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

机译:基于有效群体优化的入侵检测云分类器系统

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Most of the swarm optimization techniques are inspired by the characteristics as well as behaviour of flock of birds whereas Artificial Bee Colony is based on the foraging characteristics of the bees. However, certain problems which are solved by ABC do not yield desired results in-terms of performance. ABC is a new devised swarm intelligence algorithm and predominately employed for optimization of numerical problems. The main reason for the success of ABC algorithm is that it consists of feature such as fathomable and flexibility when compared to other swarm optimization algorithms and there are many possible applications of ABC. Cloud computing has their limitation in their application and functionality. The cloud computing environment experiences several security issues such as Dos attack, replay attack, flooding attack. In this paper, an effective classifier is proposed based on Artificial Bee Colony for cloud computing. It is evident in the evaluation results that the proposed classifier achieved a higher accuracy rate.
机译:大多数蜂群优化技术均受鸟群的特性和行为的启发,而人工蜂群则基于蜜蜂的觅食特性。但是,通过ABC解决的某些问题在性能方面无法获得理想的结果。 ABC是一种新设计的群体智能算法,主要用于优化数字问题。 ABC算法成功的主要原因是,与其他群体优化算法相比,它具有可肥性和灵活性等特征,并且ABC有许多可能的应用。云计算在其应用程序和功能方面有其局限性。云计算环境遇到一些安全问题,例如Dos攻击,重播攻击,泛洪攻击。本文提出了一种基于人工蜂群的有效分类器,用于云计算。从评估结果中可以明显看出,所提出的分类器实现了更高的准确率。

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