利用鸽子启发优化算法中的整流线性单元函数增强入侵检测系统

Agus Tedyyana, Osman Ghazali, Onno W. Purbo, M. A. A. Seman
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引用次数: 0

摘要

数字世界中的网络犯罪率不断上升,这凸显了拥有一个可靠的入侵检测系统(IDS)来检测未经授权的攻击并通知管理员的重要性。IDS 可以利用机器学习技术来识别攻击模式并提供实时通知。在构建成功的 IDS 时,选择正确的特征至关重要,因为它决定了模型预测的准确性。本文提出了一种新的 IDS 算法,该算法在特征选择中结合了整流线性单元(ReLU)激活函数和鸽子启发优化器。在网络安全层--数据库知识发现(NSL-KDD)数据集上对所提出的算法进行了评估,结果表明,与以前的 IDS 模型相比,该算法在训练速度和准确性方面都有了很大提高。因此,在特征选择中使用 ReLU 激活函数和鸽子启发优化器可以显著提高 IDS 检测未经授权攻击的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing intrusion detection system using rectified linear unit function in pigeon inspired optimization algorithm
The increasing rate of cybercrime in the digital world highlights the importance of having a reliable intrusion detection system (IDS) to detect unauthorized attacks and notify administrators. IDS can leverage machine learning techniques to identify patterns of attacks and provide real-time notifications. In building a successful IDS, selecting the right features is crucial as it determines the accuracy of the predictions made by the model. This paper presents a new IDS algorithm that combines the rectified linear unit (ReLU) activation function with a pigeon-inspired optimizer in feature selection. The proposed algorithm was evaluated on network security layer - knowledge discovery in databases (NSL-KDD) datasets and demonstrated improved performance in terms of training speed and accuracy compared to previous IDS models. Thus, the use of the ReLU activation function and a pigeon-inspired optimizer in feature selection can significantly enhance the effectiveness of an IDS in detecting unauthorized attacks.
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