Adapting to Evolving Cyber Threat Landscapes with Dynamic Security Protocol Management in Large-Scale IoT Sensor Networks

Anil  Audumbar Pise ¹, Saurabh Singh ², Hemachandran K. ³, Shraddhesh Gadilkar⁴, Zakka Benisemeni  Esther⁵, Ganesh Shivaji Pise⁶, Jude Imuede⁷

¹Siatik Premier Google Cloud Platform Partner Johannesburg South Africa, University of the Witwatersrand Johannesburg-South Africa Computer Science, Head of Data Science & Machine Learning, Adjunct Professor

²Assistant Professor, Department of AI and Big data, woosong University, Daejeon South Korea

³Professor, School of Business, Woxsen University, Hyderabad, India

⁴Associate Engineer, TSYS Global Payments, Pune, India

⁵Senior Lecturer, Federal Polytechnic Bauchi, Nigeria

⁶Assistant Professor in Pune Institute of Computer Technology Pune

⁷University of  Prince Edward Island

Emails: anil@siatik.com; singh.saurabh@wsu.ac.kr; hemachandran.k@woxsen.edu.in; sgadilkar@tsys.com; benizakka@fptb.edu.ng;  gspise@pict.edu; jimuede@upei.ca

   Abstract

The Adaptive Security Protocol Framework (ASPF) is introduced as a sophisticated algorithm designed for dynamic security protocol adaptation in large-scale IoT sensor networks. Comprising five integral algorithms, namely ASPF, MLTD, DKMS, BAP, and CTIS, the framework ensures a comprehensive and adaptive defense mechanism against evolving cyber threats. ASPF initiates with data collection, preprocessing, and feature extraction, employing supervised learning for model training. Anomaly detection triggers alerts and responses, guiding continuous learning and security protocol adaptation. MLTD enhances real-time threat detection through dynamic model training and threat intelligence integration. DKMS focuses on secure key management for data transmissions, calculating device thresholds and ensuring adaptive key exchanges. BAP leverages historical data for behavioral profiling, enabling real-time anomaly detection and adaptive profile updates. CTIS assesses and aggregates threat levels, fostering continuous collaboration and collective defense. The ablation study emphasizes the indispensable role of each algorithm, showcasing their synergistic contributions to the overall system's adaptability and robustness. Evaluation through comprehensive tables and visual representations highlights the proposed method's superiority over existing security protocols. The ablation study underscores the holistic nature of ASPF, solidifying its efficacy in addressing the dynamic challenges of cybersecurity in large-scale IoT sensor networks.

Keywords: Adaptive Security Protocol Framework (ASPF); Algorithm; Anomaly Detection; Behavioral Analysis and Profiling (BAP); Collaborative Threat Intelligence Sharing (CTIS); Continuous Learning; Cyber Threats, Dynamic Key Management System (DKMS); Large-scale IoT Sensor Networks; Machine Learning-Based Threat Detection (MLTD).