Machine Learning-Based Reconnaissance Bee Colony with Custom AES & AE for Robust IoT Data Security and Transmission

Objectives: To propose a robust method to enhance data security and transmission in IoT to address the challenges in real-time data transmission and create a comprehensive security solution. The new method takes credit for the machine learning-based bioinspired method to minimize data loss and maximize end-to-end data transmission rate. Methods: Custom-AES with Avalanche Effect (AE) and Machine Learning-based Reconnaissance Bee Colony are employed to secure the data with enhanced encryption by creating a multi-layered IoT security solution. Key stretching is used to provide high resistance to cryptographic attacks. In order to optimize threat detection, Pareto-based selection and dynamic parameter tuning with ML-RBC are used. To improve attack resilience, rank, and tournament selection methods are employed to target DDoS and MMIT attacks with a minimized encryption time. The NSL-KDD IoT network dataset is used to test the proposed CAES-AE with the ML-RBC model using a network simulator. To assess the performance of the proposed work, the results are compared with prevailing IoT data transmission techniques such as ERSA, QoS-BFT, AES, Custom AES, and RBC. Findings: The suggested CAES-AE with ML-RBC data security and transmission method attains the promising results of 97% authentication success rate, 6 seconds response time to security actions, 96% vulnerability detection speed, 97% data encryption strength, 5 seconds incident response time, and 97.8% network performance (energy efficiency & lifetime), which is higher than earlier methods. Novelty: This research provides a secured data transmission method in an IoT environment to address the real-time data transmission challenges faced in sensor-based networks. This will function as a multi-layered IoT security model that optimizes real-time attack detection and resilience. The performance metrics are evaluated with multiple packet transfer rates to boost the network performance. Keywords: Machine Learning, Advanced Networking, Data Security, Secured Data Transmission, Bio­Inspired Algorithm