Abstract :

The Rapid Expansion Of Network-based Applications And Internet Services Has Significantly Increased The Risk Of Cyber-attacks Such As Denial-ofservice Attacks, Probing, Malware Intrusions, And Unauthorized Access. Conventional Intrusion Detection Systems Rely On Predefined Rules And Signatures, Making Them Ineffective Against Novel And Evolving Attack Patterns. To Address These Challenges, This Project Presents An Artificial Intelligence-based Approach For Detecting Anomalous Network Traffic .The Proposed System Employs Machine Learning Techniques To Learn Normal Network Behavior And Identify Deviations That Indicate Potential Security Threats. Network Traffic Data Is Collected From Standard Intrusion Detection Datasets And Undergoes Preprocessing, Feature Extraction, And Normalization To Improve Detection Efficiency

Published:

31-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1478 - 1483

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite