Ai-powered Intelligent Framework For Detection And Prevention Of Cybersecurity Attacks Using Machine Learning Algorithms

Abstract

The Rapid Growth Of Digital Technologies Has Substantially Increased Exposure To Cybersecurity Threats Including Malware, Phishing, Ransomware, And Unauthorized Network Intrusions. Traditional Signature-based Security Systems Are Inherently Reactive And Fail Against Zero-day Exploits And Polymorphic Attacks. This Paper Presents ThreatGuardian, An AI-powered Cybersecurity Threat Detection And Prevention Framework That Leverages Machine Learning Algorithms — Random Forest (RF), AdaBoost Classifier (ADC), And Bernoulli Naive Bayes Classifier (BNC) — To Identify And Classify Malicious Network Activities In Real Time. The System Integrates Data Preprocessing, Exploratory Data Analysis, Feature Extraction, And Model Evaluation Pipelines With A Django-based Web Interface For Practical Deployment. Trained And Evaluated On A Publicly Available Network Intrusion Dataset From Kaggle, The Best-performing Model Is Serialized And Deployed For Real-time Inference. Performance Evaluation Using Accuracy, Precision, Recall, And F1-score Demonstrates That The Proposed Framework Significantly Outperforms Traditional Rule-based Methods, Providing An Adaptive, Scalable, And User-accessible Solution For Modern Cyber Threat Management.

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