Detecting Cyber-Attacks with Intrusion Detection Systems - Exploring Machine Learning Approaches

Aug 4, 2026

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Rising internet usage has coincided with a surge in cyber-attacks, including emerging and novel threats that require stronger detection capabilities. Intrusion Detection Systems are essential for monitoring network traffic and identifying malicious activity, while ML-based models use large datasets to distinguish normal from anomalous patterns. High-dimensional data creates the curse of dimensionality, so feature selection and dimensionality reduction are adopted to improve classification. The work surveys supervised machine learning approaches for IDS, reviewing core IDS concepts, ML methodologies, and algorithm suitability across IDS datasets.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Cybersecurity and IDS basics

Role of machine learning in IDS

Types of Intrusion Detection System

Network-based

Host-based

Signature-based

Anomaly-based

Hybrid

Protocol-based

Application-based

Using Machine Learning for Intrusion Detection Systems

Data-driven detection of known and zero-day threats

Detecting Cyber-Attacks with Intrusion Detection Systems Exploring Machine Learning Approaches SEEJPH Volume XXVI, S2,2025, ISSN: 2197-5248; Posted:03-02-25 Detecting Cyber-Attacks with Intrusion Detection Systems Exploring Machine Learning Approaches Priya Agrawal1, Hemant Pal2, Ritesh Joshi3, Priyanka Jain4 1Research Scholar, Department of Computer Science, Medi-Caps University, Indore, MP, India 2Assistant Professor, Department of Computer Science, Medi-Caps University, Indore, MP, India 3Assistant Professor, Department of Computer Applications, Medi-Caps University, Indore, MP, India 4Assistant Professor, Department of Computer Science, Medi-Caps University, Indore, MP, India KEYWORDS Intrusion detection system, Machine learning. ABSTRACT increase in internet usage has been paralleled by a surge in cyber-attacks, many of which are novel and necessitate advanced detection mechanisms. Intrusion Detection Systems amuse oneself is critical bit part within keep track of network congestion which identify malicious activity. The detection of emerging threats requires the development of models using vast amounts of data to effectively distinguish between normal and anomalous traffic patterns. This has led to the growing appeal of machine learning algorithms that intensify predictive rightness of Intrusion Detection Systems. However, the high dimensionality of data poses significant challenges, particularly the “curse of dimensionality,” which can degrade classification performance. This issue has prompted the adoption of featureselection and dimensionality reduction techniques to improve classification outcomes. In response to the increasing undivided attention about that application at supervised ML for Intrusion Detection Systems, this wrapper attending a sweeping look over of supervised learning algorithms along with their effectiveness in intrusion detection system. We review the core concepts of IDS, machine learning methodologies, and dimensionality reduction approaches. Additionally, we provide a detailed taxonomy that outlines the suitability of various algorithms for different IDS datasets, with a focus on the impact of feature selection on enhancing classification performance. Introduction: Cybersecurity exist a heterogeneous branch of knowledge dedicated to sheltering digital skeletons, tactful data together with passing on channels from spiteful pursuits, unaccredited access and systemic in perils. It skirts a gamut of dominions, embrace network matrix, cryptographic treaties, prevalence reciprocation, act toward intelligence, and security diminution. Within an aeon at cyber threats, out of ultra-modern persistent threats onto ground zero exploits, cybersecurity amalgamates inventive mechanisms like as artifice machine learning together with blockchain onto embattle digital biodiversity. In cybersecurity an Intrusion Detection System is apparatus which motif to keep track of and scrutinize network congestion conversely system ventures to pick out spiteful ventures or proposed action infringements. One time located the Intrusion Detection System give rise to vigilantes that notify structure controllers to those potential threatening remarks. Which is a censorious unit of contemporary network shielding strategies. Detecting Cyber-Attacks with Intrusion Detection Systems Exploring Machine Learning Approaches SEEJPH Volume XXVI, S2,2025, ISSN: 2197-5248; Posted:03-02-25 1. Types of Intrusion Detection System: | Type of Intrusion Detection System | Network based Intrusion
Detection System | Host based Intrusion Detection System | Signature based Intrusion
Detection System | Anomaly based Intrusion
Detection System | Hybrid Intrusion
Detection System | Protocol based Intrusion
Detection System | Application Based Intrusion Detection System | | --- | --- | --- | --- | --- | --- | --- | --- | | Description | Scrutinize network congestion
for unsure
ventures or motifs. | Scrutin

FAQs

Why are Intrusion Detection Systems necessary in cybersecurity?

Intrusion Detection Systems monitor and scrutinize network congestion to identify malicious activities and notify system controllers about potential threats.

How does machine learning improve intrusion detection compared with traditional methods?

Machine learning approaches learn patterns and data-driven models to detect potential intrusions, rather than relying only on fixed rules or signatures.

What problem does high-dimensional data create, and how is it addressed?

High dimensionality leads to the curse of dimensionality, which can degrade classification performance. Feature selection and dimensionality reduction techniques are used to improve outcomes.

Aug 4, 2026

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