Message Queue Telemetry Transport (MQTT), a widely used messaging protocol in IoT applications, faces significant security challenges, particularly with denial-of-service attacks and ensuring continuous, secure communication. Traditional security measures, like TLS/SSL, often introduce CPU overhead, bandwidth issues, and require complex certificate management. This paper has two main objectives: first, to provide a comprehensive review of MQTT security, detailing how MQTT operates, the associated security vulnerabilities, and existing solutions; and second, to propose a framework called the Stateful Authentication Framework (SAF). SAF enhances MQTT security through two key components: the SAF authentication protocol and a Large Language Model (LLM)-based Intrusion Detection System (IDS). The SAF protocol uses client-state information to establish secure, stateful interactions between clients and brokers, and incorporates a multi-factor authentication mechanism to prevent replay attacks and unauthorized access. Our evaluation of SAF shows that it significantly improves MQTT security, with Scyther simulations confirming the robustness of the protocol against known generic attacks. Additionally, the LLM-based IDS that integrates SAF’s security policies provides an advanced approach for detecting intrusions. To our knowledge, this is the first integrated approach combining a state-based authentication protocol and LLM-based IDS to tackle MQTT security challenges.

The technology of Autonomous Vehicles (AVs) and Unmanned Aerial Vehicles (UAVs) has evolved and contributed to improving road safety and traffic efficiency in Intelligent Transportation Systems (ITS). ITS applications are dependent on a communication network formed between AVs and UAVs suffer from security and privacy issues owing to vulnerable wireless communication channels. Considering these issues, SP2AP, a Physically Unclonable Function (PUF) and Elliptic Curve Cryptography (ECC)-based efficient and reliable privacy-preserving authentication protocol for secure UAV-UAV communications in IoT systems is presented in this paper. The proposed protocol not only offers anonymity and untraceability features but also mitigates UAV capture attacks. A robust informal security analysis confirms that the SP2AP protocol safeguards against various known security attacks, such as masquerade, Man-In- The-Middle (MITM), Ephemeral Secret Leakage (ESL), replay, node tampering, cloning, etc. The proposed protocol offers other security features, including mutual authentication, secure key agreement, no clock synchronization, user anonymity, untraceability, (for/back)ward secrecy, property, and drone-to-drone direct authentication. The formal security proof is performed using the Real-OR-Random (ROR) model, and the Scyther tool also demonstrates that the SP2AP protocol is more robust in terms of security and privacy. Performance analysis evaluates computation and communication costs, which show better superiority compared to similar existing protocols.

With the rapid advancements in wireless communication technologies, Unmanned Aerial Vehicles (UAVs), also known as Small Unmanned Aerial Vehicles (SUAVs) or drones, have been increasingly used in various applications, including the civilian sector. As a result, the security of SUAVs has garnered significant attention from the research community. Furthermore, drones are resource-constrained in nature and can be vulnerable to various known cybersecurity attacks over wireless communication. In light of these considerations, we propose aProvablySecure andReliable Privacy-Preserving AuthenticationScheme forDrone-to-Drone Communications in Internet of Autonomous Things (PSRS-D2D). The proposed scheme employs a secure one-way cryptographic hash and Elliptic Curve Cryptography (ECC) to accomplish a certain level of security. We provide security and privacy analysis, comparing it with competing UAV authentication schemes. This ensures that the PSRS-D2D scheme can withstand various prominent security properties, including mutual authentication and strong anonymity, and is secure against several attacks, such as replay, impersonation, and Man-In-The-Middle (MITM) attacks. We evaluated the performance of the proposed scheme in terms of computational and communicational costs. Furthermore, we conducted a formal security analysis using the Real-Or-Random (ROR) model and the Scyther simulation tools, which demonstrate that our scheme offers significant advantages in terms of security and performance.

The rapid advancement of the Internet of Things (IoT) has enabled the adoption of autonomous vehicles (AVs) and drones in intelligent transportation systems (ITS), improving traffic efficiency and safety. However, security and privacy in interconnected ITS environments is a major concern. It is imperative to safeguard sensitive information from various known attacks while enabling secure communication. Keeping in view the security and privacy of autonomous vehicles in IoT systems, this paper puts forward Provable Secure Lightweight Privacy-Preserving Authentication Protocol (PSLAP). The proposed PSLAP protocol achieves high-level security by utilizing cryptographic primitives such as exclusive-OR, secure one-way hash, elliptic curve cryptography (ECC), and concatenation operators . The proposed PSLAP protocol is demonstrated to be resistant to numerous known security assaults through an informal security and privacy assessment. This study presents a formal security analysis using a widely accepted real-or-random (ROR) model which demonstrates the security hardness of the proposed scheme. Additionally, the performance analysis shows that the proposed protocol has minimal computation and communication costs compared to other existing protocols.

With the rapid proliferation of the Internet of Things (IoT), autonomous vehicles (AVs), or self-driving cars, rely heavily on real-time data sharing and message exchanges over wireless networks. AVs use sensors, artificial intelligence, machine learning, and advanced algorithms to perform various functions, enabling users to operate without human intervention. Owing to the flexibility and high mobility of drones, they could aid in the operations of AVs. However, the security and privacy are the main concerns; specifically, the threat of physical capture and violation of anonymity are the main hurdles for realization of secure communication among the AVs and drones. To address these challenges, we propose an anonymous and provably secure lightweight authentication protocol for unmanned-aerial-vehicle-assisted Internet of Autonomous Vehicles (SLAP-IoAV). The proposed protocol uses cryptographic primitives such as exclusive-OR operations, elliptic-curve cryptography, collision-resistant one-way hashing, and concatenation to ensure robust security. An informal security analysis found that SLAP-IoAV is secure against several known attacks, and a performance analysis established that the protocol has less computational and communication overhead than existing competitive protocols. Additionally, Scyther simulation results confirm that no security vulnerabilities are present. Overall, our protocol delivers superior security and performance, making it well-suited to real-world applications in the AV industry.

With the rapid development of the Internet of Things (IoT), modern Maritime Transportation Systems (MTS) have played a crucial role in the transport industry. The various potential privacy and security concerns (such as vessels’ location tracking, message tampering, unauthorized access to data, etc.) have increased substantially in IoT-enabled MTS. Considering the above issues, we propose a secure, robust, and efficient authentication scheme for MTS based on Elliptic-Curve Cryptography (ECC) and Hash-based Message Authentication Code (HMAC) called PSAS-MTS. The proposed PSAS-MTS scheme not only monitors the vessel’s condition but also ensures protection against collisions in route management and provides safety to the vessel’s passengers. The scheme uses simple XOR, a cryptographic one-way hash, an ECC cryptosystem, and HMAC to achieve a high level of security in MTS. The formal security analysis is carried out using a well-known Real-Or-Random (ROR) model, which ensures the security harness features. A rigorous informal security analysis is also done, which ensures various privacy and security features such as mutual authentication, anonymity, forward and backward secrecy, etc. The proposed PSAS-MTS scheme resists various possible well-known active and passive security attacks. The performance analysis shows that the proposed PSAS-MTS scheme is more efficient in terms of computation and communication overheads when compared to other competitive schemes.

Intelligent Transportation Systems (ITS) dwell on Vehicular Ad-hoc NETworks (VANETs) for message dissemination to achieve the goal of traffic safety and efficiency. VANETs achieve communication among vehicles and roadside units via wireless communication. Hence, security and privacy are significant concerns to be addressed for an effective application of secure VANETs in any ITS. Researchers have addressed these issues with trust management-based schemes or cryptography-based schemes. While these schemes can secure VANETs, they have various limitations presenting hindrances in their deployment. In this context, we have proposed a Blockchain-assisted Trust Computation with a Conditional Privacy-preserving Authentication (BTC2PA) scheme for connected vehicles. The BTC2PA scheme uses a blockchain (Ethereum) assisted PKI infrastructure with digital signatures to achieve authentication for secure communication. Furthermore, it integrates a trust score computation scheme based on a reward and punishment mechanism to provide resistance against internal attacks. The feasibility and validity of the proposed BTC2PA scheme have been studied by implementation work in Rinkeby (Ethereum test network) and extensive simulations using NS-3. The results obtained show that the proposed BTC2PA scheme meets the security and privacy requirements while significantly improving the performance metrics such as communication, storage, computation cost, and end-to-end delay when compared to existing schemes.

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.

Abstract This study investigates a Novel Hybrid Informational model for the prediction of creep and shrinkage deflection of reinforced concrete (RC) beams containing different percentages of ground granulated blast furnace slag (GGBFS) at different ages, varying from 1 to 150 days. The percentage of cement replacement by GGBFS varies from 20 to 60%. In order to examine the effects of the applied load and tensile reinforcement on creep behavior, the magnitude of two-point loading was varied from 200 kg to a maximum of 350 kg while the percentage of tensile reinforcement (ρ) was selected as either 0.77% or 1.2%. The current situation about short-term and long-term deflections due to creep and shrinkage available in the international standards, including ACI, BS and Eurocode 2, is discussed. The results indicate that RC beams containing GGBFS have larger deflections than the ones with conventional concrete (i.e., ordinary Portland cement concrete). After 150 days, the average creep deflection of RC beams containing 20, 40, and 60% GGBFS was 30, 70, and 100% higher than the ones for conventional concrete beams, respectively. A hybrid artificial neural network coupled with a metaheuristic Whale optimization algorithm has been developed to estimate the overall deflection of concrete beams due to creep and shrinkage. Several statistical metrics, including the root mean square error and the coefficient of variation, revealed that the generalized model achieved the most reliable and accurate prediction of the concrete beam’s deflection in comparison with international standards and other models. This novel informational model can simplify the design processes in computational intelligence structural design platforms in future.