Review of Detection Techniques for Internal Security Threats in Local Area Networks

Insider threats in Local Area Networks (LANs) is a significant cybersecurity challenge. Insiders have valid access, complicating the identification of malicious actions. This paper systematically reviews identification techniques for LAN security threats, referencing 26 scholarly sources covering various aspects of insider threats and detection systems. The review explores diverse detection methodologies, including supervised classification and deep learning-based approaches. It integrates behavioral and organizational research with technical detection to contextualize insider threats. Research gaps are identified, such as a lack of real-world datasets and unified detection frameworks. A comparative analysis is conducted to assess methodologies, threat categories, and detection performance. Findings reveal that while ML methods excel in benchmarks, real-world application poses significant challenges.