The pervasive issue of cheating in online video games poses a significant threat to fair competition, player engagement, and the integrity of eSports. Traditional anti-cheating solutions, often reliant on static detection techniques and elevated privileges (e.g. kernel-level access), suffer from limitations in adaptability, intrusiveness, and resistance to evolving threats. This thesis introduces an innovative approach to cheat detection in online video games, based on player’s behavioural analysis for anomaly identification. Leveraging machine learning (ML) and Vision-Language Models (VLMs), the proposed system is designed to be game-agnostic and player-specific, capable of identifying anomalous behaviours in real-time ensuring applicability across different game categories.