An Innovative Intrusion Detection Systems for smart Electronic Consumers

Abstract: The advancement of Internet of Things (IoT) technologies has ushered in a new era for Consumer Electronics (CE), characterized by heightened connectivity and intelligence. This evolution enables enhanced data availability and automated control within CE networks, comprising sensors, actuators, and consumer devices. Cu-BLSTM offers advantages in processing sequential data and capturing long-term dependencies, making it a promising candidate for intrusion detection tasks. However, Cu-BLSTM also presents limitations, including high computational complexity and sensitivity to hyperparameters. To provide a comprehensive analysis, this study compares with Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks (RNN) in the context of intrusion detection for smart CE networks. CNNs excel in extracting spatial features from data, making them suitable for certain types of intrusion patterns. DNNs offer scalability and ease of training, which can be advantageous for large-scale deployment scenarios. RNNs, on the other hand, are well-suited for processing sequential data with temporal dependencies. By understanding the strengths of CNN, DNN, and RNN, this research aims to inform the design and implementation of effective IDS solutions tailored to the unique requirements of smart CE networks. Index terms: Consumer Electronics, Cyber Attacks, Deep Learning, Internet Of Things, Intrusion patterns