Mirai Botnet Attacks on IoT Applications: Challenges and Controls

Abstract

IoT is rapidly developing technology to enhance the quality of human life with embedded technologies. IoT can control and access daily usable devices and equipment with an internet connection. Smart technology provides a connected infrastructure to heterogeneous devices like IP cameras, cell phones, cars, home appliances, and industrial equipment for autonomous communication and interaction. The great perspective of IoT infrastructure comes with more security challenges. The multiplication of IoT gadgets it can be more easily negotiated than personal computers has led to intensification in the IoT-based botnet attacks. These IoT devices need to ensure the security and privacy of sensitive information and network communication. In the public channel, an adversary can damage the transferred information for unauthorized activities on applications. To moderate this hazard, there is a necessity for new procedures that diagnose the threats dispatched from exchanged IoT appliances and that are dispersed amongst all IoT-based attacks. We discuss the bio-inspired-based attack discovery techniques for IoT botnet attacks and network traffic from hacked IoT gadgets. This paper aims to review the existing attack detection approaches that have been used to address the security issues on IoT applications. In this work, bio-inspired computing models were independently trained to detect and mitigate the Mirai botnet attacks on IoT applications. The bio-inspired computing framework shows the high accuracy and high detection rate over the IoT environment. And also we are exploring details of the bio-inspired models for improving security measures in different scenarios on smart technology.