Predicting Cyber-Attacks and Identifying Perpetrators Using Machine Learning Techniques

The new generation of technology has experienced significant growth in recent years and is currently evolving. Instead, as technology advances, the number of crimes also rises. It will result in financial difficulties for the nation and its citizens. Cyberattacks account for the majority of reported attacks. It will target the privacy data of the people and the nation. As a result, it is challenging to determine what kind of attack would be employed, and the length of the inquiry would also rise. Numerous systems have been presented that use different machine-learning techniques to analyze and forecast threats. Consequently, we presented a methodology using machine learning algorithm KNN, SVM, Random Forest, and Gradient Boost Algorithm regression approach for identifying the assault. It determines the type of cyberattack that will be launched and how to foresee it. According to our research, the probability of a cyberattack declines as potential victims' income and educational levels rise. We believe that our suggested model will be helpful to law enforcement cybercrime units in identifying and stopping cyberattacks, increasing their efficacy in thwarting these threats. In conclusion, this study emphasizes how important machine learning is to improving the ability to detect cyberattacks and identify their perpetrators. By employing data-driven insights and advanced analytics, companies can enhance their capacity to anticipate, identify, and mitigate cyber threats, thereby fortifying their defenses in a constantly changing digital landscape. Going forward, sustaining a proactive posture against cyber adversaries and safeguarding the security of our global community that is interconnected depends critically on continuous research and machine learning the result accuracy and efficient has produced KNN is 84%, SVM is 94.6%, and Random Forest is 99.13%.