Abstract:
"This research report addresses the critical issue of insider threat detection in small and medium-sized enterprises (SMEs) by introducing a comprehensive framework specifically designed for this context. The problem at hand is the increasing frequency and sophistication of insider attacks, which can lead to significant financial and reputational losses for SMEs. The absence of dedicated insider threat detection systems tailored to SMEs exacerbates their vulnerability to such attacks. To tackle this challenge, the researchers developed a systematic framework for detecting insider threats within SMEs. The framework consists of four crucial stages: log collection, normalization, correlation, and detection. By analyzing log data generated from various sources within the organization, the framework identifies anomalous user behavior and potential indicators of malicious intent. Additionally, a risk scoring mechanism is implemented to prioritize incident response, enabling SMEs to allocate resources efficiently and respond promptly to the most critical threats. The evaluation conducted by a team of cybersecurity expert evaluators demonstrated promising results. The framework proved effective in identifying insider threats and streamlining the incident response process. Notably, its simplicity and ease of implementation were particularly advantageous for SMEs, allowing them to bolster their cybersecurity defenses without significant investments in complex technologies. The framework's resource-friendliness addresses the unique challenges faced by SMEs, making it a valuable and practical solution to empower these businesses in safeguarding their sensitive data and mitigating insider threats effectively.
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