Biomaterial Database

Intrusion Detection in IoT Using Deep Learning. 2022

Alaa Mohammed Banaamah, and Iftikhar Ahmad

Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Cybersecurity has been widely used in various applications, such as intelligent industrial systems, homes, personal devices, and cars, and has led to innovative developments that continue to face challenges in solving problems related to security methods for IoT devices. Effective security methods, such as deep learning for intrusion detection, have been introduced. Recent research has focused on improving deep learning algorithms for improved security in IoT. This research explores intrusion detection methods implemented using deep learning, compares the performance of different deep learning methods, and identifies the best method for implementing intrusion detection in IoT. This research is conducted using deep learning models based on convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs). A standard dataset for intrusion detection in IoT is considered to evaluate the proposed model. Finally, the empirical results are analyzed and compared with the existing approaches for intrusion detection in IoT. The proposed method seemed to have the highest accuracy compared to the existing methods.

UI	MeSH Term	Description	Entries
D000077321	Deep Learning	Supervised or unsupervised machine learning methods that use multiple layers of data representations generated by nonlinear transformations, instead of individual task-specific ALGORITHMS, to build and train neural network models.	Hierarchical Learning,Learning, Deep,Learning, Hierarchical
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D001336	Automobiles	A usually four-wheeled automotive vehicle designed for passenger transportation.	Cars,Automobile,Car
D016494	Computer Security	Protective measures against unauthorized access to or interference with computer operating systems, telecommunications, or accompanying data; especially the modification, deletion, destruction, or release of data in computers. It includes methods of forestalling interference by computer viruses or computer hackers aiming to compromise stored data.	Computer Viruses,Data Protection,Data Security,Compromising of Data,Computer Hackers,Computer Worms,Cyber Security,Cybersecurity,Data Encryption,Information Protection,Computer Hacker,Computer Virus,Computer Worm,Data Compromising,Data Encryptions,Encryption, Data,Encryptions, Data,Hacker, Computer,Hackers, Computer,Protection, Data,Protection, Information,Security, Computer,Security, Cyber,Security, Data,Virus, Computer,Viruses, Computer,Worm, Computer,Worms, Computer
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron