What type of study is this?

September 5, 2025Open Access

A Survey on An Anomaly Detection Improvement in Computer Communication Network using ML Techniques

Key Points

The improved system identifies a variety of attacks like Denial-of-Service and probe attacks in real-time, enhancing security.
Anomaly detection through machine learning techniques shows effective classification from preprocessed datasets like NSL-KDD.
Supervised machine learning models such as Random Forest and KNN are employed for optimal detection performance in network traffic.
The project addresses the limitations of traditional intrusion detection systems by applying machine learning for flexible solutions.

Abstract

In today's digital age, guarding network structure from cyber pitfalls is a major concern. Traditional hand-grounded Intrusion Detection Systems (IDS) often struggle to identify new or unknown attacks. This creates a need for smarter and more flexible results. This project introduces an anomaly-grounded Network Intrusion Detection System (NIDS) that uses ML approaches to spot unusual functioning in network traffic. The system analyzes patterns and differences from typical activity to detect implicit intrusions, including Denial-of-Service (DoS) attacks, probe attacks, User to Root (U2R) exploits, and Remote to Local (R2L) breaches in real-time. The system works with preprocessed network datasets like NSL-KDD or CICIDS. It extracts important features and trains supervised machine learning models such as Random Forest, Support Vector Machine, and KNearest Neighbors (KNN) for accurate classification.

Read Full Paperexternally

Demander à l'IA

Bookmark

View Full Paper