HARNESSING BLOCKCHAIN WITH ENSEMBLE DEEP LEARNING-BASED DISTRIBUTED DOS ATTACK DETECTION IN IOT-ASSISTED SECURE CONSUMER ELECTRONICS SYSTEMS

Fatma S. Alrayes, Mohammed Aljebreen, Mohammed Alghamdi, Faheed A. F. Alrslani, Asma Alshuhail, Wafa Sulaiman Almukadi, Iman Basheti and Mahir Mohammed Sharif
Additional contact information
Fatma S. Alrayes: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Mohammed Aljebreen: ��Department of Computer Science, Community College, King Saud University, P. O. Box 28095, Riyadh 11437, Saudi Arabia
Mohammed Alghamdi: ��Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia
Faheed A. F. Alrslani: �Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia
Asma Alshuhail: �Department of Information Systems, College of Computer Sciences & Information Technology, King Faisal University, Saudi Arabia
Wafa Sulaiman Almukadi: ��Department of Software Engineering, College of Engineering and Computer Science, University of Jeddah, Saudi Arabia
Iman Basheti: *Jadara University, Irbid, Jordan††The University of Sydney, NSW, Australia
Mahir Mohammed Sharif: ��‡Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-16

Abstract: Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.

Keywords: Consumer Electronics; Internet of Things; Cyberattacks; IDS; Distributed DoS Attack; Orca Predators Algorithm; Deep Learning; Blockchain; Complex Systems (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400444

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