The intelligent optimization of AI/ML framework to Detect Network Abnormalities via Time Series Analysis

This paper presents an intelligent optimization of the AI/ML framework to detect network anomalies via time collection analysis. The proposed framework entails leveraging an $\mathrm{AI} / \mathrm{ML}$ version to manner the time series facts and to identify community anomalies. Traditional methods of describing the information through time variety or histogram analysis are then improved by introducing a supervised schooling set of rules for higher accuracy. The methodology is similarly validated using a real-global dataset. The result of the preliminary analysis famous that the $\mathrm{AI} / \mathrm{ML}$ model trained with the proposed framework enables efficient detection of extraordinary network sports. The findings of this examination underscore the benefits of using AI/ML models for the timely detection of community anomalies. The painting affords an intelligent optimization of AI/ML frameworks to locate community abnormalities via time collection evaluation. The proposed framework is evolved through leveraging a hard and fast of features, consisting of topology, via graph-primarily based strategies, an excellent way to seize the worldwide behavior of the community and trigger alarms in case of ability network anomalies. The time series evaluation is then achieved to extract and model features from the monitored traffic information to discover patterns and insights, also used in labeling and predicting a suitable elegance for each traffic file. In the end, the AI/ML model skilled the use of the categorized statistics to locate any ability community abnormalities and cause alarms to inform of such unusual behavior. The proposed framework, as a result, presents an automated solution for network anomaly detection and acts as a powerful tool in mitigating security threats for businesses and companies that rely heavily on their community infrastructure…