Active Attacks Research Articles

An Underwater Source Location Privacy Protection Scheme Based on Game Theory in a Multi-Attacker Cooperation Scenario.

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP.

Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection.

Detecting anomalies in large networks is a major challenge. Nowadays, many studies rely on machine learning techniques to solve this problem. However, much of this research depends on synthetic or limited datasets and tends to use specialized machine learning methods to achieve good detection results. This study focuses on analyzing firewall logs from a large industrial control network and presents a novel method for generating anomalies that simulate real attacker actions within the network without the need for a dedicated testbed or installed security controls. To demonstrate that the proposed method is feasible and that the constructed logs behave as one would expect real-world logs to behave, different supervised and unsupervised learning models were compared using different feature subsets, feature construction methods, scaling methods, and aggregation levels. The experimental results show that unsupervised learning methods have difficulty in detecting the injected anomalies, suggesting that they can be seamlessly integrated into existing firewall logs. Conversely, the use of supervised learning methods showed significantly better performance compared to unsupervised approaches and a better suitability for use in real systems.

Secure Aggregation for Privacy-preserving Federated Learning in Vehicular Networks

The automotive industry has been enhancing autonomous driving systems utilizing the computation and communication networks embedded in vehicles (e.g., cellular networks and sensors) and roadside units (e.g., radar and cameras). Robust security and privacy requirements are essential in Intelligent Transportation Systems (ITS). To satisfy these requirements, most developed autonomous driving systems (e.g., Waymo and Tesla) use machine learning. Machine learning models trained on sensitive raw data promise improvements in performance; however, they cannot provide privacy for sensitive raw data and users. Federated learning advances privacy-preserving distributed machine learning by aggregating the model parameter updates from individual devices in a secure manner. Security Credential Management System (SCMS) for Vehicle to Everything (V2X) communication provides a guarantee for authentication in a privacy-preserving manner and punishes misbehaving vehicles through misbehavior reporting. In this paper, we design a secure aggregation protocol for privacy-preserving federated learning for vehicular networks. Our protocol allows a server to verify vehicles in a secure manner and is used to aggregate each vehicle-provided global model update for federated learning. We prove our protocol for security in the honest-but-curious framework and detect active adversary attacks, as well as show that it provides trust in different domains (e.g., SCMS and outside the domain of SCMS) and in a privacy-preserving manner for vehicles using SCMS. We analyze the process of federated learning in each vehicle and server while communicating during driving on several types of roads (e.g., local, urban, and rural) using cellular networks (LTE and 5G).

Variational autoencoder-assisted unsupervised hardware fingerprint authentication in a fiber network.

Physical-layer authentication (PLA) based on hardware fingerprints can safeguard optical networks against large-scale masquerade or active injection attacks. However, traditional schemes rely on massive labeled close-set data. Here, we propose an unsupervised hardware fingerprint authentication based on a variational autoencoder (VAE). Specifically, the triplets are generated through variational inference on unlabeled optical spectra and then applied to train the feature extractor, which has an excellent generalization ability and enables fingerprint feature extraction from previously unknown optical transmitters. The feasibility of the proposed scheme is experimentally verified by the successful classification of eight optical transmitters after a 20 km standard single-mode fiber (SSMF) transmission, to distinguish efficiently the rogue from legal devices. A recognition accuracy of 99% and a miss alarm rate of 0% are achieved even under the interference of multiple rogue devices. Moreover, the proposed scheme is verified to have a comparable performance with the results obtained from supervised learning.

An Elliptic Curve Menezes–Qu–Vanston-Based Authentication and Encryption Protocol for IoT

The exponential growth of the Internet of Things (IoT) has led to a surge in data generation, critical for business decisions. Ensuring data authenticity and integrity over unsecured channels is vital, especially due to potential catastrophic consequences of tampered data. However, IoT’s resource constraints and heterogeneous ecosystem present unique security challenges. Traditional public key infrastructure offers strong security but is resource intensive, while existing cloud-based solutions lack comprehensive security and rise to latency and unwanted wastage of energy. In this paper, we propose a universal authentication scheme using edge computing, incorporating fully hashed Elliptic Curve Menezes–Qu–Vanstone (ECMQV) and PUF. This approach provides a scalable and reliable solution. It also provides security against active attacks, addressing man-in-the-middle and impersonation threats. Experimental validation on a Zybo board confirms its effectiveness, offering a robust security solution for the IoT landscape.

Wifi Pentesting Roadmap for Classic-Future Attacks and Defenses

The most advanced attack on the Wireless Fidelity (WIFI) network uses social engineering. The hacker makes portal captive and forces the victim for disconnecting to internet instead of entering the real password of the WIFI. In normal actions, asking WIFI password on the web interface is not the real process, but sometimes the victim is not experience enough on security and thinks that it is a technical problem. Also, the victim didn’t have internet connection due to the hard deauthentication and the select open access, which is not his WIFI network. The future generation of WIFI could be use a secure deauthentication. So, this article proposed how the actual attack will be processed, how is the secure deauthentication and how hacker could use this same attack with more secure network. Like conclusion, solutions to resolve this problem will be proposed. New hacking arsenal for replacing the deauthentication is the smart-jamming. With the secure deauthentication, reforging the packet for telling the victim to deauthenticate to the network will not be possible anymore. The smart-jamming select the frequency of the access point of the victim and jam only this specific frequency by sending a noise. In this scenario, the same effect of the first attack is still possible. For the best security of network, two solutions will be proposed: secure deauthentication and hopping frequency. A defensive proposition about secure deauthentication will be found in this article by using cryptographic key exchange like Diffie Hellman (DH), Elliptic Curve Diffie Hellman (ECDH) and Super Isogenies Diffie Hellman (CSIDH).

Auto metric graph neural network optimized with woodpecker mating algorithm for detecting network layer attacks in mobile ad hoc networks

ABSTRACT Nowadays, Mobile Ad hoc Network (MANET) gains more attraction due to its seamless and dynamic use. The existing methods in MANET are plagued by some weaknesses, like higher computation time, undermining their real-time responsiveness. Whereas, lower accuracy in intrusion detection compromises the reliability of these systems, potentially leading to missed threats posing a challenge for effective security measures in dynamic MANET environments. To mitigate these weaknesses, an Auto Metric Graph Neural Network optimized with Woodpecker Mating Algorithm is proposed in this paper for detecting Network Layer Attack in MANET (AGNN-WMA-DNLA-MANET). Initially, the data are gathered from CIC-IDS 2019 dataset. The gathered data undergoes pre-processing to eliminate redundancy and missing values replacement using the Local Least Squares method. Afterward, the preprocessing output is fed to AGNN for classification, which classifies the attack as active, passive, or normal. Then, Woodpecker Mating Algorithm (WMA) is proposed to optimize the AGNN weight parameters to ensure the accurate classification. The proposed technique is executed in NS2 tool. The metrics, like accuracy, false positive rate (FPR) and attack detection rate (ADR), computation time, and RoC is analyzed. The proposed AGNN-WMA-DNLA-MANET approach provides 14.68%, 7.142%, and 4.65% higher accuracy for active attack, 15.19%, 2.77%, and 9.85% higher accuracy for passive attack and 38.18%, 12.02%, and 7.59% better accuracy for normal compared with existing methods, such as Dependable intrusion detection scheme under deep convolutional neural network (DCNN-MANET), deep learning-based intrusion detection scheme for MANET (AE-DNN-MANET), and Benchmarking of machine learning for anomaly based intrusion detection scheme (ML-MANET), respectively.

Artificial Intelligence in Intellectual Property Protection: Application of Deep Learning Model

To create and train a deep learning model costs a lot in comparison to ascertain a trained model. So, a trained model is considered as the intellectual property (IP) of the person who creates such model. However, there is every chance of illegal copying, redistributing and abusing of any of these high-performance models by the malicious users. To protect against such menaces, a few numbers of deep neural networks (DNN) IP security techniques have been developed recently. The present study aims at examining the existing DNN IP security activities. In the first instance, there is a proposal of taxonomy in favor of DNN IP protection techniques from the perspective of six aspects such as scenario, method, size, category, function, and target models. Afterwards, this paper focuses on the challenges faced by these methods and their capability of resisting the malicious attacks at different levels by providing proactive protection. An analysis is also made regarding the potential threats to DNN IP security techniques from various perspectives like modification of models, evasion and active attacks.
 Apart from that this paper look into the methodical assessment. The study explores the future research possibilities on DNN IP security by considering different challenges it would confront in the process of its operations.
 Result Statement: A high-performance deep neural Networks (DNN) model is costlier than the trained DNN model. It is considered as an intellectual property (IP) of the person who is responsible for creating DNN model. The infringement of the Intellectual Property of DNN model is a grave concern in recent years. This article summarizes current DNN IP security works by focusing on the limitations/ challenges they confront. It also considers the model in question's capacity for protection and resistance against various stages of attacks.

Carry Your Fault: A Fault Propagation Attack on Side-Channel Protected LWE-based KEM

Post-quantum cryptographic (PQC) algorithms, especially those based on the learning with errors (LWE) problem, have been subjected to several physical attacks in the recent past. Although the attacks broadly belong to two classes – passive side-channel attacks and active fault attacks, the attack strategies vary significantly due to the inherent complexities of such algorithms. Exploring further attack surfaces is, therefore, an important step for eventually securing the deployment of these algorithms. Also, it is mportant to test the robustness of the already proposed countermeasures in this regard. In this work, we propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms (KEMs) exploiting fault propagation. The attack typically originates due to an algorithmic modification widely used to enable masking, namely the Arithmetic-to-Boolean (A2B) conversion. We exploit the data dependency of the adder carry chain in A2B and extract sensitive information, albeit masking (of arbitrary order) being present. As a practical demonstration of the exploitability of this information leakage, we show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber. The attack on Kyber targets the decapsulation module and utilizes Belief Propagation (BP) for key recovery. To the best of our knowledge, it is the first attack exploiting an algorithmic component introduced to ease masking rather than only exploiting the randomness introduced by masking to obtain desired faults (as done by Delvaux [Del22]). Finally, we performed both simulated and electromagnetic (EM) fault-based practical validation of the attack for an open-source first-order secure Kyber implementation running on an STM32 platform.

A new secure offloading approach for internet of vehicles in fog-cloud federation

The Internet of Vehicles (IoV) plays a crucial role in advancing intelligent transportation systems. However, due to limited processing power, IoV faces challenges in independently handling large volumes of data, necessitating the use of offloading as a solution. Offloading data in wireless environments raises security concerns, highlighting the need for robust data protection mechanisms. This study introduces a secure offloading (SO) scheme within the Fog-Cloud Federation for IoV. The proposed NSO-VFC scheme undergoes both informal and formal analysis using the Avispa tool, demonstrating resilience against active and passive attacks. Performance evaluations indicate that the security measures of NSO-VFC meet acceptable standards compared to similar approaches. Nonetheless, the heightened focus on security incurs higher computational and communication costs than alternative strategies. Simulation experiments using the NS3 tool involve varying numbers of IoVs (50, 70, and 100), revealing that increased IoV density correlates with enhanced packet delivery rates and throughput within the NSO-VFC scheme.

Active RIS-Empowered Signal Cancellation Attacks

Active RIS-Empowered Signal Cancellation Attacks

Observed-Mode-Dependent Nonfragile Control of Networked Control Systems Under Hidden DoS Attacks

This paper develops a methodology on observed-mode-dependent non-fragile control for networked control systems subject to denial-of-service attacks, which takes the strong concealment of attack behavior into account. To describe complex switching phenomenon between different forms of attacks that the adversary may launch on communication channels, the semi-Markov chain is introduced. DoS attacks with stealth characteristic is analyzed from hidden semi-Markov jump systems perspective by constructing a double-layer stochastic process consisting of semi-Markov chain and observed mode sequence. With the aid of emission probability, an observed-mode-dependent non-fragile controller is designed, which can still stabilize the system even if actual attack modes are unavailable. By utilizing the Lyapunov function that depends on actual attack modes and observed ones, sufficient conditions to ensure the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> -error mean square stability of the closed-loop system is deduced. Eventually, a simulation related to the mass-spring-damper system with single channel and an example of the unmanned ground vehicle with two channels are studied, both of which demonstrate the feasibility and validity.

Analysis of the comparison between concentric and eccentric muscle strength in the bilateral knee joint of elite female volleyball players

&lt;p&gt;目的：本研究目的為探討優秀女子排球選手在雙側膝關節伸展與屈曲動作肌力與肌力平衡特徵之差異。方法：以等速肌力測量儀檢測參與者雙側膝關節之力矩，研究參與者為 16 名大專公開一級女子排球選手。統計分析以成對樣本t-tset比較女子排球選手在雙側膝關節伸屈肌力矩之差異及傳統式腿後肌與四頭肌比值 (hamstring to quadriceps strength ratio, H/Q 比值) 與功能性 H/Q 比值的差異，顯著水準訂為&amp;alpha; ＝ .05。結果：排球選手在雙側膝關節的伸屈肌向心最大力矩中，只有在 60 &amp;deg;/s 時的伸肌未達顯著差異，其餘向心最大力矩皆有達顯著差異，且慣用側大於非慣用側；在雙側膝關節肌力之伸屈肌離心最大力矩與傳統式 H/Q 比值皆未達顯著差異，傳統式 H/Q 比值雖未達顯著差異，但都低於理想的安全值；在功能性比值方面，60 &amp;deg;/s 時伸、屈動作皆未達顯著差異，而 180 &amp;deg;/s 時伸展與屈曲動作則皆達到顯著差異，且非慣用側大於慣用側。結論：參與者應加強雙側的腿後肌群向心力矩，以提高傳統式 H/Q 比值，降低受傷的風險。功能性比值在伸展與屈曲動作的爆發力矩皆出現差異，這與排球攻擊動作落地時以非慣用側著地緩衝的特性有關。因此，應重視非慣用側的肌力訓練，以降低受傷的風險，更進一步提升選手攻擊的能力。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;Purpose: The purpose of this study was to investigate the differences in muscular strength and muscle balance characteristics in bilateral knee joint flexion-extension movements of elite female volleyball players. Methods: The study utilized the isokinetic dynamometer to measure the torque of the knee joint in 16 collegiate division Ⅰ female volleyball players. Statistical analysis was us-ing paired sample t-tests to compare the differences in knee joint flexion-extension torque, tradi-tional H/Q ratio(hamstring to quadriceps strength ratio, H/Q ratio) and functional H/Q ratio be-tween the dominant and non-dominant sides, with a significance level set at &amp;alpha; = .05. Results: The study found that in the concentric maximum torque of knee joint flexion-extension muscles, sig-nificant differences were observed at all angular velocities except for the extension muscle at 60&amp;deg;/s. The dominant side exhibited greater torque than the non-dominant side. However, there were no significant differences in eccentric maximum torque and traditional H/Q ratio of knee joint muscles. Although the traditional H/Q ratio did not show significant differences, it remained below the ideal safety value. Regarding functional H/Q ratio, there were no significant differences in both flexion and extension movements at 60&amp;deg;/s, while at 180&amp;deg;/s, significant differences were observed in both flexion and extension movements, with the non-dominant side exhibiting greater values than the dominant side. Conclusion: Participants should enhance the concentric force of the traditional H/Q ratio to reduce the risk of injury. Discrepancies in functional ratios are evident in the torque generated during extension and flexion movements, attributed to the characteristic landing pattern, which involves buffering on the non-dominant side during volleyball attacking actions. Therefore, prioritizing strength training on the non-dominant side is crucial to mitigate injury risks and further enhance athletes’ attacking capabilities.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Influence of Drying Conditions on the Durability of Concrete Subjected to the Combined Action of Chemical Attack and Freeze-Thaw Cycles.

The durability of concrete is critical for the service life of concrete structures, and it is influenced by various factors. This paper investigates the impact of the relative humidity (RH) of the curing environment on the durability of five different concrete types. The aim is to determine a suitable approach for designing concrete that is well-suited for use in the salt lake region of Inner Mongolia. The concrete types comprise ordinary Portland cement (OPC), high-strength expansive concrete (HSEC), high-strength expansive concrete incorporating silica fume, fly ash, and blast furnace slag (HSEC-SFB), steel fiber-reinforced high-strength expansive concrete (SFRHSEC), and high elastic modulus polyethylene fiber-reinforced high-strength expansive concrete (HFRHSEC). All these concrete types underwent a 180-day curing process at three distinct relative humidities (RH = 30%, 50%, and 95%) before being subjected to freeze-thaw cycles in the Inner Mongolia salt lake brine. The curing environment with a 95% RH is referred to as the standard condition. The experimental results reveal that the durability of OPC and HSEC decreases significantly with increasing relative humidity. In comparison with the control sample cured in 95% RH, the maximum freeze-thaw cycles for concrete cured in lower RHs are only 31% to 76% for OPC and 66% to 77% for HSEC. However, the sensitivity of the durability of HSEC-SFB, SFRHSEC, and HFRHSEC to variations in RH in the curing environment diminishes. In comparison with the corresponding reference value, the maximum freeze-thaw cycles for samples cured in dry conditions increase by 14% to 17% for HSEC-SFB and 21% for SFRHSEC. Specifically, the service life of HFRHSEC cured in a low RH is 25% to 46% higher than the reference value. The durability of HSEC-SFB, SFRHSEC, and HFRHSEC has been proven to be appropriate for structures located in the salt lake region of Inner Mongolia.

Crypto Analysis of the Key Distribution Scheme Using Noise-Free Resistances

Known key exchange schemes offering information-theoretic (unconditional) security are complex and costly to implement. Nonetheless, they remain the only known methods for achieving unconditional security in key exchange. Therefore, the explorations for simpler solutions for information-theoretic security are highly justified. Lin et al. [1] proposed an interesting hardware key distribution scheme that utilizes thermal-noise-free resistances and DC voltages. A crypto analysis of this system is presented. It is shown that, if Eve gains access to the initial shared secret at any time in the past or future, she can successfully crack all the generated keys in the past and future, even retroactively, using passively obtained and recorded voltages and currents. Therefore, the scheme is not a secure key exchanger, but it is rather a key expander with no more information entropy than the originally shared secret at the beginning. We also point out that the proposed defense methods against active attacks do not function when the original shared secret is compromised because then the communication cannot be efficiently authenticated. However, they do work when an unconditionally secure key exchanger is applied to enable the authenticated communication protocol.

Graph neural network‐based attack prediction for communication‐based train control systems

AbstractThe Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions. GNN‐AP is proposed, a framework utilising an alert log to predict potential attack targets. Firstly, GNN‐AP uses causality to eliminate confounding elements from the alert dataset and then uses an encoder‐decoder model to reconstruct an attack scenario graph. Based on the chronological characteristics of APT attacks, GNN‐AP identifies APT attack sequences from attack scenario graphs and integrates these attack sequences with communication‐based train control (CBTC) devices topology information to construct an Attack‐Target Graph. Based on the attack‐target graph, a graph neural network approach is used to identify the attack intent and transforms the attack prediction problem into a link prediction problem that predicts the connected edges of the attack and target nodes. The simulation results obtained using DARPA data show that the proposed method can improve the comparison methods by 4% of accuracy in terms of prediction. Furthermore, the method was applied to the CBTC system dataset with a prediction accuracy of 88%, demonstrating the efficacy of the proposed method for industrial control systems.

A deep learning-based authentication protocol for IoT-enabled LTE systems

The connected devices in Internet-of-Things (IoT)-enabled systems are continuously increasing nowadays, and likely to grow exponentially worldwide in near future. Hence, the next generation IoT-enabled mobile networks (e.g., 5G onward) are expected to provide higher system capacity and ultra-low latency to deal with. According to the Third Generation Partnership Project (3GPP), Long-Term Evolution (LTE) technology can serve the purpose efficiently, and also bridge the gap between earlier and future generation mobile networks. However, the network may face problems associated with privacy and security, as the underlying communication is mostly wireless. Thus, a secure and efficient Authentication and Key Agreement (AKA) protocol is desirable. Recently, many protocols have been proposed to address these goals. Unfortunately, the security and efficiency of such protocols are still in doubt. This paper introduces a deep learning-based AKA protocol for IoT-enabled LTE systems. The proposed protocol can address mutual authentication among the communicating entities. It employs a Deep Residual Network (DRN)-based key generation technique, called DRN-KeyGen, to establish a shared secret key on-the-fly among the communicating entities in such environment where the number of IoT devices are excessively large and extremely heterogeneous in nature. The security of DRN-KeyGen is verified considering various active and passive attacks that may occur in wireless-enabled LTE systems. The efficiency of DRN-KeyGen is measured through two different parameters: attack detection rate and attack detection time, where each attack is experimented using Python tool-based simulation with varied key lengths. The empirical results show that the proposed DRN-KeyGen can achieve an average detection rate of 0.924 and an average detection time of 30.634 s considering key lengths of 32 bits, 64 bits, 128 bits, 192 bits, 256 bits, and 512 bits. Finally, we have compared the security and efficiency of DRN-KeyGen with other existing protocols to show its superiority.

Dynamic Real-Time Analysis of Network Attacks Based on Dynamic Risk Probability Algorithm

With the rapid development of Internet technology and its application, the existence of network vulnerabilities is very common. Attackers may use the defects of software, hardware, or system security policy in the network system to access or destroy the system without authorization. How to nip in the bud and carry out a safety risk assessment and early warning is an urgent problem to be solved. Based on the overall assessment of the risk factors in the whole network, the more dangerous nodes are found and priority measures are taken. The method proposed in this paper can reflect and predict the actions of attackers, repair, and adjust the previously predicted probability. It is compared with the method that evaluates the uncertainty in the network solely by calculating the static probability. The proposed new ideas and methods better reflect the real-time changes in the actual environment of the Internet, thereby better responding to the actual situation. This method can be well applied to threat detection, threat analysis, and risk assessment of monitoring system networks, enabling monitoring network managers to evaluate and protect the security of real-time power grids. It is of great significance to effectively defend against network attacks, ensure system security, and study the resistance of control systems under network attacks.

Cu2-xS homojunction coatings empower titanium implants with near-infrared-triggered antibacterial and antifouling properties.

For decades, implant-associated infections (IAIs) caused by pathogenic bacteria have been associated with high failure and mortality rates in implantation surgeries, posing a serious threat to global public health. Therefore, developing a functionalized biomaterial coating with anti-fouling and anti-bacterial functions is crucial for alleviating implant infections. Herein, a near-infrared-responsive anti-bacterial and anti-adhesive coating (Ti-PEG-Cu2-xS) constructed on the surface of titanium (Ti) implants is reported. This coating is composed of nano-Cu2-xS with anti-bacterial activity and super-hydrophilic polyethylene glycol (PEG). Under near-infrared irradiation, the nano-catalyst Cu2-xS on the surface of Ti-PEG-Cu2-xS induces bacterial death by catalyzing the production of singlet oxygen (1O2). The Ti-PEG-Cu2-xS coating can effectively prevent bacterial adhesion and biofilm formation. This coating combines the antibacterial mechanisms of "active attack" and "passive defense", which can kill bacteria and inhibit biofilm formation. The results of in vitro and in vivo experiments have shown that Ti-PEG-Cu2-xS exhibits excellent anti-bacterial properties under near-infrared irradiation and can effectively prevent implant-related infections caused by Escherichia coli (E. coli) ATCC 8739 and Staphylococcus aureus (S. aureus). The antibacterial efficiency of Ti-PEG-Cu2-xS coatings against E. coli was 99.96% ± 0.058% and that of S. aureus was 99.66% ± 0.26%, respectively. In addition, the Ti-PEG-Cu2-xS coating has good blood compatibility and excellent bactericidal ability. Therefore, this multifunctional coating combines a non-adhesive surface strategy and a near-infrared phototherapy sterilization method, effectively blocking the initial attachment and proliferation of bacteria on implants via photothermal/photodynamic effects and providing a promising method for preventing bacterium-induced IAIs.

AN IMPROVED AND EFFICIENT RSA-BASED AUTHENTICATION SCHEME FOR HEALTHCARE SYSTEM

Owing to the fast advancements of wireless communication, the telehealthcare platform makes it possible for patients to access healthcare services online. However, creating a secure and efficient authentication scheme for healthcare systems still presents a challenge. Several solutions have been introduced, but the majority are shortly found to be unable to meet some essential security standards. In this paper, we first revisit Dharminder et al.'s scheme, and prove its failure to provide mutual authentication, and pacient’s untraceability, and its vulnerability to impersonation attacks. Furthermore, we suggest an improved RSA-based authentication scheme to mitigate the deficiencies observed in Dharminder et al.'s schema. The proposed scheme can provide mutual authentication, patients’ anonymity and untraceability, and resist various types of attacks. Extensive evaluation on AVISPA proves the safeness of the proposed scheme against both passive and active attacks. Additionally, the proposed scheme is computationally and communicationally more efficient in comparison to existing schemes.