Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics

Security in the Internet of Things (IoT)-consumer electronics is decisive in safeguarding connected devices from possible vulnerabilities and threats. Strong security measures are required to protect against unauthorized access, data breaches, and cyberattacks as these smart devices gather, transfer, and save sensitive information. Employing regular software updates, secure authentication protocols, and strong encryption are indispensable approaches to guarantee the integrity and privacy of user details. Drones offer users a bird's-eye view that can be started and implemented anywhere and anytime. But, the malicious use of drones has developed among criminals and cyber-criminals. The possibility and frequency of these attacks are maximum, and their effect is highly unsafe and devastating. Thus, the desire for protective, preventive counter-measures and detective are needed. Intrusion detection utilizing deep learning (DL) drones control advanced neural network (NN) structures to improve security surveillance in dynamic outdoor environments. Prepared with sophisticated sensors and onboard processing abilities, these drones autonomously examine aerial imagery to identify and classify possible risks like suspicious activities, unauthorized personnel, or vehicles. DL approaches allow drones to learn complex patterns and anomalies in real-time, enabling quick response and proactive security procedures. This study introduces an enhanced Mathematical Modeling-based Blockchain with Mountain Gazelle Optimization and Attention to Deep Learning for Cybersecurity (MGOADL-CS) technique in the drone's platform. The MGOADL-CS method aims to improve cybersecurity using BC technology in the drone's environment by detecting attacks using optimal DL models. In the initial stage, the MGOADL-CS technique uses a linear scaling normalization (LSN) approach to normalize the input data. The MGOADL-CS technique uses an improved tunicate swarm algorithm (ITSA) based feature selection approach for dimensionality reduction. Besides, the attention long short-term memory neural network (ALSTM-NN) model is employed to detect and classify cyberattacks. Finally, the MGO-based hyperparameter tuning process is performed to adjust the hyperparameter values of the ALSTM-NN model. To highlight the enhanced attack detection results of the MGOADL-CS technique, a detailed simulation set is accomplished under the NSL dataset. The performance validation of the MGOADL-CS method portrayed a superior accuracy value of 99.71 % over existing approaches.