aspg-18-3513-1792025430 20261015005030 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 15 2 Ehab Bahaudien Ashary Electrical and Computer Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia Intrusion Detection Systems (IDS) are increasingly being integrated into smart homes for effective pervasive sensing and resource management, thanks to advancements in sensor technologies and the development of Information and Communication Technology (ICT). Securing IDSs in smart homes is significant for safeguarding crucial data and ensure the integrity of related devices. Implementing strong cybersecurity, measures, including regular software updates, encrypted communication protocols, and secure authentication mechanisms, is critical to safeguard potential risks. As the smart home network constantly increasing, developers, users, and manufacturers must work together to maintain and prioritize stringent security standards, alleviating the risks closely related to connected devices and preserving the safety and privacy of the consumer. Blockchain (BC) technology can increase the security of IDS in smart homes by giving a tamper-resistant and decentralized framework to manage data transactions and device interactions. By leveraging blockchain, smart home networks can establish a more secure and resilient infrastructure, which provides consumers with high confidence in the security and privacy of the interconnected devices. This study introduces a Blockchain and Multi-Head Attention-Based Deep Learning for Intrusion Detection System in Smart Networks (BCMHDL-IDSSN) technique in Smart Home Networks. The BCMHDL-IDSSN method aims to enhance security in the smart home networks. In the BCMHDL-IDSSN technique, BC technology is used to achieve security. Besides, the BCMHDL-IDSSN technique involves the design of a multi-head attention bidirectional gated recurrent unit (MHA-BiGRU) method for the detection of malicious activities. Finally, an enhanced pigeon-inspired optimization (EPIO) model is applied for the optimal hyperactive parameter choice of the MHA-BiGRU model. A detailed investigation was applied to validate the performance of the BCMHDL-IDSSN method. The simulation values emphasized that the BCMHDL-IDSSN method gains high efficiency over other techniques. 2025 01 13 3513 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.150201 https://americaspg.com/journal/18/article/3513 https://americaspg.com/storage/61737922960.pdf