Abstract
The Internet of Things (IoT) has grown rapidly, and nowadays, it is exploited by cyber attacks on IoT devices. An accurate system to identify malicious attacks on the IoT environment has become very important for minimizing security risks on IoT devices. Botnet attacks are among the most serious and widespread attacks, and they threaten IoT devices. Motionless IoT devices have a security weakness due to lack of sufficient memory and computation results for a security platform. In addition, numerous existing systems present themselves for finding unknown patterns from IoT networks to improve security. In this study, hybrid deep learning, a convolutional neural network and long short-term memory (CNN-LSTM) algorithm, was proposed to detect botnet attacks, namely, BASHLITE and Mirai, on nine commercial IoT devices. Extensive empirical research was performed by employing a real N-BaIoT dataset extracted from a real system, including benign and malicious patterns. The experimental results exposed the superiority of the CNN-LSTM model with accuracies of 90.88% and 88.61% in detecting botnet attacks from doorbells (Danminin and Ennio brands), whereas the proposed system achieved good accuracy (88.53%) in identifying botnet attacks from thermostat devices. The accuracies of the proposed system in detecting botnet attacks from security cameras were 87.19%, 89.23%, 87.76%, and 89.64%, with respect to accuracy metrics. Overall, the CNN-LSTM model was successful in detecting botnet attacks from various IoT devices with optimal accuracy.
Highlights
Academic Editor: Abdallah Meraoumia e Internet of ings (IoT) has grown rapidly, and nowadays, it is exploited by cyber attacks on IoT devices
Extensive empirical research was performed by employing a real N-BaIoT dataset extracted from a real system, including benign and malicious patterns. e experimental results exposed the superiority of the convolutional neural network (CNN)-long short-term memory (LSTM) model with accuracies of 90.88% and 88.61% in detecting botnet attacks from doorbells (Danminin and Ennio brands), whereas the proposed system achieved good accuracy (88.53%) in identifying botnet attacks from thermostat devices. e accuracies of the proposed system in detecting botnet attacks from security cameras were 87.19%, 89.23%, 87.76%, and 89.64%, with respect to accuracy metrics
Introduction e fourth industrial revolution, as described by Klaus Schwab, was built on the great achievements of the third revolution, especially the Internet, enormous processing capacity, the ability to store information, and the unlimited potential for access to knowledge [1]. These achievements open the doors to unlimited possibilities through major breakthroughs of emerging technologies in the field of artificial intelligence, robotics, the Internet of ings, autonomous vehicles, 3D printing, nanotechnology, biotechnology, materials science, quantum computing, block chain, and others. e Internet of ings (IoT) aims to interconnect thousands of smart objects/devices in a seamless manner by sensing, processing, and analyzing large amounts of data obtained from heterogeneous IoT devices [2]. e IoT is recognized as one of the Gartner top 10 strategic technology trends in 2020, which projected that IoT will be used to develop 20 times more smart devices than conventional IT devices in 2023 [3]
Summary
Academic Editor: Abdallah Meraoumia e Internet of ings (IoT) has grown rapidly, and nowadays, it is exploited by cyber attacks on IoT devices. Hybrid deep learning, a convolutional neural network and long short-term memory (CNN-LSTM) algorithm, was proposed to detect botnet attacks, namely, BASHLITE and Mirai, on nine commercial IoT devices. Introduction e fourth industrial revolution, as described by Klaus Schwab, was built on the great achievements of the third revolution, especially the Internet, enormous processing capacity, the ability to store information, and the unlimited potential for access to knowledge [1] Today, these achievements open the doors to unlimited possibilities through major breakthroughs of emerging technologies in the field of artificial intelligence, robotics, the Internet of ings, autonomous vehicles, 3D printing, nanotechnology, biotechnology, materials science, quantum computing, block chain, and others. E intrusion detection system (IDS) is one solution for dealing with botnet attacks It uses artificial intelligence for discovering new patterns of botnet attacks. Deep recurrent neural network (DRNN) has been implemented to identify botnet attacks from IoT devices [17,18,19]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
