Detecting Network Traffic Anomalies with Machine Learning: A Comprehensive Approach

Continued growth in network traffic as well as increased complexity in network architecture make anomaly detection critical to maintaining the security and reliability of networks. This abstract provides a detailed review of utilizing machine learning techniques in anomaly detection for network traffic. Machine learning methodologies have thus far proven promising approaches in identifying anomalies of network traffic since they can detect pattern-specific characteristics in large datasets. Different approaches to machine learning; that this abstract discusses, both supervised and unsupervised learning techniques deployed in anomaly detection. The supervised learning methods are basically those in which classifiers are trained on labeled data to classify data into normal versus anomalies in terms of traffic patterns. Instead, this technique employs unsupervised learning that finds the presence of anomalies without pre-classified data. A copy of the similar instances would be made and outliers flagged out. Another significant area that this abstract talks about is the difficulties that arise in anomaly detection in network traffic. These include the imbalanced nature of network data, evolving attack strategies, and the requirement for realtime detection. It aims to develop robust models that can identify different types of anomalies which may arise with cyberattacks like DDoS, port scanning, or even simple data exfiltration. Using LOF, SVM, KNN.