Physical Layer Security for the Smart Grid: Vulnerabilities, Threats, and Countermeasures

AI-assisted secure data transmission techniques for next-generation HetNets: A review

The physical layer security techniques have made great progress in the past few years. The essential idea of physical layer transmission is to maximize the randomness of undesired channels. The 4-D antenna arrays formed by introducing time as the fourth controlling degree of freedom can be used to regulate the radiated fields in space, time, and frequency domains simultaneously. Thus, 4-D antenna arrays are an effective carrier for achieving physical layer secure transmission. However, the traditional secure transmission approach based on 4-D antenna arrays always inevitably leads to high sidelobe levels (SLLs) of radiated power or less channel randomness. Aimed at this problem, a novel physical layer secure transmission approach based on 4-D antenna arrays is proposed, which combines the advantages of traditional phased arrays and 4-D arrays. The approach can be used to reduce the SLL of radiated power and improve the channel randomness by optimizing the time sequences and static amplitude weighting. Moreover, the signal distortion caused by time modulation can be compensated in the desired direction by premodulating the transmitted signals. Both the numerical and experimental results demonstrate the effectiveness of the proposed approach.

Securing communication between multiple users efficiently while there are too many potential eavesdroppers has become an important issue with the rise of the Internet of Things (IoTs). This paper extends on earlier research, moving from a single‐user and single‐eavesdropper scenario to a complex multiuser and multieavesdropper context, and incorporates an advanced physical layer security (PLS) technique for the first time. Using reconfigurable intelligent surfaces (RISs) enhances the strength and quality of signals for intended users, while those to the unintended users are suppressed. Real‐time control of the RIS phase shifts is enabled through a deep deterministic policy gradient (DDPG) algorithm and this control significantly changes the trade‐off between security and energy wastage. The simulation results demonstrate that the developed approach can scale up in densely populated urban centers, while increasing the bit error rate (BER) performance and the overall energy efficiency across different wireless mobile channels.

With the uninterrupted revolution of communications technologies and the great-leap-forward development of emerging applications, the ubiquitous deployment of Internet of Things (IoT) is imperative to accommodate constantly growing user demands and market scales. Communication security is critically important for the operations of IoT. Among the communication security provisioning techniques, physical layer security (PLS), which can provide unbreakable, provable, and quantifiable secrecy from an information-theoretical point of view, has drawn considerable attention from both the academia and the industries. However, the unique features of IoT, such as low-cost, wide-range coverage, massive connection, and diversified services, impose great challenges for the PLS protocol design in IoT. In this article, we present a comprehensive review of the PLS techniques toward IoT applications. The basic principle of PLS is first briefly introduced, followed by the survey of the existing PLS techniques. Afterwards, the characteristics of IoT are identified, based on which the challenges faced by PLS protocol design are summarized. Then, three newly-proposed PLS solutions are highlighted, which match the features of IoT well and are expected to be applied in the near future. Finally, we conclude the paper and point out some further research directions.

Due to the advances in computing and sensing, deep learning (DL) has widely been applied in smart energy systems (SESs). These DL-based solutions have proved their potentials in improving the effectiveness and adaptiveness of the control systems. However, in recent years, increasing evidence shows that DL techniques can be manipulated by adversarial attacks with carefully-crafted perturbations. Adversarial attacks have been studied in computer vision and natural language processing. However, there is very limited work focusing on the adversarial attack deployment and mitigation in energy systems. In this regard, to better prepare the SESs against potential adversarial attacks, we propose an innovative adversarial attack model that can practically compromise dynamical controls of energy system. We also optimize the deployment of the proposed adversarial attack model by employing deep reinforcement learning (RL) techniques. In this paper, we present our first-stage work in this direction. In simulation section, we evaluate the performance of our proposed adversarial attack model using standard IEEE 9-bus system.

Since the development of remote applications and benefit, the request for a secure and quick information exchange association requires unused innovation arrangements competent to guarantee the finest countermeasure against security assaults, it is one of the foremost promising unused remote communication innovation, due to the plausibility of utilizing natural counterfeit lights as information exchange channel in free- space. The broadcast nature of VLC makes it fundamental to consider the security of fundamental transmissions. Physical layer information communication and security beneath the channel has ended up a raising concern for communication analysts and due to the victory of physical layer security (PLS) in making strides the security of radio-frequency (RF) remote systems, expanding such PLS techniques to VLC frameworks has been of incredible intrigued. By analysing some cases of the conceivable busybody dangers that can happen in these frameworks, this offers novel bits of knowledge into the vulnerabilities of state-of-the-art PLS plans for VLC frameworks, counting distinctive channel models, input disseminations, arrange arrangements, precoding/signalling techniques, and mystery capacity and data rates and beneath VLC the tall level security and security overhauling arrangements are educated and moderated a period. Key Words: Visible Light, Physical layer security, Multi User, Spread-spectrum, security performance.

Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems.

Physical layer security and covert communication in UAV-ISAC networks: A comprehensive survey

Non-orthogonal multiple access (NOMA) is emerging as a popular radio access technique to serve multiple users under the same resource block to improve spectral efficiency in 5G and 6G communication. But the resource sharing in NOMA causes concerns on data security. Since power domain NOMA exploits the difference in channel properties for bandwidth-efficient communication, it is feasible to ensure data confidentiality in NOMA communication through physical layer security techniques. In this work, we propose to ensure resistance against internal eavesdropping in NOMA communication through a secret key derived from channel randomness. A unique secret key is derived from the channel of each NOMA user; which is used to randomize the data of the respective user before superposition coding (SC) to prevent internal eavesdropping. The simulation results show that the proposed system provides very good security against internal eavesdropping in NOMA.

Radio frequency identification (RFID) systems are gaining more attention in new frontiers of applications due to their simplicity, low size factor and low cost of deployment. One of the emerging frontiers of applications of RFID is the healthcare system. In this paper, we propose a novel physical layer security technique (PLS) to secure radio frequency identification (RFID) tags used in healthcare systems. To achieve secure communications for RFID systems, directional modulation (DM) scheme is exploited at the reader side. The proposed system maintains the simple circuity and processing nature of passive tags. The results obtained demonstrate that DM techniques can achieve physical layer security for RFID systems.

In the new generation wireless communication systems, when multiple users are served, information security among them should be considered to protect each user’s privacy. In this case, physical layer security techniques can be adopted for this mutual confidence problem. Unlike other traditional physical layer security methods, the proposed scheme improves secrecy rate with the information of users’ access statuses (i.e., being served or idle), and it is denoted as SRT-AS. In the SRT-AS, appropriate parameters expressing users’ actual access statuses are introduced, and an adaptive transmitter and receiver design strategy is used to increase security. It can be found that no iterative process is included in the SRT-AS, moreover, performance analysis and simulation results illustrate that the SRT-AS is feasible for the multi-user system.

This survey paper explores the application of machine learning (ML) techniques in the domain of workplace injuries, focusing on three key areas: risk prediction, return to work (RTW) strategies, and demographic analysis. Through an extensive review of literature from January 2015 to July 2024, this paper examines the latest advancements in ML-driven approaches to workplace safety and identifies important research gaps. This paper highlights how classical ML techniques, such as ensemble models and decision trees, have become essential tools for identifying workplace injury risks, enabling more accurate interventions. It emphasizes the importance of leveraging ML in personalized RTW programs, which use data-driven insights to improve recovery outcomes and reduce economic demands. In the context of demographic analysis, this paper explores how ML algorithms can uncover disparities in injury rates across various age groups, industries, and occupations, underscoring the need for targeted safety measures. Moreover, research gaps are identified, particularly regarding the emerging potential of advanced ML techniques, such as deep learning and large language models (LLMs), for analyzing structured and unstructured safety data, methods that have not yet been widely applied in workplace injury research. As such, future research should apply recent advances in ML, integrating these approaches with comprehensive and accessible datasets to enhance the prediction and prevention of workplace injuries, provide more detailed analytics and insights, and improve safety protocols across all industries. This comprehensive survey is an invaluable resource for researchers and practitioners leveraging ML to address complex challenges in workplace safety.

Background Peripheral Arterial Disease(PAD) is a prevalent cardiovascular condition affecting millions worldwide, and the advent of machine learning(ML) techniques offers promising opportunities to enhance risk prediction and personalise patient management in this complex disease. This systematic review and meta-analysis aimed to evaluate the performance of ML models in predicting adverse outcomes in PAD patients compared to traditional statistical approaches. Methods A comprehensive search of major databases(MEDLINE,EMBASE,CINAHL,Cochrane CENTRAL) was conducted for studies published between 2000 and 2024. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the PROBAST tool. The quality of included studies was also assessed using criteria outlined by Qiao et al. A modified Hierarchical Summary Receiver Operating Characteristic(HSROC) analysis was performed to compare the predictive performance of various ML models and traditional regression methods. Results Thirteen studies met the inclusion criteria. Gradient boosted models demonstrated the highest predictive performance with a diagnostic odds ratio(DOR) of 36.593(95% CI:24.44-54.79), sensitivity of 0.853, and specificity of 0.863. The best performing ML models for outcomes within 2 years, showed a DOR of 34.169(95% CI:22.840-51.117). Traditional regression models consistently underperformed compared to ML approaches, with the lowest DOR of 3.326(95% CI:1.814-6.097). The quality assessment revealed a mix of methodological rigor, with 54% of studies rated as low risk of bias and 46% as unclear. Conclusion Advanced ML techniques demonstrate superior predictive power for adverse outcomes in PAD patients compared to traditional regression methods.

The accurate detection and assessment of stress play a pivotal role in enhancing individual well-being and healthcare outcomes. Traditional methods of stress detection often grapple with limitations in accuracy and scalability. With the advent of machine learning (ML), the potential to revolutionize stress detection has emerged. This paper presents a comprehensive study on the application of ML algorithms for stress detection, with a focus on physiological and behavioral data analysis. Central to our approach is the integration of SHapley Additive exPlanations (SHAP), an Explainable Artificial Intelligence (XAI) technique, to interpret ML models. SHAP provides a novel lens to understand the impact of individual features in the complex decision-making processes of ML models, thereby enhancing the transparency and reliability of stress predictions. We demonstrate how SHAP not only aids in elucidating model decisions but also contributes to refining the models for greater accuracy. Our results highlight the effectiveness of ML in detecting stress and the pivotal role of XAI in making these models more interpretable and trustworthy. This study underscores the synergy between advanced ML techniques and XAI, paving the way for more nuanced and reliable stress detection methodologies that are essential in diverse settings, from healthcare to workplace environments.

Ultra-reliable low-latency communication (uRLLC) emerges as one of the three service categories in fifth-generation (5G) wireless networks, with a potential to support multiple advanced services for latency-sensitive communications. The inherent openness of the wireless environment makes security in 5G unprecedentedly urgent to be addressed. Accordingly, the physical layer security (PLS) technique exploits the randomness characteristics of the wireless channel to fulfill security and confidentiality of information transmission. Different from many current works, which ignored the significance of security in uRLLC, this paper studies secrecy improvement strategy design in uRLLC scenarios. First, we propose suitable performance metrics for evaluating PLS of uRLLC and make a compromise between reliability, latency, and security, while maximizing the network throughput. Next, we identify the key challenging issues for achieving PLS in the context of uRLLC, and discuss the role of cooperative communication and mobility in terms of possible solutions. To the best of our knowledge, this is the first paper to make a trade-off among reliability, security and network throughput. Simulations validate the theoretical analyses.