Majority Voting Ensemble Classifier for Detecting Keylogging Attack on Internet of Things

Abstract

An intrusion attack on the Internet of Things (IoT) is any malicious activity or unauthorized access that jeopardizes the integrity and security of IoT systems, networks, or devices. Regarding IoT, intrusions can result in severe problems, including service disruption, data theft, privacy violations, and even bodily injury. One of the intrusion attacks is a keylogging attack, sometimes referred to as keystroke logging or keyboard capture, which is a type of cyberattack in which the attacker secretly observes and records keystrokes made on a device’s keyboard. In the context of IoT, where connected objects communicate and exchange data, this assault may be especially concerning. Keylogging attacks can have severe repercussions in the IoT ecosystem since they can compromise sensitive information, including login passwords, personal information, financial information, or confidential communications. This paper explored the possibility of using an ensemble classifier to detect keylogging attacks in IoT networks. We built an ensemble classifier consisting of three classifiers: a convolutional neural network (CNN), a recurrent neural network (RNN), and a long-short memory network (LSTM). A proposed model uses the BoT-IoT dataset to detect a keylogging attack. Results show that the ensemble model can improve the model’s performance. The ensemble model had excellent accuracy and a low false positive rate. It also had significantly improved detection rates for keylogging attacks than other classifiers.

Keywords

• Convolutional neural network
• Internet of Things
• intrusion detection system
• keylogging attacks
• long short-term memory network
• recurrent neural network