Integrating Predictive Big Data Analytics with Behavioral Machine Learning Models for Proactive Threat Intelligence in Industrial IoT Cybersecurity

aspg-20-2374-1791299181 20261006150621 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Wireless and Ad Hoc Communication IJWAC 2692-4056 2023 7 2 Integrating Predictive Big Data Analytics with Behavioral Machine Learning Models for Proactive Threat Intelligence in Industrial IoT Cybersecurity Vishwesh Nagamalla Associate Professor in CSE (AI&ML), Holy Mary Institute of Technology & Science, Hyderabad J.Raj karkee Department of CSE (AI&ML), St. Martin’s Engineering College, Secunderabad, Telangana, India. Ravi Kumar Sanapala Department of ECE, St. Martin’s Engineering College, Secunderabad, Telangana, India. This paper introduces a comprehensive framework for industrial Internet of Things (IoT) cybersecurity, integrating multiple algorithms to enhance threat intelligence. The proposed framework encompasses five key algorithms, each addressing specific aspects of data preprocessing, time series analysis, predictive analytics, and behavioral machine learning. The Data Preprocessing and Integration algorithm refines raw IoT data through a meticulous 20-step process, ensuring high-quality input for subsequent analyses. The Time Series Analysis algorithm delves into temporal patterns, while the Random Forest algorithm focuses on predictive analytics for proactive threat detection. The LSTM Ensemble algorithm extends the analysis into behavioral machine learning, capturing temporal dependencies and detecting anomalies. The Weighted Average Ensemble combines outputs from predictive analytics and behavioral models, leveraging their correlation for enhanced threat intelligence. An ablation study dissects the individual contributions of each algorithmic component, shedding light on their specific impacts. The results highlight the significance of each step, guiding optimizations for improved performance. The proposed framework outperforms existing methods in various performance metrics, showcasing its potential as a robust solution for proactive threat intelligence in complex industrial environments. This framework stands at the forefront of industrial IoT cybersecurity, offering a holistic and adaptive approach to address evolving threats. The ablation study enhances the transparency and understanding of the framework, contributing to its continuous refinement and effectiveness in safeguarding critical industrial systems. 2023 08 24 2374 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJWAC.070201 https://americaspg.com/journal/20/article/2374 https://americaspg.com/storage/51704321176.pdf