AI Based Cyber Threat Detection and Intelligent Security Assistant

Cybersecurity is becoming more Challenging due to the wider use of the internet, IoT devices, mobile applications cloud computing and interconnected computer networks has increased the complexity and high frequency of cybersecurity threats. Common attacks such as malware, phishing, Denial-of-Service (DoS), brute-force attacks, port scanning, botnets and unauthorized access are becoming harder to detect using traditional security methods that mainly depend on fixed rules and known attack signatures. To address these challenges, this research proposes an AI -based Cyber Threat Detection and Intelligent System for analysing cybersecurity activities. The Proposed system uses machine learning techniques to examine security related data and identify whether the observed activity is normal or potentially harmful. Along with thread detection, the system also considers unusual pattern so that activities which do not clearly match known attack categories can be identified for future analysis After identifying the threat, the system generates an alert and suggests suitable security measures so that the user can understand the problem and respond accordingly. The performance of the detection models can be measured using accuracy, recall, precision, F1-score and confusion matrix. By combining threat detection, anomaly identification, threat classification and risk assessment, the approach provides a single framework for analysing suspicious activities. It can also reduce the need for continuous manual analysis and provide users with useful information about potential security issues. The system can help users understand suspicious activities at an early stage and get a clearer idea about the detected security issue. It provides information about the type of threat and suitable security measures based on generated alerts. In future, this approach can be improved for real-time monitoring and handling of new types of cyber threats.