Development a Data Mining Techniques to Detect and Prevention Cyber Attack for Cybersecurity

Abstract

The frequency of cyberattacks is rising dramatically every day as a result of technological improvements. For every firm that handles sensitive data for commercial objectives, the identification and forecasting of cyber-attacks is crucial. 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. The Canadian Institute of Cybersecurity's accessible to the public cyber security datasets are then used to apply the methodology. 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. Future cyberattacks can be predicted using the extracted patterns from the estimation method on past data. 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 are provided using data mining. Given the state of computer networks today Users of computer networks ought to take security very seriously. Web mining technique is a solid option among them. Programs for data mining may be employed to analyze behaviour of the system, surfing patterns, and other factors to identify potential cyberattacks in the future. By observing unusual system activity, behaviours, and indicators, data mining tools offer an intelligent way for attack detection. In this study, implications of data mining for risk evaluation and identification are highlighted, along with a unique method for quickly and accurately detecting malware. Appropriate precautions must be taken in order to avoid problems. From the point of thinking, we must prioritize data security consciousness.