An Effective Swarm Optimization Based Intrusion Detection Classifier System for Cloud Computing

Kalaivani, S.; Vikram, A.; Gopinath, G.

doi:10.1109/icaccs.2019.8728450

Cited by 19 publications

(7 citation statements)

References 7 publications

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“…At present, researchers have proposed different algorithms for optimizing parameters in the context of Swarm Intelligence (SI) [2,4,[11][12], which has had a significant impact on the field of computational science over the past decade, inspired by the natural evolution of organisms and is especially useful for solving optimization problems. For example, Kalaivani et al proposed an effective classifier based on Artificial Bee Colony (ABC) for intrusion detection [4]. Neha et al introduced Salp Swarm Optimization (SSO) to optimize the hyperparameters of classifiers [2].…”

Section: Background and Related Workmentioning

confidence: 99%

“…WSN is a kind of network with limited energy consumption and weak storage capacity, in which nodes are often placed in "no man's land". Considering factors such as the "openness" of the network, the fixed routing mechanism of the data, and the limitation of resources in terms of time and space, some critical and sensitive data could be subject to several attacks, resulting in serious security problems [2][3][4]28].…”

Section: Introductionmentioning

confidence: 99%

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An Evolutionary-Based Approach for Low-Complexity Intrusion Detection in Wireless Sensor Networks

Zhang

Han

Marino

et al. 2021

Wireless Pers Commun

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As security threats are increasingly diversified, a critical problem in Wireless Sensor Network environments (WSNs) is detecting anomalies. WSNs are affected by several limitations, such as limited energy, insufficient memory, weak computation power, and short communication range. Hence, it is necessary to improve the detection accuracy and convergence speed of intrusion detection in such environments. In this article, we propose an intrusion detection model based on Time-Varying Parameter Improved Particle Swarm Optimization (TVP-IPSO) with Principal Component Analysis (PCA) and Support Vector Machine (SVM). The PCA is applied aimed at the data dimension reduction by compressing the data to reduce energy consumption, and an intrusion detection algorithm based on SVM is considered to ensure high detection accuracy. To optimize the SVM algorithm and identify its optimal parameters, the TVP-IPSO is used to improve the detection precision and convergence speed of the intrusion detection algorithm. Experimental results show that the detection accuracy of TVP-IPSO-SVM is higher than GA-SVM and IPSO-SVM, demonstrating that the proposed research has better adaptability, higher detection accuracy, and faster convergence speed when compared to other works presented.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evolutionary-Based Approach for Low-Complexity Intrusion Detection in Wireless Sensor Networks

Zhang

Han

Marino

et al. 2021

Wireless Pers Commun

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“…Indeed, SI can build security decision making models to fight against malicious threats and intrusions based on security postures and intelligence information using full-text search, mathematical optimization, logical reasoning, and probability analytic. For a typical use case, the work in [166] tries to adopt SI for intrusion detection in cloud computing. An ABC-based optimization classifier is built to perform classification of the cloud data packets for intrusion identification that is potential to solve security issues such as denial-of-service attack (DoS attack), replay attack, and flooding attacks in cloud computing.…”

Section: Attack Detectionmentioning

confidence: 99%

“…Attack detection [166] ABC An ABC-based optimization classifier is built to perform classification of the cloud data packets for intrusion identification that is potential to solve DoS attacks.…”

Section: A Antenna Designmentioning

confidence: 99%

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Pham¹,

Nguyen²,

Mirjalili³

et al. 2020

Preprint

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Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in nextgeneration wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

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“…Nowadays, SIA using metaheuristic algorithm which is inspired by nature are emerging and becoming more popular. Ant Colony Optimization (ACO) algorithm is one successful examples of SIA that has been developed by M. Dorigo [12]. The ACO algorithm is inspired by the behavior of an ant colony in finding the shortest path to food source [13].…”

Section: Introductionmentioning

confidence: 99%

Modelling of flexible beam based on ant colony optimization and cuckoo search algorithms

Ali¹,

Fadzilan²,

Shaari³

et al. 2021

J. vibroeng.

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Flexible beam structure is usually applied in various fields of engineering and industrial. There are few points of interest using flexible structure and one of the advantages is that its lighter compared to a rigid structure. Besides that, flexible beam also can save cost, reduce energy consumption, and improve operation safety. However, flexible beam structures are too sensitive and susceptible to with unwanted vibration that would cause damage or degradation to the structure system. Hence, to overcome the problem, appropriate modelling and controller for such systems should be developed. Currently, there are plenty of methods that have been developed by researchers to suppress undesired vibration. Based on previous studies, most researchers nowadays use system identification (SI) as a modelling technique to develop a dynamic model of flexible structure via swarm intelligence algorithm (SIA). Therefore, two type of algorithms was used in this work for modelling development of flexible beam structure, which are ant colony optimization (ACO) and cuckoo search algorithm (CSA). Based on the comparative results, CSA achieved the lowest mean square error (MSE) value of 6.1547×10 -9 meanwhile ACO recorded a MSE of 1.0728×10 -8 . Moreover, CSA was deduced to be the best model for flexible beam structure because it achieved 95 % confidence level in correlation test and has excellent stability in pole-zero diagram system. Thus, CSA is a suitable algorithm to represent the real behavior of flexible beam structure in a system.

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An Effective Swarm Optimization Based Intrusion Detection Classifier System for Cloud Computing

Cited by 19 publications

References 7 publications

An Evolutionary-Based Approach for Low-Complexity Intrusion Detection in Wireless Sensor Networks

An Evolutionary-Based Approach for Low-Complexity Intrusion Detection in Wireless Sensor Networks

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Modelling of flexible beam based on ant colony optimization and cuckoo search algorithms

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