References 57 publications

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“…SI first inaugurated by Beni [4] in cellular robotics system. Researchers are attracted with the behavior of flocks of social insects from many years.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Mutation Strategies in Bat Algorithm

Bangyal¹,

Ahmad²,

Tayyab³

et al. 2018

ijacsa

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Abstract-Bat algorithm (BA) is a population based stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. BA has been mostly used to resolve diverse kind of optimization problems and one of major issue faced by BA is frequently captured in local optima meanwhile handling the complex real world problems. Many authors improved the standard BA with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This paper aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of BA. It has been tried to provide guidelines for the researchers who are active in the area of BA and its mutation strategies. The objective of this study is divided in two sections: primarily to display the improvement of BA with mutation strategies that may enhance the performance of standard BA up to great extent and secondly, to motivate the researchers and developers for using BA to solve the complex real world problems. This study presents a comprehensive survey of the various BA algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the BA algorithm in detail for the researcher.

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“…SI first inaugurated by Beni [4] in cellular robotics system. Researchers are attracted with the behavior of flocks of social insects from many years.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Mutation Strategies in Bat Algorithm

Bangyal¹,

Ahmad²,

Tayyab³

et al. 2018

ijacsa

View full text Add to dashboard Cite

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“…In order to disarm DoS/DDoS attacks and any deviation, not only the detection of the malevolent behavior must be achieved, but the network traffic belonging to the attackers should be also blocked [23,24,25]. Thus, a predictor (detector) should take an appropriate action to thwart the attacks and should be able to adjust itself to the changing dynamics of the anomalies/attacks [20,26].…”

Section: Introductionmentioning

confidence: 99%

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

Karami

Zapata

2015

Neurocomputing

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Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based (host-centric) Internet infrastructure. NDN can overcome the fundamental limitations of the current Internet, in particular, Denial-of-Service (DoS) attacks. However, NDN can be subject to new type of DoS attacks namely Interest flooding attacks and content poisoning. These types of attacks exploit key architectural features of NDN. This paper presents a new intelligent hybrid algorithm for proactive detection of DoS attacks and adaptive mitigation reaction in NDN. In the detection phase, a combination of multiobjective evolutionary optimization algorithm with PSO in the context of the RBF neural network has been applied in order to improve the accuracy of DoS attack prediction. Performance of the proposed hybrid approach is also evaluated successfully by some benchmark problems. In the adaptive reaction phase, we introduced a framework for mitigating DoS attacks based on the misbehaving type of network nodes. The evaluation through simulations shows that the proposed intelligent hybrid algorithm (proactive detection and adaptive reaction) can quickly and effectively respond and mitigate DoS attacks in adverse conditions in terms of the applied performance criteria.

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“…The exact difference is, Data Mining-based approaches are found on predicting unknown patterns and knowledge discovery of Databases, whereas, Machine Learning-based approaches are found on predicting 'known' patterns and also represent a Pre-Process step with an intent to improve the learner efficiency. Later, Evolutionary Detection methodologies [6] are developed based on real behavior of mammals, insects and species hunting the prey. For example, the prompt of Ant Colony Optimization algorithm was designed based on the real attitude of ants moving towards the prey.…”

Section: Introductionmentioning

confidence: 99%