Journal of Information Security Research Xiao Lijun 17 1 2026 https://doi.org/10.6025/jisr/2026/17/1/23-36 https://www.dline.info/jisr/fulltext/v17n1/jisrv17n1_2.pdf This paper outlines a probabilistic intrusion detection and computer forensics framework tailored for cloud and networked environments. It addresses the limitations of traditional intrusion detection systems (IDSs), particularly the inefficacy of signature based methods against evolving threats such as polymorphic malware and advanced persistent threats (APTs). The proposed approach integrates Bayesian networks with an Improved Synthetic Weighted Naïve Bayes (ISWNB) classifier, which enhances detection accuracy by incorporating attribute correlation and value sensitive weighting thereby relaxing the strict independence assumption of standard Naïve Bayes models. The methodology includes Bayesian fuzzy clustering for grouping cloud nodes based on behavioral similarity, followed by a two level classification using a Group Search- Gravitational optimized Support Vector Neural Network (GG-SVNN), as introduced in prior work by Tummalapalli (2021). Experimental evaluation was conducted on the KDD Cup 1999 dataset, with preprocessing steps including feature selection and discretization. Results show that ISWNB achieves an average classification accuracy of 98.76%, outperforming baseline models such as J48, standard Naïve Bayes, and other weighted variants. The system demonstrates strong performance against common attack types (DoS, Probe), but faces challenges with minority classes such as R2L and U2R due to dataset imbalance. Computational efficiency remains high, enabling near real time forensic analysis. The framework also emphasizes forensic integrity, aligning with standardized digital evidence handling procedures. Future work includes validation on modern datasets (e.g., NSL-KDD, CICIDS), integration with deep or ensemble learning, and incorporation of explainable AI (XAI) techniques to enhance interpretability in forensic investigations. Overall, the study advances scalable, accurate, and interpretable intrusion detection for dynamic cloud environments.