LOF-Based Anomaly Detection Approach for Mitigating DOS Attacks

Abstract

This research was conducted using the Kali Linux operating system, which is a specialized platform in cyber security and penetration testing due to its powerful tools for network analysis and vulnerability detection. Open-source tools such as Wireshark and T Shark were employed to capture and analyze network traffic, enabling the researcher to study network data flows in detail and with precision. After data collection, the Local Outlier Factor (LOF) algorithm was implemented using Python and the scikit-learn library- one of the most prominent machine learning libraries for data analysis and anomaly detection. The study also referred to a set of modern academic sources and scientific papers discussing cybersecurity and data analysis, including works that examined the effectiveness of the LOF algorithm in detecting abnormal network activities.