In device-to-device (D2D) communications, the channel gain between a transmitter and a receiver is difficult to predict due to channel variations. Hence, an attacker can easily perform an impersonation attack between two authentic D2D users. As a countermeasure, we propose a reinforcement learning-based technique that guarantees identification of the impersonator based on channel gains. To show the merit of our technique, we report its performance in terms of false alarm rate, miss-detection rate, and average error rate. The secret key generation rate is also determined under the impersonation attack based on physical layer security.

The next-generation Internet of vehicles (IoVs) seamlessly connects humans, vehicles, roadside units (RSUs), and service platforms, to improve road safety, enhance transit efficiency, and deliver comfort while conserving the environment. Currently, numerous entities communicate in the IoVs environment via insecure public channels that are susceptible to a variety of security assaults and threats. To address these security challenges, we design an anonymous authenticated key exchange mechanism for the IoVs in smart transportation supported by blockchain, referred to as AAKE-BIVT. AAKE-BIVT securely transmits traffic information to a cluster head, before heading to a nearby RSU utilizing the established secret session keys via mutual authentication and key agreement. A cloud server (CS) then securely aggregates data from related RSUs and generates transactions. The CS combines the transactions into blocks in a peer-to-peer network of CSs, and the blocks are confirmed and added to the blockchain via a voting-based consensus method. By means of rigorous informal security studies and formal security analysis through the random oracle model, we reveal that the proposed AAKE-BIVT is resistant to a broad range of potential security assaults in the IoVs environment. Furthermore, a comparative study reveals that AAKE-BIVT outperforms existing state-of-the-art techniques, in terms of security and functionality while being more efficient in terms of communication and computation. Additionally, the blockchain simulation validates the implementation viability of our proposed AAKE-BIVT.

In smart grids (SGs), the systematic utilization of consumer energy data while maintaining its privacy is of paramount importance. This research addresses this problem by energy theft detection while preserving the privacy of client data. In particular, this research identifies centralized models as more accurate in predicting energy theft in SGs but with no or significantly less data protection. Current research proposes a novel federated learning (FL) framework, namely FedDP, to tackle this issue. The proposed framework enables various clients to benefit from on-device prediction with very little communication overhead and to learn from the experience of other clients with the help of a central server (CS). Furthermore, for the accurate identification of energy theft, the use of a novel federated voting classifier (FVC) is proposed. FVC uses the majority voting-based consensus of traditional machine learning (ML) classifiers namely, random forests (RF), k-nearest neighbors (KNN), and bagging classifiers (BG). To the best of our knowledge, conventional ML classifiers have never been used in a federated manner for energy theft detection in SGs. Finally, substantial experiments are performed on the real-world energy consumption dataset. Results illustrate that the proposed model can accurately and efficiently detect energy theft in SGs while guaranteeing the security of client data.

The use of Internet of Drones (IoD) technology has surged across various domains such as logistics, surveying, industrial inspections, emergency response, security, infrastructure monitoring, crop management, and more. However, real-time communication with drones or Unmanned Aerial Vehicles (UAVs) in the IoD environment occurs over an insecure open channel, making it susceptible to various security and privacy vulnerabilities, including unauthorized access, data interception, denial of service attacks, and privacy concerns. Due to their unique characteristics, including long transmission distances, unstable communication environments, resource limitations, and the highly dynamic nature of UAVs, ensuring the security and privacy of IoD systems is of paramount importance for the success of IoD-based applications. Furthermore, drones are resource-constrained devices, and employing expensive security solutions is impractical, as it would significantly reduce the operational capacity of drones. In this paper, we present the design of an ultralightweight, secure, and robust user-authenticated key agreement framework for the IoD environment, named USAF-IoD. The proposed USAF-IoD is developed by incorporating authenticated encryption (ASCON), cryptographic hashing, XOR operations, and the use of physical unclonable functions (PUFs). PUFs are employed to enhance resistance against physical tampering attacks. The security analysis reveals that the proposed USAF-IoD meets the essential security requirements of the IoD environment. The comparative analysis further highlights the effectiveness of the proposed USAF-IoD, notably excelling in terms of security and functionality characteristics when compared to existing benchmark schemes, and showcasing competitive performance in computation, communication, and energy overheads.

Interference has been a key roadblock against the effectively deployment of applications for end-users in wireless networks including fifth-generation (5G) and beyond fifth-generation (B5G) networks. Protocols and standards for various communication types have been established and utilised by the community in the last few years. However, interference remains a key challenge, preventing end-users from receiving the quality of service (QoS) expected for many 5G applications. The increased need for better data rates and more exposure to multimedia information lead to a non-orthogonal multiple access (NOMA) scheme that aims to enhance spectral efficiency and link additional applications employing successive interference cancellation and superposition coding mechanisms. Recent work suggests that the NOMA scheme performs better when combined with suitable wireless technologies specifically by incorporating antenna diversity including massive multiple-input multiple-output architecture, data rate fairness, energy efficiency, cooperative relaying, beamforming and equalization, network coding, and space–time coding. In this paper, we discuss several cooperative NOMA systems operating under the decode-and-forward and amplify-and-forward protocols. The paper provides an overview of power-domain NOMA-based cooperative communication, and also provides an outlook of future research directions of this area.

Ensuring security and privacy in the Internet of Things (IoT) while taking into account the resource-constrained nature of IoT devices is challenging. In smart home (SH) IoT applications, remote users (RUs) need to communicate securely with resource-constrained network entities through the public Internet to procure real-time information. While the 6LoWPAN adaptation-layer standard provides resource-efficient IPv6 compatibility to low-power wireless networks, the basic 6LoWPAN design does not include security and privacy features. A resource-efficient authenticated key exchange (AKE) scheme becomes imperative for 6LoWPAN-based resource-constrained networks to render indecipherable communication functionality. This article presents a lightweight user AKE scheme for 6LoWPAN-based SH networks (LAKE-6SH) to achieve authenticity of RUs and establish private session keys between the users and network entities by employing the SHA-256 hash function, exclusive-OR operation, and a simple authenticated encryption primitive. Informal security validation illustrates that LAKE-6SH is protected against different pernicious security attacks. The security is further validated formally through the random oracle model. Moreover, through Scyther validation, it is demonstrated that LAKE-6SH is secure. In addition, it is demonstrated that LAKE-6SH renders better security features aside from its low communication and computational overheads.