Anomaly Detection for Network Security

Abstract

Network security is increasingly challenged by sophisticated cyber threats, necessitating advanced methods for anomaly detection. In this project, we developed an anomaly detection application specifically designed for cybersecurity datasets. Our contribution includes a Python-based application that integrates both supervised and unsupervised anomaly detection techniques, leveraging statistical, clustering, and machine learning approaches. The application is capable of analyzing both pre-existing and synthetic datasets, providing comprehensive anomaly detection and actionable insights for enhancing cyber defenses. We evaluated the application through a detailed case study in network security, applying it to real-world scenarios. The results demonstrate the effectiveness of our application in identifying anomalies and potential threats within network traffic. The flexibility in selecting various anomaly detection methods ensures adaptability to diverse cybersecurity datasets, underscoring the practical relevance and robustness of our approach. Keywords: anomaly detection, network security, cybersecurity, supervised learning, unsupervised learning, machine learning, statistical methods, clustering, Python, synthetic datasets, cyber threats, data analysis.