A novel deep synthesis-based insider intrusion detection (DS-IID) model for malicious insiders and AI-generated threats.

ABSTRACT: Insider threats pose a significant challenge to IT security, particularly with the rise of generative AI technologies, which can create convincing fake user profiles and mimic legitimate behaviors. Traditional intrusion detection systems struggle to differentiate between real and AI-generated activities, creating vulnerabilities in detecting malicious insiders. To address this challenge, this paper introduces a novel Deep Synthesis Insider Intrusion Detection (DS-IID) model. The model employs deep feature synthesis to automatically generate detailed user profiles from event data and utilizes binary deep learning for accurate threat identification. The DS-IID model addresses three key issues: it (i) detects malicious insiders using supervised learning, (ii) evaluates the effectiveness of ge

SUBMITTER: Kotb HM 

PROVIDER: S-EPMC11695639 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature