Entrust Adaptive Security for Social Iot Twin Environments Using Pattern Miner, Blockchain and LSTM Enhanced Machine Learning

Abstract

In this paper it details the increasing security challenges in Social Internet of Things (SIoT) systems, focusing on twin environments digital entities with identical structures and vulnerabilities. A novel approach is introduced for detecting and preventing cyberattacks in these environments through the use of a pattern miner to analyze communication and behavior patterns. Attack detection is reinforced by employing Block Chain technology for secure data storage, ensuring integrity and immutability. A machine learning-based recommendation system is integrated to predict vulnerabilities and suggest adaptive security measures. Additionally, Long Short-Term Memory (LSTM) networks enhance this solution by learning from recurring attack patterns, enabling proactive threat mitigation. This multi-layered approach provides robust and dynamic security for SIoT systems, effectively safeguarding twin environments against evolving cyber threats.