Scalable AI Framework for Real-Time Anomaly Detection and Authentication Threat Management in Digital Networks

Abstract

Real-time digital infrastructures including enterprise networks, cloud platforms, Internet of Things (IoT) ecosystems, and online service environments generate massive volumes of traffic in the form of packet data, authentication logs, and system performance metrics. These continuous data streams require instant security monitoring and intelligent threat assessment. Traditional security approaches rely on manual log inspection, rule-based Intrusion Detection Systems (IDS), and static threshold mechanisms, where administrators analyze predefined signatures to detect anomalies. However, such methods are slow, laborintensive, and unable to adapt to evolving cyberattack patterns, leading to delayed threat response, high false positive rates, poor scalability, and increased risk of undetected intrusions. To address these challenges, this work proposes a robust, automated, and scalable Artificial Intelligence (AI)-powered security framework for real-time anomaly detection and authentication threat management. The system initially employs machine learning algorithms such as K-Nearest Neighbor (KNN) and Support Vector Classifier (SVC) to learn behavioral patterns and establish decision boundaries between normal and malicious activities. Despite their effectiveness, these models face limitations such as high computational cost, sensitivity to feature scaling, large memory requirements, and inefficiency in handling probabilistic uncertainty. To overcome these issues, a Naive Bayes Classifier (NBC) is introduced as the core probabilistic inference engine. NBC leverages Bayesian decision theory to estimate posterior probabilities, enabling faster training, lightweight computation, and better scalability for highdimensional data. The framework integrates data preprocessing, class balancing, multi-model training, and Flask-based web deployment for real-time inference. Performance evaluation using accuracy, precision, recall, and F1-score demonstrates reliable and efficient anomaly detection, confirming the framework’s effectiveness in modern network security.