Anomaly Detection Using Adaptive Probability Distribution Modeling

The exponential expansion of IP-based networked services across finance, healthcare, education, and government has intensified the need for effective, real-time anomaly detection mechanisms. Traditional threshold-based and machine-learning-driven systems struggle with dynamic traffic variability, high false-positive rates, and computational inefficiency. This paper proposes a Probability Distribution–Based Anomaly Detection Framework (PD-ADF) that models normal network behavior through univariate and multivariate statistical fitting using Maximum Likelihood Estimation and validated by Kolmogorov–Smirnov and Anderson–Darling tests. Anomalies are identified through adaptive confidence-interval thresholding and probabilistic scoring, enabling fast, resource-efficient detection in large-scale IP environments. Evaluations on NSL-KDD, KDD-Cup ’99, and proprietary enterprise datasets yield an accuracy of 0.96, F1-score 0.91, and false-positive rate 0.02 — surpassing Support Vector Machine and threshold baselines while requiring only 0.1 seconds per observation. The proposed model demonstrates that statistical inference can deliver ML-level precision with significantly lower complexity, offering a scalable, interpretable, and energy-efficient alternative for next-generation cyber-defense infrastructures.

Adaptive Probability Distribution Modeling, Network Anomaly Detection, Real-Time Cybersecurity Analytics, Statistical Learning for IP Networks, Dynamic Threshold Recalibration

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