8Performance Analysis of Intrusion Detection System Using ML Techniques

Paridhi Pasrija, Utkarsh Singh, and Mehak Khurana

The NorthCap University, Gurugram, India

8.1 Introduction

In today’s speedily growing technological landscape, guaranteeing the safety of documents and systems has turned into a vital task. With the ever‐growing complexity of cyber threats and the escalating number of attackers, the development of robust defense mechanisms has become a top priority. This has led to the growth of Intrusion Detection Systems (IDS), which play an important role in safeguarding these digital environments. An IDS functions as a surveillance mechanism, identifying potentially malicious actions, and producing notifications upon their discovery. These notifications enable a Security Operations Center (SOC) analyst to examine the situation and implement necessary measures to address the identified risk [1]. There are three major types of IDS‐ Host‐based Intrusion Detection Systems (HIDS), Network‐based Intrusion Detection Systems (NIDS), and a hybrid of the two. HIDS focuses its attention on individual host machines within a network [2]. It monitors system logs, file activities, and host‐specific events to identify potential security breaches, such as unauthorized modifications to critical files or suspicious user behaviors. On the other hand, NIDS functions at the network level, analyzing the incoming and outgoing traffic flows. By analyzing network packets and assessing communication ...

Get Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.