Project Abstract

Abstract
With the continuous growth and complexity of computer networks, the detection of anomalies in network traffic has become a critical task for ensuring network security and performance. In this research study, we focused on leveraging machine learning algorithms for anomaly detection in network traffic. The primary objective of this research was to develop and evaluate a machine learning-based approach for accurately identifying and classifying anomalies in network traffic. The research methodology involved collecting a large dataset of network traffic data, pre-processing the data to extract relevant features, and training various machine learning models on the dataset. The study utilized supervised learning techniques such as decision trees, support vector machines, neural networks, and ensemble methods to build and evaluate the anomaly detection models. Furthermore, unsupervised learning algorithms like k-means clustering and isolation forests were also employed to detect anomalies in an unsupervised manner. The literature review conducted in this research covered various existing approaches and methodologies for anomaly detection in network traffic. The review highlighted the importance of machine learning techniques in effectively identifying anomalies in network traffic and discussed the advantages and limitations of different algorithms. The findings from the research revealed that machine learning algorithms could effectively detect anomalies in network traffic with high accuracy. The study showed that ensemble methods such as random forests and gradient boosting performed exceptionally well in classifying different types of anomalies. Additionally, the research demonstrated the effectiveness of unsupervised learning algorithms in detecting unknown anomalies and outliers in network traffic. The discussion of findings in this research delved into the performance comparison of different machine learning algorithms, the impact of feature selection on anomaly detection accuracy, and the trade-offs between false positives and false negatives in anomaly detection. The study also explored the scalability and efficiency of the proposed anomaly detection models in large-scale network environments. In conclusion, this research contributes to the field of network security by providing a comprehensive analysis of machine learning-based anomaly detection in network traffic. The findings demonstrate the effectiveness of machine learning algorithms in detecting and classifying anomalies in real-time network traffic. The research highlights the potential of machine learning techniques to enhance network security and performance by accurately identifying and mitigating network anomalies.

Project Overview