Unlocking Secure Homes: Blockchain and Machine Learning Revolutionize Smart Home Networks

By Joel ScanlonReviewed by Susha Cheriyedath, M.Sc.Jul 9 2023

With the advent of the Internet of Things (IoT), the world witnessed a proliferation of smart devices, which led to a heightened awareness of security risks to the communication network.

An article recently published in the journal Sensors proposes the implementation of a blockchain layer to facilitate user authentication and to establish a ledger for the network. It presents an enhanced version of the Dragonfly algorithm that integrates effective communication and security features. This updated algorithm includes a training layer that facilitates the selection of optimal samples for three specific classes: "SMART T," "MOD-T," and "AVO-T."

Study: Unlocking Secure Homes: Blockchain and Machine Learning Revolutionize Smart Home Networks. Image Credit: Jirsak /Shutterstock

Background

The contemporary era is characterized by the presence of intelligent technologies capable of managing and optimizing domestic environments, thereby augmenting the quality of human life. There are five crucial facets pertaining to the security and privacy of smart homes, which are instrumental in enhancing the dependability of data transmission among smart devices.

The first is "authentication," which serves to authenticate the entire setup, and the second is "authorization," which verifies and verifies and verifies the user's access privileges. The third one is "confidentiality," which ensures data privacy preservation by granting access solely to authorized users. The fourth aspect is "integration," which serves to mitigate data losses and uphold data accuracy, and the fifth and final facet is referred to as "availability," which pertains to the provision of service access to users who are authorized while ensuring their protection from potential threats. Therefore, the susceptibility of a smart home network to security threats is heightened as a result of the extensive proliferation of interconnected devices. 

This study centers on the development of a learning engine integrated into a smart home communication network. In order to accomplish this objective, a neural-based propagation engine was employed to facilitate the decision-making process for smart transactions, moderate transactions, and transactions intended to be blocked.

Significance of the results

The engine developed in the present study demonstrated superior performance compared to two state-of-the-art algorithms in the domains of electronic information engineering and optimization analysis. This superiority was observed in terms of false authentication rate, qualitative parameters, and computation complexity.

The algorithm under consideration demonstrated a reduced incidence of false authentications in comparison to alternative algorithms. This observation is evident in the percentage improvement values, which indicate the reduction in false authentications as a percentage when compared to state-of-the-art algorithms.

In the 500 test samples, the proposed procedure exhibited 18 false authentications, whereas the alternative algorithm demonstrated 19 false authentications. This signifies a percentage enhancement of 5.26%. The proposed method consistently demonstrated high precision, with an average precision ranging from approximately 0.97 to 0.98. Similarly, the average computational complexity of the algorithm proposed in this study was 3.544 for all test samples. In comparison, the average computational complexity of the algorithm that utilized blockchain and fused machine learning was 3.944. This indicates a significant improvement of 10.14% achieved by the proposed algorithm when compared to the algorithm used in the c study.

Also, it was seen that the neural network acquires the ability to identify patterns within the dataset that correspond to each of the three categories, subsequently utilizing this acquired knowledge to classify novel instances. The authors observed that the computational complexity may experience a substantial increase with the growth in the number of devices and transactions within the network. Additionally, this network can process additional real-time datasets, provided that they are specifically designed to address a similar aspect.

Moreover, this study offers potential prospects for incorporating these methodologies with other advancements, such as 5G and edge computing, to enhance the security and efficiency of communication networks. The proposed architecture exhibits potential applications across diverse domains, including but not limited to hospitals, home care, data marketplaces, and city services

Conclusion

This research paper presents a novel architectural framework integrating multiple technologies to establish a robust and optimized communication network with enhanced security measures. It offers a comprehensive overview of each of its constituent elements, encompassing the ledger generation, user interface, decision-making, and data evaluation methodologies. The authors concluded that the mining procedure could pose a significant computational burden within a proof-of-work blockchain framework, thereby presenting a challenge for IoT devices with limited resources.