With the continuing growth in various research domains such as Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics (BDA), and Cloud Computing (CC), the Internet of Things (IoT) has become a popular technology nowadays. It provides a virtual interaction between physical objects and the cyber world over the Internet without human intervention. The IoT devices are embedded with sensors, software, or some other useful technologies for connecting, sharing, and exchanging data with other devices. Among recent technologies impacting human lives, Radio Frequency IDentification (RFID) has become a core identification technology that can be integrated into the IoT environments which connect billions of things or objects. However, an RFID system has two major issues: (i) an adversary can tamper or intercept the sensitive information of the RFID tags, which may cause forgery and privacy problems, and (ii) RFID tags have limited computational power capability which makes it challenging to use current security solutions. To deal with these issues, we propose an anonymous and reliable ultralightweight RFID-enabled authentication scheme (namely AnonR²AS) for IoT systems in a cloud computing environment. AnonR²AS integrates bitwise exclusive-OR, left–right rotations, and ultralightweight half-flip operation, to reduce computational overheads on tag. The AnonR²AS provides stronger security (by preventing several attacks) and improves performance concerning low computational, communication, and storage costs. Also, it preserves information privacy and tags untraceability property by using Vaudenay privacy model. The Scyther simulation tool verification has been performed for its formal security analysis. The performance analysis ensures our AnonR²AS scheme is preferable for low-cost RFID systems.

The concept of Internet of Vehicles (IoV) played an important role for the improvements that happened in the smart transportation field. Nowadays, it is one of the hot topics in academia, automotive sector, industry, and research. The message transfer between the vehicles or infrastructures are necessary for communication in a vehicular network. A public channel is used for all these types of communication, which offers adversary to get a chance to delete, modify, insert, and extract data. Similar to security, lightweight property is yet another important factor needed to be considered thoroughly because of the highly dynamic nature of IoVs. In the state-of-the-art, several schemes are designed separately for security and lightweight property; however, we have only a small number of works that discussed the importance of maintaining a balance between these two criteria. Among them all schemes use a secure medium for the registration process. Different from them, we used a public channel, which makes our scheme more vulnerable to attacks than the state-of-the-art. In this paper, we designed a lightweight, secure authentication and key agreement scheme (LightKey), which is strong enough to resist different types of attacks and at the same time lightweight as well. In particular, LightKey is resistant to replay, man-in-the-middle, impersonation, physical capture, session key disclosure, perfect forward secrecy, backward secrecy, and ephemeral secret leakage attacks. We have simulated LightKey in Scyther (automated security protocol verification tool) and proved that it is secure against the above mentioned attacks. The performance analysis results show that LightKey takes 10% less computation cost than most related competitive scheme, which makes it to fit in the On-Board Unit (OBU) of any vehicles easily.

In the era of the Internet of Communication Technologies (ICT), the Internet is becoming more popular and widely used across the world. Radio Frequency IDentification (RFID) has become a prominent technology in healthcare systems for identifying tagged objects. The RFID tags are attached to the billions of different healthcare devices or things in several associated applications. However, RFID tags’ security and privacy are regarded as the two biggest concerns. An adversary might eavesdrop, tamper, or even intercept the transmitted messages in RFID systems. Also, the privacy of the users (patients, doctors, and nurses) may breach. In past years, numerous ultralightweight RFID authentication schemes have been proposed in the healthcare sector. However, all these schemes were pointed out as insecure under several known security attacks namely, replay, impersonation, full-disclosure, and de-synchronization attacks. Keeping in view such security flaws, we present an efficient and reliable ultralightweight RFID authentication scheme (ER²AS) for healthcare systems to enhance patients’ medication safety. The scheme employs bitwise XOR, circular left–right rotations, and our proposed ultralighweight reformation operation to achieve higher-level security. The security and privacy evaluations demonstrate that ER²AS scheme resists several known security attacks. The performance analysis also demonstrates that it incurs lower computation and storage overhead on the RFID tags, thus making it practical to be implemented in real-time healthcare environments.

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.