Cloud Computing Intrusion Detection Using Artificial Bee Colony-BP Network Algorithm

Yang Hui
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Abstract

Cloud computing characterizes a methodology for computing communications in a much effective manner, and a business paradigm for trading computing resources and services. Alternatively, these difficult and distributed planning’s turn a striking objective for intruders. Cloud computing provides huge latent for enhancing production and decrease expenditures. However it simultaneously acquires several novel security risks. Intrusion Detection Systems (IDS) have been employed broadly for identifying malicious actions in network communication and hosts. In this work, an artificial bee colony-BP neural network algorithm is applied to the detection module, in order to detect the complicated aggressive behaviors. Through example verification, the artificial bee colony-BP network algorithm has improved intrusion detection efficiency and classification precision, and can effectively guarantee the safety of the cloud computing environment. Subject Categories and Descriptors [D.4.6 Security and Protection] [C.2 Cmputer Communication Networks] Security and protection [F.1.1 Models of Computation]; Neural networks General Terms: Cloud Computing, Neural Networks, IDS
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基于人工蜂群bp网络算法的云计算入侵检测
云计算的特点是一种以非常有效的方式计算通信的方法,以及一种交易计算资源和服务的业务范例。另外,这些困难和分散的计划变成了入侵者的一个引人注目的目标。云计算为提高生产和减少支出提供了巨大的潜力。然而,它同时也带来了一些新的安全风险。入侵检测系统(IDS)被广泛用于识别网络通信和主机中的恶意行为。本文将人工蜂群- bp神经网络算法应用于检测模块,以检测复杂的攻击行为。通过实例验证,人工蜂群- bp网络算法提高了入侵检测效率和分类精度,能够有效保障云计算环境的安全。主题类别和描述词[D.4.6安全与保护][C.2 .6计算机通信网络安全与保护[F.1.1计算模型];通用术语:云计算,神经网络,入侵检测系统
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