利用集合机器学习进行入侵检测

Ms. Nikita Kotangale, Dr.Shrikant Sonekar, D. S. S. Sawwashere, Prof. Mirza Moiz Baig
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引用次数: 0

摘要

如今,入侵检测系统对于抵御计算机网络的敌对活动至关重要。随着现代网络威胁的复杂性和多样性不断增加,传统的基于单一分类器的入侵检测系统往往难以达到最佳的检测性能。为了应对这一挑战,本研究提出了一种使用集合机器学习的入侵检测系统。该方法在一个集合框架中结合了多种机器学习算法的优势,以提高入侵检测的准确性、鲁棒性和效率。该系统包含数据预处理、特征选择、集合模型构建和模型性能等步骤。系统采用了数据平衡、属性编码和基于相关性的特征选择等技术来优化 IDS 性能。集合模型得益于多个分类器的集体智慧和多样化决策,提高了系统准确识别和应对网络入侵的能力。通过全面的结果分析,该研究从评估指标、特征重要性、鲁棒性和实际影响等方面验证了所提出的 IDS 的有效性。利用集合机器学习技术提出的入侵检测系统为应对动态和不断演变的网络威胁、提高计算机网络的安全性和复原力提供了一种可行的方法。关键词 - 入侵检测系统、集合机器学习、数据平衡、特征选择、网络安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intrusion Detection using Ensemble Machine Learning
Now a days intrusion detection systems are essential for defending computer networking toward hostile activity. With the increasing complexity and diversity of modern cyber threats, traditional single-classifier-based IDS approaches often struggle to achieve optimal detection performance. To address this challenge, this study proposes an Intrusion Detection System using Ensemble Machine Learning. The methodology combines the strengths of multiple machine learning algorithms in an ensemble framework to enhance the accuracy, robustness, and efficiency of intrusion detection. The system incorporates steps such as data preprocessing, feature selection, ensemble model construction, and model performance. Techniques like data balancing, attribute encoding, and feature selection based on correlation are applied to optimize the IDS performance. The ensemble model benefits from the collective intelligence and diverse decision-making of multiple classifiers, improving the system's ability to accurately identify and respond to network intrusions. Through comprehensive result analysis, the study validates the effectiveness of the proposed IDS in terms of evaluation metrics, feature importance, robustness, and real- world impact. The proposed IDS using Ensemble Machine Learning offers a promising approach to tackle the dynamic and evolving nature of cyber threats, enhancing the security and resilience of computer networks. Keywords - Intrusion Detection System, Ensemble Machine Learning, Data Balancing, Feature Selection, Cyber Security.
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