基于孔雀交配概念的入侵检测系统特征选择
Feature Selection using the concept of Peafowl Mating in IDS
AI总结：
本文提出一种基于孔雀交配行为的优化特征选择算法，用于减少云数据规模，提升入侵检测系统在NSL-KDD和Kyoto数据集上的检测效率与安全性。
摘要：
云计算作为一种基于互联网的服务，具有很高的适用性，它依赖于计算资源的共享。云计算提供的服务包括基础设施即服务、平台即服务和软件即服务。该技术之所以流行，是因为其卓越的性能、高水平的计算能力、低成本的服务、可扩展性、可用性和灵活性。云环境中数据的可获取性和开放性使其容易遭受网络攻击。为了检测攻击，使用入侵检测系统来识别攻击并确保信息安全。本文提出了一种连贯且高效的入侵检测系统，以在云环境中实现更高的安全确定性。本文将孔雀的交配行为融入优化算法中，并将其用作特征选择算法。该算法用于减少云数据的庞大规模，使入侵检测系统能够在云上高效地检测入侵。所提出的模型已在NSL-KDD数据集和Kyoto数据集上进行了实验，并证明是一种更好且高效的入侵检测系统。
英文摘要：
Cloud computing has high applicability as an Internet based service that relies on sharing computing resources. Cloud computing provides services that are Infrastructure based, Platform based and Software based. The popularity of this technology is due to its superb performance, high level of computing ability, low cost of services, scalability, availability and flexibility. The obtainability and openness of data in cloud environment make it vulnerable to the world of cyber-attacks. To detect the attacks Intrusion Detection System is used, that can identify the attacks and ensure information security. Such a coherent and proficient Intrusion Detection System is proposed in this paper to achieve higher certainty levels regarding safety in cloud environment. In this paper, the mating behavior of peafowl is incorporated into an optimization algorithm which in turn is used as a feature selection algorithm. The algorithm is used to reduce the huge size of cloud data so that the IDS can work efficiently on the cloud to detect intrusions. The proposed model has been experimented with NSL-KDD dataset as well as Kyoto dataset and have proved to be a better as well as an efficient IDS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
