The AI that's rewiring industrial security from the inside out. It doesn't just detect threats - it predicts and neutralizes them before they exist. The future of manufacturing security isn't human. 
#IndustrialAI
https://www.nature.com/articles/s41598-024-75414-0
NatureA secure and lightweight trust evaluation model for enhancing decision-making in resource-constrained industrial WSNs - Scientific ReportsThe dependability of nodes in an Industrial Wireless Sensor Network (IWSN) is vital for precise decision-making and the overall functioning of the network. Unreliable nodes have the potential to result in incorrect data, which can undermine the reliability of monitoring and control systems. Inaccurate decision-making in IWSNs can result in operational failures, safety risks, inadequate resource utilization, elevated energy consumption, and susceptibility to security vulnerabilities. Trust models tackle these challenges by recognizing and reducing the impact of unreliable nodes, improving the precision of data, and strengthening the security of the IWSNs. To overcome these challenges, this research work introduces a “Novel approach for Trust Utilization and Reliability Enhancement” (NATURE) in IWSNs. The unique approach employed in proposed NATURE model is distinguished by its multi-level clustered model, which improves attack mitigation, reduces communication overhead, and enhances overall network efficiency. This model is particularly effective in industrial environments where the network structure and the nature of data traffic are highly dynamic. The use of a multifactor trust estimation framework allows NATURE to assess the trustworthiness of SNs based on a comprehensive set of criteria, including behavior patterns, energy consumption, and communication reliability. By operating on multiple levels, NATURE can dynamically adjust trust functions to accurately distinguish between trustworthy and faulty SNs, thereby improving network reliability. Moreover, NATURE employs the temporal decay factor to prioritize recent node behaviors, ensuring that obsolete actions have minimal impact. Additionally, it uses the dynamic adjustment factor to balance the influence of negative interactions, encouraging reliable and responsive trust evaluations. Additionally, NATURE integrates a dynamically adjustable logical time window to enhance monitoring precision and adaptability, outperforming fixed-length windows in anomaly detection. NATURE integrates an Optimal Lead Node Election Algorithm (OLNEA) to improve cluster leader selection process. OLNEA considers network density, link quality, and Lead Node (LN), battery life, ensuring competent data aggregation and load balancing. By periodically selecting robust LNs and seamlessly switching to alternatives, NATURE promotes reliability and mitigates the impact of low LN battery levels. Additionally, NATURE employs a trust-based attacks detection algorithm to fortify IWSN security. This algorithm employs keen methods to verify data integrity, monitor energy levels, and ensure message authenticity, effectively safeguarding the IWSN from malevolent attacks and ensuring the safe transmission of data. Experimental results highlight NATURE’s exceptional performance across significant metrics when compared to existing trust schemes. In a WSN of 500 SNs with 30% being malicious, NATURE detects malicious behavior with a 97% accuracy, outperforming other models. Even with 50% malicious SNs, NATURE maintains a high detection accuracy of 91%, again surpassing alternative approaches. Additionally, NATURE significantly reduces energy consumption while achieving efficient throughput rates, underscoring its effectiveness in challenging network conditions.
#MWC25 
