Botnet Threat Intelligence in IoT-Edge

Abstract

Recently, deep learning has gotten progressively popular in the domain of security. However, Traditional machine learning models are not capable to discover zero-day botnet attacks with extraordinary privacy. For this purpose, researchers have utilized deep learning based computational framework for Botnet which can detect zero-day attacks, achieve data privacy and improve training time using machine learning techniques for the IoT-edge devices. However, it combines and integrates various models and contexts. As a result, the objective of this research was to incorporate the deep learning model which controls different operation of IoT devices and reduce the training time. In deep learning, there are numerous components that aspect the false positive rate of every detected attack type. These elements are F1 score, false-positive rate, and training time; reduce the time of detection, and Accuracy. Bashlite and Mirai are two examples of zero-day botnet attacks that pose a threat to IoT edge devices. The majority of cyber-attacks are executed by malware-infected devices that are remotely controlled by attackers. This malware is often referred to as a bot or botnet, and it enables attackers to control the device and perform malicious actions, such as spamming, stealing sensitive information, and launching DDoS attacks. The model was formulated in Python libraries and subsequently tested on real life data to assess whether the integrated model performs better than its counterparts. The outcomes show that the proposed model performs in a way that is better than existing models i.e. DDL, CDL and LDL as Botnet Attacks Intelligence (BAI) the purposed deep learning model.