A Review on Network Intrusion Detection System Using Machine Learning

IF 1.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Innovative Computing Information and Control Pub Date : 2020-05-20 DOI:10.11113/ijic.v10n1.252
B. Kagara, M. Md. Siraj
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引用次数: 4

Abstract

The quality or state of being secure is the crucial concern of our daily life usage of any network. However, with the rapid breakthrough in network technology, attacks are becoming more trailblazing than defenses. It is a daunting task to design an effective and reliable intrusion detection system (IDS), while maintaining minimal complexity. The concept of machine learning is considered an important method used in intrusion detection systems to detect irregular network traffic activities. The use of machine learning is the current trend in developing IDS in order to mitigate false positives (FP) and False Negatives (FN) in the anomalous IDS. This paper targets to present a holistic approach to intrusion detection system and the popular machine learning techniques applied on IDS systems, bearing In mind the need to help research scholars in this continuous burgeoning field of Intrusion detection (ID).
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基于机器学习的网络入侵检测系统综述
安全的质量或状态是我们日常生活中使用任何网络的关键问题。然而,随着网络技术的飞速发展,攻击比防御更具开拓性。设计一个有效可靠的入侵检测系统是一项艰巨的任务,同时保持最小的复杂性。机器学习的概念被认为是入侵检测系统中检测不规则网络流量活动的重要方法。使用机器学习是开发IDS的当前趋势,以减轻异常IDS中的假阳性(FP)和假阴性(FN)。本文旨在介绍入侵检测系统的整体方法和应用于入侵检测系统的流行机器学习技术,同时考虑到需要帮助研究学者在这个不断发展的入侵检测(ID)领域。
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来源期刊
CiteScore
3.20
自引率
20.00%
发文量
0
审稿时长
4.3 months
期刊介绍: The primary aim of the International Journal of Innovative Computing, Information and Control (IJICIC) is to publish high-quality papers of new developments and trends, novel techniques and approaches, innovative methodologies and technologies on the theory and applications of intelligent systems, information and control. The IJICIC is a peer-reviewed English language journal and is published bimonthly
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