Wireless Sensor Networks (WSNs) are often used for critical applications where trust and security are of paramount importance. Trust evaluation is one of the key mechanisms to ensure the security and reliability of WSNs. Traditional trust evaluation schemes rely on fixed, predetermined thresholds, or rules and static attack models, which may not be suitable for all situations such as dynamic and heterogeneous network environments with new and unknown attack scenarios as well as have several problems such as limited security and scalability, limited accuracy, incomplete coverage, lack of adaptability that can limit their effectiveness. Machine Learning (ML) has been shown to be an effective tool for trust evaluation in WSNs, offering several benefits over existing schemes such as greater adaptability, scalability, and accuracy since ML algorithms can analyze and learn from the data collected in real-time from multiple sources (sensor readings, network traffic, and user behavior) enabling them to dynamically adjust their decision-making criteria based on the current network conditions. Trust-aware ML-based security mechanisms achieve safety and efficient decision-making by reducing uncertainty and risk to accomplish real-world tasks. This paper presents a Machine Learning (ML)-based trust evaluation model in the unattended autonomous WSN environment to achieve reliability, adaptability, scalability, and accuracy by generating quick and reliable trust values dynamically. The proposed machine learning algorithm extracts various trust features such as Co-Location Relationship (CLR), Co-Work Relationship (CWR), Cooperativeness-Frequency-Duration (CFD), and Reward (R) to obtain a robust trust rating of sensor devices and predict future misbehavior. These trust features are combined to generate a final trust rating before making any decision about the reliability of any sensor device. Moreover, the projected trust model (ETDMA) integrate direct communication trust and indirect trust with the help of a logical time window that periodically records the trustworthy and suspicious interactions. Simulation experiments exhibit the effectiveness of the proposed trust evaluation method in terms of change in trust values, malicious nodes detection (94%), FNR (0.9%), F1-Score (0.6), and accuracy (92%) in the presence of 50 malicious nodes.

Internet of Things (IoT) technologies rapidly evolve and are used in many real-life applications. One of the core technologies used in various IoT applications is Radio Frequency IDentification (RFID) technology. RFID wirelessly and uniquely identifies the tagged objects without a direct line of sight. However, the research community had reported privacy and security-related concerns in RFID systems, where an adversary may tamper, eavesdrop, add, delete, or even modify the transmitted messages over an insecure communication channel. To address the issues mentioned above, we propose an Efficient, Secure, and practical ultra-lightweight RFID Authentication Scheme (ESRAS), which uses rank operation for low-cost tags. We utilize a simplistic bitwise exclusive-OR, circular left rotation, and a newly proposed ultra-lightweight rank operation to provide high security at low cost. The analysis of ESRAS concerning its security and performance shows that it effectively resists several known attacks and is relatively superior to the existing schemes regarding the computational and storage costs. Moreover, ESRAS shows more suitability for low-cost RFID tags and can be executed in a multimedia big data environment.