Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.
Department of Artificial Intelligence & Data Science, Annamacharya Institute of Technology and Sciences, Rajampet, India.
Math Biosci Eng. 2021 Sep 15;18(6):8024-8044. doi: 10.3934/mbe.2021398.
Cybersecurity experts estimate that cyber-attack damage cost will rise tremendously. The massive utilization of the web raises stress over how to pass on electronic information safely. Usually, intruders try different attacks for getting sensitive information. An Intrusion Detection System (IDS) plays a crucial role in identifying the data and user deviations in an organization. In this paper, stream data mining is incorporated with an IDS to do a specific task. The task is to distinguish the important, covered up information successfully in less amount of time. The experiment focuses on improving the effectiveness of an IDS using the proposed Stacked Autoencoder Hoeffding Tree approach (SAE-HT) using Darwinian Particle Swarm Optimization (DPSO) for feature selection. The experiment is performed in NSL_KDD dataset the important features are obtained using DPSO and the classification is performed using proposed SAE-HT technique. The proposed technique achieves a higher accuracy of 97.7% when compared with all the other state-of-art techniques. It is observed that the proposed technique increases the accuracy and detection rate thus reducing the false alarm rate.
网络安全专家估计，网络攻击造成的损失将大幅增加。网络的大规模应用引发了人们对如何安全传递电子信息的担忧。通常，入侵者会尝试不同的攻击来获取敏感信息。入侵检测系统（IDS）在识别组织中的数据和用户偏差方面起着至关重要的作用。在本文中，将流数据挖掘与 IDS 相结合，以完成一项特定任务。该任务是成功地在更短的时间内区分重要的、被掩盖的信息。该实验专注于使用达尔文粒子群优化 (DPSO) 改进使用提出的堆叠自动编码器 Hoeffding 树方法 (SAE-HT) 的 IDS 的有效性，用于特征选择。实验在 NSL_KDD 数据集上进行，使用 DPSO 获得重要特征，并使用提出的 SAE-HT 技术进行分类。与所有其他最先进的技术相比，所提出的技术的准确率达到了 97.7%。结果表明，该技术提高了准确率和检测率，从而降低了误报率。
