An Enhanced Machine Larning based Threat Hunter An Intelligent Network Intrusion Detection System

Sayed Salah Ahmed Hasan, H. Mohamed, Ayman M. Bahaa-Eldin
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Abstract

In network security, there are many applications and techniques that can be used to maintain targets of high-level security such as confidentiality, integrity, availability, and nonrepudiation for safe communication between different sources. This can be done by supporting networks with security systems to thwart any chances of exploitations by any attacker. Intrusion Detection System (IDS) is one of the major systems as it is capable of monitoring all network traffics (ingoing and outgoing) and performs some analysis and inspection to evaluate the behavior of such traffics. IDS can block all suspicious activities that are trying to breach any network based on policies that are demanded by a system administrator. Traditional IDS has some limits and does not provide a complete solution for some kind of problems. IDS searches for potential abnormal activities on the network traffic and sometimes succeeds to find some vulnerability which may result in compromising the network. We, therefore, suggest an efficient application in this paper of Machine Learning (ML) based IDS.
基于增强机器学习的威胁猎人智能网络入侵检测系统
在网络安全中,有许多应用程序和技术可用于维护高级别安全性目标,例如机密性、完整性、可用性和不可否认性,以实现不同来源之间的安全通信。这可以通过支持带有安全系统的网络来阻止任何攻击者利用的机会来实现。入侵检测系统(Intrusion Detection System, IDS)是主要的网络检测系统之一,它能够监控所有的网络流量(进出),并对这些流量进行分析和检测,以评估这些流量的行为。IDS可以根据系统管理员要求的策略阻止试图破坏任何网络的所有可疑活动。传统的IDS有一些限制,并且不能为某些问题提供完整的解决方案。IDS在网络流量中搜索潜在的异常活动,有时会成功发现一些可能导致网络危害的漏洞。因此,我们在本文中提出了基于机器学习(ML)的IDS的有效应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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