Federated learning (FL) has emerged as a promising technique in the Industrial Internet of Things (IIoT) by enabling distributed devices to collaboratively train models without sharing raw data. In FL, ensuring data privacy and secure authentication becomes essential due to the sensitivity of industrial data and the potential for adversarial attacks. This paper highlights a security flaw in a recently proposed FL authentication protocol designed for IIoT environments. Specifically, the scheme is analyzed to be susceptible to public-key replacement attacks. We propose a secure, lightweight authentication scheme for privacy-enhanced federated learning (SLAPE) to address vulnerabilities in participant registration, group key distribution, local data training, and aggregation processes. SLAPE leverages the Elliptic Curve Cryptography with the Chinese Remainder Theorem to support malicious group member traceability, revocation of compromised identities, and efficient batch verification of multiple messages. It effectively resists Type-I attacks that previous schemes could not, while also incorporating forward and backward security essential for IIoT applications. We rigorously demonstrate SLAPE’s resilience against prevalent threats through both formal and informal analyses. Our evaluation results indicate that SLAPE demonstrably enhances the security and privacy of existing schemes, with improvements in computational efficiency for both proof generation and verification, while keeping communication overhead relatively low.

In this paper, we analyze a recent algorithm for resilient consensus control in distributed multi-agent systems. While effective in theory, its reliance on security in communication and strong connectivity assumptions limits its practicality in dynamic electronic and cyber-physical systems, such as embedded device networks and uncrewed platforms. To address these limitations, we propose a State-repairing Multi-agent Resilient Consensus (SMARC) scheme to eliminate the need for normal agents to collect trustworthy state values from a fixed bounded threshold of neighbors to achieve reliable consensus. The core innovation of SMARC is a decentralized state-repair mechanism, which enables agents to obtain additional information from their reachable sets to repair unavailable or corrupted state values of malicious or faulty agents, and autonomously adjust their convergence speed. Additionally, without negatively impacting the consensus performance, SMARC employs a noise-masked surface state to protect the initial states of agents from eavesdropping. This approach avoids extra storage and reduces computations without adhering to strict security prerequisites, making it more suitable for resource-constrained electronic systems compared to existing methods. Theoretical convergence and security proofs demonstrate that SMARC can successfully resist passive attacks while ensuring accurate convergence on multi-dimensional data. Most importantly, from small- to large-scale networks, SMARC achieves a three- to four-fold increase in convergence speed compared to the most competitive recent state-of-the-art resilient consensus algorithm in both passive and active attacks. A prototype electronic of a MAS was also built using six Raspberry Pi devices to validate its performance and robustness in practical environments.

The fog computing paradigm enables mobile users to seamlessly interact with crowds of nearby IoT devices for low-latency services. However, existing authentication and key agreement (AKA) protocols suffer from critical limitations in this mobile context. While existing AKA schemes are optimized for mobile edge environments, they incur substantial overhead in multi-device scenarios: they require repeated authentication for each device and generate distinct session keys per user-device pair, leading to high key management complexity at scale. Furthermore, traditional one-to-many protocols are unsuitable for distributed fog environments due to their reliance on trusted central nodes (e.g., gateways or servers) and incompatible communication models. To address these issues, this paper proposes an efficient and secure authentication protocol for fog computing that integrates elliptic curve cryptography with secret sharing. Our solution enables a user to authenticate an entire group of devices managed by a semi-trusted fog node (honest-but-curious) through a single protocol execution, effectively eliminating the authentication bottleneck caused by scaling devices. Formal security proof under the Real-Or-Random (ROR) model is provided, along with informal analysis, demonstrating the protocol ensures forward secrecy, user anonymity, and resistance to a comprehensive set of attacks, including ephemeral secret leakage, key compromise impersonation, and replay attacks. Performance analysis confirms the scheme maintains low communication and computational overhead while achieving comprehensive security.

The inherent massive heterogeneous devices and open channels in the Internet of Things (IoT) present significant challenges for identity authentication between devices and cloud servers. For this issue, reliable protocols ensure the legality of participants and act as a crucial method to provide security for authentication. In previous research, schemes devised by researchers exhibit certain security vulnerabilities, making it challenging to withstand comprehensive network attacks, e.g., stolen device attacks, replay attacks, impersonation, etc. Additionally, some protocols have complex interaction processes, which incur significant computational redundancy and resource loss. Motivated by this, this article proposes an anonymous and certificateless lightweight authentication protocol (ACLAP) for device-to-server and device-to-device based on elliptic curve cryptography. It improves the communication quality between devices and cloud servers and solves the security risks in authentication. In the scheme, we utilize device users’ passwords and biometric features as verification credentials without storing any trusted proofs on the cloud server. We address the issue of resource consumption caused by numerous devices in the IoT environment. From formal security analysis and comparisons with other works, our protocol has preferable security performance and effectively saves communication resources for authentication. Simulation results demonstrate the feasibility and practical significance of the scheme.