Leveraging Artificial Intelligence and Machine Learning for Anomaly Detection in Zero Trust Network Environments: A Comprehensive Exploration of Algorithm Selection and Performance Evaluation
Keywords:
Zero Trust Network Architecture, Anomaly Detection, Machine Learning, Supervised Learning, Reinforcement LearningAbstract
Modern cybersecurity confronts advanced cyberattacks. New threats make perimeter-based security worthless. The potential Zero Trust Network Architecture (ZTNA) emphasizes "never trust, always verify" for access control. ZTNA works by detecting network anomalies. This research suggests AI and ML may improve ZTNA anomaly detection.
First, we design ZTNA anomaly detection ML algorithms theoretically. This research uses Supervised Learning to train algorithms using pre-labeled datasets of usual and atypical network traffic patterns. Using labeled data patterns, SVMs and Random Forests may categorize new network activity as normal or abnormal. ZTNAs with rapidly increasing attack routes may have trouble labeling data.
If data is less labeled, unsupervised learning performs best. These algorithms anticipate behavior from unlabeled network traffic. Baselines reveal abnormalities. Abnormality detection methods include PCA and K-Means. Unsupervised learning may overlook malicious outliers without pre-labeled data.
ZTNA anomaly detection is investigated in RL. RL algorithms use incentives and penalties for trial-and-error learning. ZTNA RL agents may monitor network traffic and prohibit questionable connections depending on security system input. RL can adapt dynamically, but training is computationally costly and setup-dependent.
Test any deployed ML algorithm for efficacy. This article criticizes ZTNA anomaly detection metrics. Precision and recall, which quantify the ratio of properly identified anomalies to real abnormalities, are essential for measuring the system's capacity to distinguish normal and abnormal behaviors. A harmonic mean of Precision and Recall, F1-score, displays balanced system performance. DR and FAR measure the system's anomalous sensitivity and false positive rate.
This research helps choose and evaluate ZTNA anomaly detection methods by studying ML algorithms and performance measures. This technique lets security professionals use AI and ML appropriately to secure networks.
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