Application of Machine Learning for the Detection of Secure Cyber Fraud

Identifying fraudulent transactions is of utmost importance in the cybersecurity business, since the volume of valid transactions far exceeds the fraudulent ones. There has to be a concerted effort to improve cybersecurity in a practical manner because cybercriminals are becoming smarter and more aggressive, and they're getting better at hiding their traces. Cybercrime has become more prevalent because to the COVID-19 epidemic. Every nation that falls victim to cybercrime sees a significant dent in its GDP. Hacking, phishing, online fraud, cyberbullying, cyber espionage, and most significantly, botnet-orchestrated assaults are all part of this wide-ranging category of cybercrimes. For the purpose of early botnet attack detection and identification, this paper presents a novel collaborative deep learning method based on supervised convolutional neural network (CNN) algorithms and unsupervised long short-term memory (LSTM) models. We test the suggested approach on the IoT-23 and CTU-13 datasets. With an impressively high success rate (around 98.7%) and a very low false positive rate (0.04%), the trial findings show that the suggested strategy performs better than the competition. The research helps us better comprehend cyber threat intelligence, finds new kinds of botnet assaults, and makes forensic investigation processes better.