An Improved and Effective Approach for User Attention in the IoT Through the Hybrid Lightweight Security Framework (HLSF) with Data Mining

An Improved and Effective Approach for User Attention in the IoT Through the Hybrid Lightweight Security Framework (HLSF) with Data Mining

연구 분야: Cryptography

학회: SN Computer Science

The Internet of Things (IoT) is a network of interconnected physical devices capable of communicating, processing information, and operating independently of human intervention. Examples of such intelligent devices include smartphones and laptops. IoT represents a ground breaking development in our era of pervasive connectivity, enabling wireless communication between devices. Projections estimate that up to fifty billion devices and individuals will soon be connected to the internet, significantly increasing IoT’s bandwidth demands. Communication in IoT can utilize both licensed and unlicensed frequencies. Licensed bands, which require payment, support services like 2G and 3G. Conversely, unlicensed bands, such as the Industrial, Scientific, and Medical (ISM) bands, are freely available for specific applications. This research aims to develop a novel Hybrid Lightweight Security Framework (HLSF) to protect user privacy and data integrity while minimizing resource consumption. The HLSF framework employs data mining techniques in an IoT setting, comprising three phases: registration, authentication, and data security. During registration, devices are assigned credentials. Authentication ensures mutual identity verification between the device and the server. The Lightweight Data Security (LDS) algorithm then secures data transmissions through encryption. This study compares the HLSF with the CoAP and OSCAR approaches, evaluating energy consumption, computation, throughput, and packet delivery ratio, demonstrating HLSF’s superior performance. When compared to OSCAR, HLSF offers a 36-68% smaller energy footprint and HLSF provides a 16-45% reduced energy overhead than CoAP does. Moreover, HLSF has 20-38% less computational overhead than OSCAR does. The throughput of HLSF is 14% higher than that of OSCAR and 3% higher than that of CoAP. HLSF uses between 9% and 23% less processing power than CoAP proves the success of the proposed method.