Development a data mining techniques to detect and prevention cyber attack for cybersecurity

Abstract

The identification and forecasting of cyber-attacks is crucial process. In this article, we describe a paradigm for cyber security that makes use of data mining to forecast cyberattacks and identify appropriate countermeasures. The framework’s two primary elements are the surveillance and prevention of cyberattacks. The system constructs a predictive model to forecast future cyberattacks after first extracting appropriate timing with cyberattacks from previous data that used a decision tree based on the J48 algorithm. A variety of cyber-attacks, involving DDoS, port scans, and SQL Injection, are described in the datasets. The suggested framework effectively recognizes cyberattacks and gives patterns associated with them. The suggested predictive algorithm for identifying cyberattacks has a 99% average prediction performance. The predictions model’s test outcomes demonstrate how effective it is at spotting potential cyberattacks in the future. Moreover, solutions like malware detection and monitoring were provided using data mining. Given the state of computer networks today Users of computer networks ought to take security very seriously. In this article, implications of data mining for risk evaluation and identification were highlighted, along with a unique method for quickly and accurately detecting malware.