Optimised Ensemble Classification Model for Detecting Cyber Attacks

Abstract

The repercussions of cyberattacks may be far-reaching, despite the minimal costs involved, the ease with which they can be executed, and the difficulty in pinpointing their origin. Current network architecture is one of the main obstacles that makes pinpointing the perpetrators of cyberattacks difficult. The absence of enforcement tools under international law makes it impossible to prosecute those guilty for cyberattacks, even when they may be identified. This complicates the process of punishing individuals who launch cyberattacks. Attribution is not a reliable deterrent since it is difficult to prosecute criminal charges in conjunction with cyberattacks. Due to the accessibility of the internet, it is feasible that examining data from social media can help us better understand what motivates hackers. We construct a model to identify the origin of the DDoS assault as part of our inquiry. The number of devices and their individual rights to access a network’s features are both determined by an ensemble classification model. To ensure that the model can detect the attack, a simulation is run.