Developing Anomaly-based Network Intrusion Detection Model Using Machine Learning Techniques
Year: 2025
Type: article
Abstract: s The rapid growth of information technology in recent years has brought countless benefits, but it has also led to a significant rise in cyberattacks and other malicious activities. As software and hardware technologies continue to advance, the volume and complexity of these threats are escalating at an alarming pace. With more users depending on the internet for daily activities, accurate network traffic classification has become a critical component of cybersecurity. Every day, new attack methods are developed by individuals and organizations seeking to exploit vulnerabilities and steal sensitive information. While numerous studies have been conducted in this field, many rely on outdated datasets, which can limit their effectiveness. Using outdated data often results in higher false alarm rates and lower detection accuracy, making it harder to keep up with today’s evolving threat landscape. This thesis presents a frame work on network intrusion detection model using four ensemble classifiers including Random Forest, XGBoost, AdaBoost, and a Voting Classifier. To train and test our models we use the CICIDS2017 dataset, which captures realistic network traffic and a wide range of cyberattack types. The data underwent thorough deep preprocessing, including data cleaning, normalization, and feature selection, to ensure optimal model performance. The preprocessed data has 40 attribute and 250798 records. We have applied experimental approach in our work and the experiment was done on using python programming language, which have libraries like pundas,numpy,matplotlib,seaborn and skti-learn that make it suitable to implement our models on using anaconda environment. The experiment was done for multi class and binary class with 70/30% train test ratio, among the four models on both multi class and binary class the Voting classifier significantly improves detection accuracy and result in superior performance with 99.92% accuracy, 99.67% precision, 99.80% recall, and 99.73% F1-scores compared to individual models. Lastly, the research findings indicate that ensemble learning demonstrates superior performance in anomaly-based NIDS systems which provide an effective tool for proactive network security against evolving threats.
DOI: 10.20372/nadre:9114
Author: AHMED OUSMAN
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6907172154
FWCI:
Cites:
Cited by:
Related to: