International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
•
Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
Conferences Published ↓
IC-AIRCM-T3-2026
SPHERE-2025
AIMAR-2025
SVGASCA-2025
ICCE-2025
Chinai-2023
PIPRDA-2023
ICMRS'23
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 8 Issue 1
January-February 2026
Indexing Partners
Enhancing Cyber security through Machine Learning-Based Intrusion Detection and Malware Classification
| Author(s) | Mr. Manthena Ramakrishna Raju, Dr. Krishnakartik M |
|---|---|
| Country | India |
| Abstract | The rapid growth of digital infrastructure has been accompanied by an alarming increase in the frequency, scale, and sophistication of cyberattacks. Traditional signature-based and rule-based security mechanisms are often inadequate for detecting novel, zero-day, and polymorphic attacks. In this context, machine learning (ML) has emerged as a promising approach to strengthen cybersecurity defenses through intelligent, adaptive, and data-driven threat detection. This paper presents a comprehensive study on the design and development of machine learning-based intrusion detection systems (IDS) and malware classification frameworks. Supervised, unsupervised, and deep learning models are explored to improve detection accuracy, reduce false positives, and enhance adaptability to evolving attack patterns. Benchmark cybersecurity datasets such as NSL-KDD, CICIDS2017, and VirusShare are utilized for experimental evaluation. The proposed framework integrates data preprocessing, feature extraction, model training, adaptive learning, and real-time deployment. Experimental results demonstrate that ML-based approaches significantly outperform traditional methods in detecting intrusions and classifying malware, highlighting their suitability for modern cybersecurity environments. |
| Keywords | Cybersecurity, Intrusion Detection System, Malware Classification, Machine Learning, Deep Learning |
| Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
| Published In | Volume 8, Issue 1, January-February 2026 |
| Published On | 2026-01-30 |
| DOI | https://doi.org/10.36948/ijfmr.2026.v08i01.67459 |
Share this

E-ISSN 2582-2160
CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.