Botnets are a serious danger to computer network security in the current cyber era because they can infect networked computers with malware, or harmful software. Modern botnets have shifted to decentralized designs from their previous centralized structure, which makes detection and mitigation more difficult. Additionally, botnets can carry out coordinated attacks on targets, which are known as “bot group activities.” The relationship dynamics between bots within these groups known as activity correlation are frequently missed by current detection techniques. Finding the causal linkages between bot operations is crucial for figuring out how one bot's activity affects other bots during an assault. Preventing coordinated botnet attacks requires an understanding of this causality. The current research uses a mixed analytical method to present a unique model for detecting bot group activities. The process consists of analyzing activity similarities among bots, assessing their correlations, and extracting activity patterns. Publicly accessible datasets are used to evaluate the suggested model, which shows remarkable accuracy of 98.30% and a false positive rate of less than 1% in detecting bot group activity. These outcomes demonstrate how well the model addresses the drawbacks of the existing detection techniques.