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首页> 外文会议>International Symposium on Advanced Electrical and Communication Technologies >A Hybrid Optimization Framework Based on Genetic Algorithm and Simulated Annealing Algorithm to Enhance Performance of Anomaly Network Intrusion Detection System Based on BP Neural Network
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A Hybrid Optimization Framework Based on Genetic Algorithm and Simulated Annealing Algorithm to Enhance Performance of Anomaly Network Intrusion Detection System Based on BP Neural Network

机译：基于遗传算法和模拟退火算法的混合优化框架，以提高基于BP神经网络的异常网络入侵检测系统的性能

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Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an organization plays a vital part in attack mitigation, and it has become an integral part of a secure organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.
机译：如今，网络安全已成为计算机安全和防御领域的全球热门话题。网络基础架构中的入侵和攻击主要导致巨大的财务损失，大量的敏感数据泄漏，从而降低组织的效率，竞争力和生产质量。网络入侵检测系统（NIDS）是用于深度防御计算机网络的宝贵工具。它被广泛部署在网络体系结构中，以便监视，检测并最终响应任何可能威胁网络资源和服务的机密性，完整性和可用性的异常行为和滥用。因此，在组织中存在NIDS在缓解攻击中起着至关重要的作用，并且它已成为安全组织的组成部分。在本文中，我们建议使用一种基于改进的遗传算法（GA）和模拟退火算法（SAA）的新型混合框架（GASAA），优化一种广泛用于入侵检测的非常流行的软计算工具，即反向传播神经网络（BPNN）。 。通过优化策略（适应度值散列（FVH））改进了遗传算法，该策略减少了执行时间，收敛时间并节省了处理能力。在KDD CUP'99数据集上的实验结果表明，我们基于优化的ANIDS（异常NIDS）的BPNN，称为“ ANIDS BPNN-GASAA”，在检测率和误报率方面优于几种最新方法。此外，通过FVH改进GA可以节省处理能力和执行时间。因此，我们提出的IDS非常适合于网络异常检测。

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