Project Abstract

Abstract
Anomaly detection in network traffic is a vital aspect of ensuring the security and integrity of computer networks. With the increasing complexity and volume of network data, traditional rule-based methods are often insufficient to detect subtle deviations that may indicate malicious activities. Machine learning algorithms have emerged as powerful tools for anomaly detection due to their ability to adapt to changing patterns in data. This research focuses on the application of machine learning algorithms for anomaly detection in network traffic. The primary objective of this study is to develop and evaluate machine learning models for detecting anomalies in network traffic. The research begins with a comprehensive review of the existing literature on anomaly detection, network traffic analysis, and machine learning algorithms. The literature review aims to identify gaps in current research and provide a foundation for the methodology adopted in this study. The research methodology involves collecting a dataset of network traffic data, preprocessing the data to extract relevant features, and training machine learning models for anomaly detection. Various machine learning algorithms, including supervised and unsupervised techniques, will be explored and compared in terms of their performance in detecting anomalies in network traffic. The findings of this research will be presented and discussed in Chapter Four, providing insights into the effectiveness of different machine learning algorithms for anomaly detection in network traffic. The discussion will include a comparison of the performance of various algorithms, highlighting their strengths and limitations in detecting different types of anomalies. In conclusion, this research contributes to the field of network security by demonstrating the potential of machine learning algorithms for improving anomaly detection in network traffic. The findings of this study have implications for enhancing the security posture of organizations and improving the detection of malicious activities in computer networks. Overall, this research serves as a valuable resource for network security professionals, researchers, and practitioners interested in leveraging machine learning algorithms for anomaly detection in network traffic. The insights gained from this study can inform the development of more effective and efficient approaches to securing computer networks and detecting anomalous behavior.

Project Overview