AI Based Cyber Threats and Vulnerability Detection, Prevention and Prediction System

The high-paced growth of Internet of Things (IoT), digital infrastructure, and cloud computing technologies has

considerably enhanced the level and rate of cyber-attacks. Traditional security technologies (e.g., signature-based Intrusion

Detection Systems (IDS) and firewalls) are not adequate to identify zero-day exploits, polymorphic malware, and advanced

persistent threats because they are based on known attack patterns. In order to overcome these constraints, this paper presents a

proposal of the AI-Based Cyber Attack Detection and Prevention System that applies to the intelligent detection of threats and

automated prevention through the application of the techniques of Machine Learning (ML) and Deep Learning (DL). The

suggested framework incorporates supervised learning such as the random forest and Support Vector Machine (SVM) and

Artificial Neural Networks (ANN) in detecting anomalies and classifying traffic. The system examines network flow

characteristics and behaviour patterns in order to detect maliciousness like DDoS, phishing, malware injection, and attempted

unauthorized access. There is an automated prevention module which prevents suspicious IP addresses and isolates

compromised nodes on the fly. The benchmark evaluation of experimental evaluation based on the cybersecurity datasets shows

high accuracy of detection, low rate of false positives and reduced response time as compared to the conventional rule-based

systems. The findings confirm the usefulness of artificial intelligence to construct dynamic, scalable and proactive cybersecurity

protection systems that can be used by contemporary network systems