Implementing machine learning in cyber security-based IoT for botnets security detection by applying recurrent variational autoencoder

Security for the Internet of Things (IoT) and Intrusion Detection Systems (IDS) have both benefited greatly from the use of Machine Learning (ML) approaches, the latest technology in cybersecurity. It looks on the viability of detecting IoT botnet security using recurrent variation auto-encoder. As no one method has proven to have the ability to handle this security danger, botnets can generate Distributed Denial of Service (DDoS) common cyber-attacks (Hackers flood a network) with requests to exhaust bandwidth. In many cases, DDoS attacks are meant to be more of a nuisance than anything else, and pose a serious security risk on IoT networks. IoT environment criteria, such as computing power and energy efficiency, are frequently not addressed by these technologies. Among the solutions for botnet security detection is variation Autoencoder. ML is the foundation of many botnet detection techniques. However, because network security risks are constantly evolving, essential regularly retrain the ML techniques with the most recent knowledge. This is especially important because categorizing data takes a lot of time and effort. Transfer learning is indicated as a more effective solution for botnet identification since it may learn from carefully prepared source data and transfer the knowledge to a target problem area. This method works whether or not the data from the target domain is labeled. In this paper, the Recurrent Variation Autoencoder (RVAE) structure is utilized to train a neural network and compute anomalous scores for target domain data records using the RVAE algorithm. Some security methods and features have more limited scope for use due to the unique IoT context attributes include limited compute and storage capacities. In this case of IoT, this study investigates the use of ML-based detection techniques and defenses. We provide a brand-new approach for detecting botnets that is based on the Recurrent Variational Autoencoder (RVAE) and efficiently captures the sequential traits of botnet abnormalities. Additionally, our test findings demonstrate that the suggested approach can identify botnets that were previously undetected by exploiting sequential network traffic patterns.