Search Results (6)

Deep learning (DL)-based multi-user physical layer authentication (PLA) in the Industrial Internet of Things (IIoT) requires frequent updates as new users join. Class incremental learning (CIL) addresses this challenge, but existing generative replay approaches depend on heavy parameterized models, causing high computational overhead and limiting deployment in resource-constrained environments. To address these challenges, we propose a parameter-free statistical generator-based CIL framework, PSG-CIL, for DL-based multi-user PLA in the IIoT. The parameter-free statistical generator (PSG) produces Gaussian sampling on user-specific means and variances to generate pseudo-data without training extra models, greatly reducing computational overhead. A confidence-based pseudo-data selection ensures pseudo-data reliability, while a dynamic adjustment mechanism for the loss weight balances the retention of old users’ knowledge and the adaptation to new users. Experiments on real industrial datasets show that PSG-CIL consistently achieves superior accuracy while maintaining a lightweight scale; for example, in the AAP outer loop scenario, PSG-CIL reaches 70.68%, outperforming retraining from scratch (58.57%) and other CIL methods. Full article

Due to technological advancement, the advent of smart cities has facilitated the deployment of advanced urban management systems. This integration has been made possible through the Internet of Vehicles (IoV), a foundational technology. By connecting smart cities with vehicles, the IoV enhances the safety and efficiency of transportation. This interconnected system facilitates wireless communication among vehicles, enabling the exchange of crucial traffic information. However, this significant technological advancement also raises concerns regarding privacy for individual users. This paper presents an innovative privacy-preserving authentication scheme focusing on IoV using physical unclonable functions (PUFs). This scheme employs the k-nearest neighbor (KNN) encryption technique, which possesses a multi-multi searching property. The main objective of this scheme is to authenticate autonomous vehicles (AVs) within the IoV framework. An innovative PUF design is applied to generate random keys for our authentication scheme to enhance security. This two-layer security approach protects against various cyber-attacks, including fraudulent identities, man-in-the-middle attacks, and unauthorized access to individual user information. Due to the substantial amount of information that needs to be processed for authentication purposes, our scheme is implemented using hardware acceleration on an Nexys A7-100T FPGA board. Our analysis of privacy and security illustrates the effective accomplishment of specified design goals. Furthermore, the performance analysis reveals that our approach imposes a minimal communication and computational burden and optimally utilizes hardware resources to accomplish design objectives. The results show that the proposed authentication scheme exhibits a non-linear increase in encryption time with a growing AV ID size, starting at 5$\mathsf{\mu }$s for 100 bits and rising to 39 $\mathsf{\mu }$s for 800 bits. Also, the result demonstrates a more gradual, linear increase in the search time with a growing AV ID size, starting at less than 1 $\mathsf{\mu }$s for 100 bits and rising to less than 8 $\mathsf{\mu }$s for 800 bits. Additionally, for hardware utilization, our scheme uses only 25% from DSP slides and IO pins, 22.2% from BRAM, 5.6% from flip-flops, and 24.3% from LUTs. Full article

Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie–Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency. Full article

In recent times, the advent of innovative technological paradigms like the Internet of Things has paved the way for numerous applications that enhance the quality of human life. A remarkable application of IoT that has emerged is the Internet of Vehicles (IoV), motivated by an unparalleled surge of connected vehicles on the roads. IoV has become an area of significant interest due to its potential in enhancing traffic safety as well as providing accurate routing information. The primary objective of IoV is to maintain strict latency standards while ensuring confidentiality and security. Given the high mobility and limited bandwidth, vehicles need to have rapid and frequent authentication. Securing Vehicle-to-Roadside unit (V2R) and Vehicle-to-Vehicle (V2V) communications in IoV is essential for preventing critical information leakage to an adversary or unauthenticated users. To address these challenges, this paper proposes a novel mutual authentication protocol which incorporates hardware-based security primitives, namely physically unclonable functions (PUFs) with Multi-Input Multi-Output (MIMO) physical layer communications. The protocol allows a V2V and V2R to mutually authenticate each other without the involvement of a trusted third-party (server). The protocol design effectively mitigates modeling attacks and impersonation attempts, where the accuracy of predicting the value of each PUF response bit does not exceed 54%, which is equivalent to a random guess. Full article

Quantum Key Distribution (QKD) is popular for establishing a native secure quantum communication network. However, existing QKD networks are built via classical wired fiber channels; it is difficult to distribute quantum keys directly into mobile phones, and no effective candidate solution is available yet. This paper presents a novel Flexible Physical Layer Authentication (FPLA) mechanism that exploits the unique characteristic of wireless signals from mobile phones to securely distribute quantum keys via wireless 5G channels. In particular, a 5G Up-Link Sounding Reference Signal (SRS)-based transmission model is developed to capture and extract the unique characteristic, which is then used to distribute quantum keys. Moreover, the model could lose accuracy due to SRS variations introduced by 5G Multiuser Multiple-Input Multiple-Output (MU-MIMO), so a dimensional transformation residual network is designed to classify legitimate and malicious user equipment (UE). An average authentication accuracy of 96.8% is proved by FPLA in multiple experiments in a 3 dB Signal-to-Noise Ratio (SNR) test environment with a training dataset of 300 samples per malicious UE. Simulation results show that FPLA is able to adapt to antenna diversity in 5G MU-MIMO systems. Full article

Advances in higher-end spectrum utilization has enabled user equipment to dock multiple antenna elements, and hence make use of selectivity via equalization in new generation of mobile networks. The equalization can exploit channel statistics to shape covariance matrices, and hence improve network performance at the physical layer of these networks by projecting segregated signals to non-overlapping subspaces. We propose to establish the promise of covariance shaping method by incorporating the equalizers in the modelling of a downlink multi-user multiple-input multiple-output (MU-MIMO) systems and thereby characterizing a key performance indicator, namely, the sum ergodic capacity. This is achieved by utilizing a residue theory approach which can account for indefinite eigenvalues. The system modelling is generic in a sense that it requires the base station (BS) to only have second order statistics of the channel rather than instantaneous knowledge. Furthermore, the BS incorporates a transmit beamformer design to enhance the ergodic capacity and feedforward the information of covariance shaping equalizers. Search method for transmit beamforming is also proposed which shows a promising three fold increase in sum ergodic capacity at signal-to-noise ratio of 10 dB for the considered MU-MIMO system. Proposed characterization of the system is authenticated using simulation means, and a comparative analysis of transmit beamformer designs on the sum ergodic rate is showcased. Full article