Cyber-Attack Detection Using Optimized Ensemble Classification Model - Dr. Kalaivani Kathirvelu, Yasir A. (Mathematical Statistician and Engineering Applications, 2022)
Cyberattacks may have far-reaching effects despite their low cost, simplicity of execution, and general inability to be traced back to a specific source. One of the main factors making it hard to pinpoint blame for cyberattacks is the existing state of network infrastructure. Cyber-attacks are difficult to punish because of a lack of enforcement measures in international law, even when the perpetrators have been identified. Since it is challenging to pursue cyberattacks, attribution is not an effective deterrence. For this reason, it may be possible to use social media data to shed light on the causes of cyber-attacks, given that the internet is a freely accessible resource. In this paper a model is developed to detect the DDoS attack. An ensemble classification model is developed to identify the total number of entries and approves the functional access of various devices inside the network. The simulation is conducted to test the efficacy of the model in detecting the attack and the simulation shows that the proposed method achieves higher degree of accuracy than other methods.