Train control systems are critical for ensuring railway operational safety. With the advancement of railway intelligentization strategies and the rapid development of information technology, the train control system is evolving from closed to open systems. In this context, the security of the key management system(KMS) has become a core research direction, as the train-ground communication faces the risk of unauthorized access due to the use of open channels. Currently deployed KMS in industrial settings predominantly adopt offline architectures, which exhibit vulnerabilities to physical tampering during key storage and computation processes. Meanwhile, there is a general lack of research on the adaptation of mainstream online key management technologies (such as Hardware Security Modules (HSM), quantum key distribution(QKD), and Trusted Computing(TC)) in the context of rail train control systems. To address these challenges, this paper proposes an enhanced scheme based on an online key management mechanism. By integrating TC technology and optimizing the key distribution strategy, the scheme provides high-security protection throughout the full key lifecycle (generation, distribution, storage, and destruction). Experimental results demonstrate that, compared to traditional offline systems, the proposed solution significantly improves overall system security, resilience against attacks, and key update efficiency, thereby establishing a robust foundation for constructing highly secure modern railway train control systems.

The immutability of blockchain has exposed its limitations in adapting to rapidly evolving legal requirements and preventing malicious misuse. To address these issues, transaction-level redactable blockchain solutions based on the policy-based chameleon hash (PCH) have been introduced. These solutions allow users to create transactions and encrypt trapdoors under specific attribute policies. However, current transaction-level rewriting schemes face two security challenges: Firstly, transactions encrypted with the invalid trapdoor are difficult to rewrite; Secondly, due to lacking version detection on transactions, malicious modifiers may rollback the version of the transaction to launch a reversion attack. In this paper, we present a resilient and redactable blockchain (RRB) with 2-level rewriting and transaction version detection. Specifically, we propose a new redactable blockchain structure that supports both transaction-level and block-level rewriting. To tackle the invalid trapdoor problem, we propose two protocols: a fine-grained, controllable transaction-level rewriting protocol and a centrally controlled block-level rewriting protocol. Moreover, for the transaction reversion attack, we design a version detection mechanism for RRB by using an accumulator. Through security analysis and performance evaluation, we demonstrate the security and practicality of our RRB scheme.

In industrial cyber-physical systems (ICPSs), the strong coupling characteristics between the cyber and physical systems increase the complexity of security strategy decision-making. A trade-off exists in current methods: complex decision models hinder the fulfillment of real-time demands, whereas simplified coupling characteristics compromise strategy effectiveness. In recent years, digital twin (DT) technology has gained increasing attention in industrial security because of its high-fidelity modeling and real-time interaction capabilities. Motivated by this trend, we propose an integrated security strategy generation and optimization method for ICPSs that incorporates DT to tackle the challenge of balancing real-time performance with strategy reliability. Leveraging the high fidelity of the existing DT in ICPS, the coupling characteristics can be directly mapped to the virtual model, avoiding the cumbersome modeling operations. We first quickly generate an initial strategy space through a lightweight basic decision model. Subsequently, a DT-based closed-loop evaluation mechanism is introduced to facilitate rapid convergence toward the optimal strategy. It reduces computational complexity while preserving the authenticity of coupling characteristics, thereby enhancing the efficiency and accuracy of security decision-making. Experimental verification and analysis demonstrate that our method provides a feasible solution for efficient security decision-making in ICPSs.

Industrial Internet of Things (IIoT) enables smart factories, production, and logistics. However, any vulnerability in the network can lead to severe consequences for both industries and individuals. Being essential cybersecurity tools for IIoT, intrusion detection systems (IDS) play an important role in detecting network attacks. However, IDS can suffer from inaccuracy due to the rare nature of cyberattacks, a.k.a. sample imbalance. In this article, we introduce a transformer-based model termed aTtack-awaRe divide-And-ConquEr tRansformer (Tracer) for both anomaly detection and attack classification, which only needs network traffic data instead of content data. In particular,Tracerincorporates attack-aware learnable queries to enhance category-specific information. A hierarchical divide-and-conquer decoder is also designed tailored to these queries, which is effective in enhancing the accuracy of minority classes.Traceraims to detect complex, imbalanced traffic attacks without the need for data balancing samplers or separate classifiers.Tracerachieves remarkable 98.8% accuracy in anomaly detection on the UNSW-NB15 dataset, with 0.3% false alarm rate. It also reports multiclass attack accuracy of 86.02%, 96.17%, and 99.48% on the UNSW-NB15, Edge-IIoT, and CICIDS-2017 dataset, respectively, increasing the detection accuracy by about 1%–10%. The results suggest ourTracermodel shows potential to be an effective and easy-to-use solution for generic intrusion detection in IIoT.

Automated and semi-automated ships have developed rapidly in recent years, but various external communication links have introduced some external threats. To solve the unknown impact of multiple attacks on intelligent ships, a cellular automaton is proposed to model the risk propagation process. We identify risk assessment indicators from the perspective of attack-defense confrontation and analyze the risk propagation situation from six different angles. Based on the experimental results, we identify the system’s weak nodes and propose cyber security protection suggestions to reduce the loss caused by multisource attacks on the system.

As one of the typical Industrial Control System (ICS), railway control systems nowadays are faced with many security risks during its digital transformation empowered by various Information and Communications Technology (ICT), e.g., AI, 5G/6G. In addition to ensuring safety, the fundamental property of railway control system, it is important to conduct comprehensive security assessment during their design, development, deployment, and maintenance. But how to select and apply the most appropriate and efficient assessment methods is not straightforward and deserves careful studies. This paper firstly provides an in-depth analysis of the existing standards•1 for secure design and security assessment of railway control systems, in order to clarify the relationship between safety and security. It then comparatively studies the qualitative, quantitative, and simulation-based security assessment methods, along with their application scenarios, with an objective to obtaining an effective combination of these methods for railway control systems. By taking into account the specific security requirements and system characteristics of rail control systems, we finally propose a comprehensive security assessment framework for rail control systems.

Data sharing among vehicles can effectively address the traffic congestion and accidents caused by the increasing number of vehicles, thereby enhancing traffic efficiency and the travel experience. However, it also introduces security and privacy challenges related to confidentiality, authentication, identity privacy, identity revocation, and tamper resistance. To address the above challenges, we propose a bilateral access control data sharing scheme by extending Matchmaking Encryption. Our proposal ensures data confidentiality and data source authentication by combining attribute-based encryption with identity-based encryption. Most importantly, we verify the bilateral policies within a single logical step. To achieve identity revocation, we propose a revocation scheme based on a pseudo-identity list, which can revoke all the pseudo-identities associated with a malicious user and ensure the privacy of legitimate real identity. Security analysis indicates that apart from ensuring confidentiality and authentication, our proposal resists attacks, such as tampering, guessing, and collusion. We conduct theoretical complexity analysis and experimental performance evaluations to demonstrate the efficiency and practicality of our proposal for Internet of Vehicles (IoV) data sharing.

5G industrial cyber–physical systems (5G-ICPSs) have attracted substantial research interests due to their capability in the interconnection of everything. However, integrating the 5G network may expose systems to more potential risks. To reveal attack propagation, an attack path prediction approach based on dual reinforcement learning (RL) is proposed. First, a dual-network model is established, incorporating the security constraints for attacks against the 5G network into the attack graph. Second, employing RL,$Q $-value updating functions and reward mechanisms based on topology and vulnerability are designed. Finally, an optimal attack path prediction algorithm is developed. Unlike traditional methods, the proposed approach does not rely on the monotonicity assumption that a system component has only one vulnerability, enabling it to accurately predict the optimal attack paths. Our simulation results demonstrate that the proposed approach can identify possible attack sources and paths from a 5G-ICPS.

Ring Confidential Transactions (RingCT) is a typical privacy-preserving protocol for blockchain, which is used for the most popular anonymous cryptocurrency Monero in recent years. RingCT provides the user's identity anonymity based on the linkable ring signature. At the cost of that, the transaction size is increased linearly to the involved users. In this article, we aim to overcome this inefficient aspect of RingCT by introducing the linkable threshold ring signature (LTRS). We first propose a construction of threshold ring signatures for homomorphic cryptosystems, and present an efficient instantiation based on the intractability assumption of the discrete logarithm problem. Based on this framework, an efficient LTRS scheme and a novel construction of the RingCT protocol are presented. Our proposed RingCT protocol enables multiple payers to co-construct an anonymous transaction without revealing their secret account keys, and it is more concise under multiple input accounts. For a transaction with a ring size of 100 and the input accounts number of 64, the communication overhead is about 4% of the original RingCT protocol.

BACKGROUND: As one of the fundamental problems in bioinformatics, the double digest problem (DDP) focuses on reordering genetic fragments in a proper sequence. Although many algorithms for dealing with the DDP problem were proposed during the past decades, it is believed that solving DDP is still very time-consuming work due to the strongly NP-completeness of DDP. However, none of these algorithms consider the privacy issue of the DDP data that contains critical business interests and is collected with days or even months of gel-electrophoresis experiments. Thus, the DDP data owners are reluctant to deploy the task of solving DDP over cloud. RESULTS: Our main motivation in this paper is to design a secure outsourcing computation framework for solving the DDP problem. We at first propose a privacy-preserving outsourcing framework for handling the DDP problem by using a cloud server; Then, to enable the cloud server to solve the DDP instances over ciphertexts, an order-preserving homomorphic index scheme (OPHI) is tailored from an order-preserving encryption scheme published at CCS 2012; And finally, our previous work on solving DDP problem, a quantum inspired genetic algorithm (QIGA), is merged into our outsourcing framework, with the supporting of the proposed OPHI scheme. Moreover, after the execution of QIGA at the cloud server side, the optimal solution, i.e. two mapping sequences, would be transferred publicly to the data owner. Security analysis shows that from these sequences, none can learn any information about the original DDP data. Performance analysis shows that the communication cost and the computational workload for both the client side and the server side are reasonable. In particular, our experiments show that PP-DDP can find optional solutions with a high success rate towards typical test DDP instances and random DDP instances, and PP-DDP takes less running time than DDmap, SK05 and GM12, while keeping the privacy of the original DDP data. CONCLUSION: The proposed outsourcing framework, PP-DDP, is secure and effective for solving the DDP problem.

As one of the crucial features of the blockchain technique, immutability plays the most important role in winning the so-called praise of the “trust machine” for blockchain. However, there are two sides to everything. The property of immutability of blockchain is applied maliciously sometimes, say publishing harmful or even dangerous data and hindering authorities’ law enforcement. To address this issue, authorized redactability of blockchain was introduced to support block modification without lowering the fundamental basis of security and trust that is cherished on the blockchain. During the past years, several techniques of redactable blockchain were proposed, mainly based on the well-known chameleon hashing. Different from existing methodologies, we propose a new redactable blockchain scheme for permissioned settings in this article. We first employ the trapdoor verifiable delay function to attach a time-lapse proof to each block. Moreover, the trapdoor is used to quickly construct a chain fork to redact blocks that are authorized to alter. Our proposal does not need to rollback irrelevant blocks. As a remarkable and unique feature, our proposal realizes the property of strong synchronization of redaction, which means that all nodes in the blockchain will have identical views on the chain even after some blocks are altered. Security analysis shows that the consistency of the chain is guaranteed, and the long-range attack can be resisted effectively. The performance comparison shows that our method is feasible and practical.

Advanced cyber-physical power systems (CPPS) has been put forward by the strong integration of energy networks and communication networks. While CPPS brings a promising solution with high efficiency, strong flexibility, great scalability, and improved reliability, it inevitably poses some security challenges. In order to address these challenges, it is essential to accurately describe the attack behavior and system security situation. In this article, a dynamic risk propagation evaluation approach is proposed for accurately predicting attacks and quantitatively analyzing system risk. It is equipped with a partitioned cellular automata model to deal with spatial heterogeneity in the partitioned system. The intentions of targeted attack are also considered for predicting attacks. Then, the cyber-to-physical risk is quantitatively identified from multiple dimensions. Finally, the verification of attack intention is designed to dynamically update and adjust the predicted result. The presented approach is demonstrated through a case study on a CPPS.

Cyber attacks threaten the security of cyber-physical systems (CPSs) seriously. Resilient control has been studied to defend cyber attacks. However, existing resilient control schemes have not considered system structure changes caused by actuator attacks. Such structure changes are more destructive and harmful than the actuator attack scenarios investigated in the literature, demanding new resilient control strategies. They will be addressed in this article in cloud computing environments, which are increasingly deployed in large-scale CPSs. More specifically, a resilient control scheme is designed which consists of two controllers: a local resilient controller and cloud-based resilient controller. The local resilient controller withstands actuator attacks that simply tampers the actuator output to a large extent. The cloud-based resilient controller aims to resist the actuator attacks that destroy the system structure. Simulations are conducted on a permanent synchronous motor control system to demonstrate the proposed resilient control scheme.

5G communication network has become a major pillar in the evolution of interconnected industrial systems. However, the introduction of 5G network may lead to unknown risks in the systems. To reveal the impact of network threats on 5G-based industrial systems, a 5G network security analysis approach combining formal modeling and attack penetration is proposed. Firstly, the 5G network models based on topology and transmission events are established to cope with diverse and hidden attack routes and behaviors. Then, the attack module is integrated into the network model. With attack penetration to the models, potential vulnerabilities are exploited and quantified based on the hierarchical-topology model, and network reliability is evaluated based on the transmission-event model. The simulation results identify and quantify network vulnerabilities under various attacks, including access authentication failure, destruction of data integrity, illegal control of Network Functions (NFs), and malicious consumption of shared slicing resources. Meanwhile, a more unpredictable outcome is that there is a threshold of access probability,$\alpha $, to measure the impacts of attacks against the bearer network and core network on reliability. Finally, a practical case about the impact of network security on a 5G-based coupled-tank system is discussed, which further proves the feasibility of our approach.

Resilient control of cyber-physical systems (CPSs) against actuator and/or sensor attacks has been extensively researched. However, the existing research considers actuator attacks and sensor attacks separately and also designs resilient controllers based on complex nonlinear system models caused by unknown actuator and sensor attacks. This increases the difficulty in the analysis, computation, and control of CPSs under attacks. To address this issue, this article introduces an idea to deal with both actuator attacks and sensor attacks together with feedback linearization control. This simplifies the mathematical modeling of attacked CPSs, thus reducing the difficulty of resilient controller design. Then, from the simplified modeling, a composite controller is designed to enhance system resilience. It ensures the dynamic and steady-state performance of CPSs under attacks. Simulation studies are undertaken to demonstrate the effectiveness of the proposed method.

Hop count distribution (HCD), generally formulated as a discrete probability distribution of the hop count, constitutes an attractive tool for performance analysis and algorithm design. This paper devotes to deriving an analytical HCD expression for a finite ad hoc network under the minimum hop-count routing protocols. Formulating the node distribution with binomial point process, the network is provided as a bounded area with all nodes randomly and uniformly distributed. Considering an arbitrary pair of source node (SN) and destination node, an innovative and straightforward definition is presented for HCD. In order to derive HCD out, an original mathematical framework, named as the equivalent area replacement method (EARM), is proposed and verified. Under the EARM, HCD is derived by first considering the special case where SN locates at the network center and then extending to the general case where SN is randomly distributed. For each case, the accuracy of our HCD model is evaluated by simulation comparison. Results show that our model matches well with the simulation results over a wide range of parameters. Particularly, the derived HCD outperforms the existing formulations in terms of the Kullback Leibler divergence, especially when SN is randomly distributed.

Multimicrogrid (MMG) systems play an increasingly important role in the smart grid. They come with various potential cyberattacks, which may cause power supply interruption or even human casualties. Therefore, decision-making for timely mitigation of cyberattack risks is highly desirable in the security protection of power systems. However, there is a lack of effective decentralized decision-making strategies that are able to deal with MMG scenarios through distributed consensus. To address this issue, a decentralized consensus decision-making (DCDM) approach is proposed in this article for the security of MMG systems. It achieves decentralized consensus without the need of a trusted authority or central server, making it distinct from existing consensus methods. Meanwhile, it guarantees the consistency and nonrepudiability of consensus results, which are stored on the blockchain in sequence. In each of the distributed agents, the approach consists of a fuzzy static Bayesian game model (FSB-GM) to determine the optimal security strategy and a hybrid consensus algorithm to achieve consensus. The FSB-GM considers the fuzzy preferences of different types of attackers and defenders. The hybrid consensus algorithm is implemented by the fusion improvement of two consensus mechanisms in the blockchain. The effectiveness of the presented approach is demonstrated through a case study on an MMG system.

In recent decades, much attention has been paid to timely and guaranteed delivery in industrial automation networks. Toward this aim, the IEEE 802.1 Time-Sensitive Networking (TSN) task group has developed a series of standards. IEEE 802.1AS Timing and Synchronization protocol is the basis for TSN flow control mechanisms. As a rather new protocol, modeling and security analysis is a highly attractive candidate for developing IEEE 802.1AS. In this paper, we model the IEEE 802.1AS using Hierarchical Colored Petri Nets (HCPNs) and verify the proposed model by state space analysis and synchronization performance analysis. On the basis of our model, the security of the protocol is analyzed, including attack and defense against IEEE 802.1AS. Simulation results verify the validity and practicability of the model.

Underwater optical wireless sensor networks (UOWSNs) have been attracting many interests for the advantages of high transmission rate, ultrawide bandwidth, and low latency. However, due to limited energy resources and highly dynamic topology caused by the water flow movement, it is challenging to provide a low-consumption and reliable routing in UOWSNs. To tackle this issue, in this article, we propose an efficient routing protocol based on multiagent reinforcement learning, termed as DMARL, for UOWSNs. The network is first modeled as a distributed multiagent system, and residual energy and link quality are considered into the routing protocol design to improve the adaptation to a dynamic environment and the support of prolonging network life. Additionally, two optimization strategies are proposed to accelerate the convergence of the reinforcement learning algorithm. On the basis, a reward mechanism is provided for the distributed system. The simulation results show that the DMARL-based routing protocol has low energy consumption and high packet delivery ratio (over 90%), and it is suitable for networks where the average number of neighbor nodes is less than 14.

Much attention has been paid to underwater optical wireless sensor networks with the characteristics of high transmission rate and low delay for high-bandwidth underwater applications. However, several issues may take place and hinder the routing of underwater optical communication nodes due to the highly dynamic topology caused by the ocean current movement. On the purpose of addressing the problem and enhancing the robustness of dynamic network, in this paper, we propose a novel routing protocol, based on multi-agent reinforcement learning (MARL) for underwater optical sensor networks. The network is firstly modeled as a multi-agent system and the protocol based on reinforcement learning algorithm is designed to realize dynamic route selection by information interacting between adjacent nodes and maximize the network lifetime. The simulation results demonstrate that MARL has lower energy consumption and higher delivery ratio (about 95%) in a dynamic topology than the existing Q-learning, QDTR and AODV routing protocols.

Information sharing solves the problem of information asymmetry between banks to a certain extent, but it also brings about the leakage of customer privacy. Therefore, how to protect data privacy while sharing data is an urgent issue. Blockchain protects the privacy of accounts and data while sharing data. Therefore, we propose a blockchain-based loan over-prevention mechanism. The program can hide the customer loan/repayment amount and thus protect the customer's privacy; we prove that the customer's loan amount is within a certain range and does not exceed the remaining loanable amount via a range proof. In order to share customer loan information between banks, we store the commitment of customers' amounts in the blockchain, and banks can obtain relevant information directly from the chain, which also reduces the amount of communication between banks. Finally, we implement the model and performe a performance analysis.

Collisions caused by the hidden terminal effects may result in severe packet corruption and performance degradation in multihop IEEE 802.15.4-compliant wireless sensor networks (WSNs). In order to avoid such collisions through scheduling protocols, it is important to first recognize these collisions by distinguishing them from some other noncollision cases (e.g., path loss, multipath fading, shadow fading, and IEEE 802.11 interference), which may also lead to similar consequences. In this paper, we focus on the collision recognition problem in multihop IEEE 802.15.4-compliant WSNs. First, through a series of measurements of the error properties in various collision and noncollision scenarios, we investigate the statistical behaviors of error patterns including the bit error rate and error position distribution, which reveal obvious differences between collision and noncollision cases in terms of bit- and symbol-level error position distribution. Based on these observations, we further propose a machine learning-based collision recognition mechanism by inserting some redundant blocks in a data frame. The inserted blocks are known to both the sender and receiver, thereby it enables the receiver to recognize the error patterns only according to the redundant sequences. Moreover, a mutual information-guided byte selection technique is also provided to effectively improve the recognition accuracy. Finally, the proposed mechanism is verified under three different transmission environments. The experimental results show that the proposed mechanism achieves good recognition accuracy over 90% with 94% coding efficiency.

As a promising standard to realize low data-rate, low-power, and short-range communications among wireless devices, IEEE 802.15.4 has been widely applied in wireless sensor networks, and also been intensively investigated in vehicle-toinfrastructure (V2I) communications. In this paper, we analyze the effects of antenna height on IEEE 802.15.4 V2I communications in rural areas. In particular, we first propose a geometry-based piecewise model to quantify the variable foliage effects. Furthermore, we show the feasibility of extending foliage loss predictions from static to mobile cases with classical empirical methods. Simulation results verify the precision of our proposed channel model in rural areas by comparing it with other channel models and realistic measurements.

IEEE 802.15.4 is a promising standard that can provide efficient communication quality at low cost and low data rates. This standard has been studied for vehicle-to-infrastructure (V-I) communication in wireless sensor network (WSN)-based vehicular ad hoc network (VANET) applications. To adopt this standard for vehicular communications, link quality metrics must be investigated for topology design and network optimization. In this study, a series of measurements were made with IEEE 802.15.4 radios to identify wireless channel characteristics with regard to antenna height, vehicle velocity, and distance in various vehicular scenarios. The empirical behavior of link communication quality was also investigated in terms of received signal strength, packet error rate (PER), and packet loss distribution (PLD). The empirically measured and experimental results, which have been validated through appropriate analytical modeling, provide valuable insights, as well as guide design decisions and tradeoffs for WSN-based VANET applications.

This paper presents a memory crossbar based on two serial memristors with threshold characteristic to eliminate the effect of sneak paths, which is a key issue in crossbar memory system leading to great degradation in their performance and power efficiency. At first, we analyze the threshold characteristic of memristor and propose a memristor model with threshold. Based on this model, the paper presents the design and simulation of a non-volatile memory system utilizing two serial memristors with different polarities as a memory cell. This scheme solves the sneak-path problem by taking advantage of the threshold characteristic and the performance with having always high resistance state in all the memory cells, which is validated by simulation results. The scheme also possesses the superior properties of remarkable compatibility and high density.

The rapid progress in the research and development of electronics, sensing, signal processing, and communication networks has significantly advanced the state of applications of intelligent transportation systems (ITSs). However, efficient and low-cost methods for gathering information in large-scale roads are lacking. Consequently, wireless sensor network (WSN) technologies that are low cost, low power, and self-configuring are a key function in ITS. The potential application scenarios and design requirements of WSN for urban transportation (WSN-UT) are proposed in this work. A customized network topology is designed to meet the special requirements, and WSN-UT is specifically tailored for UT applications. WSN-UT enables users to obtain traffic and road information directly from the local WSN within its wireless scope instead of the remote ITS data center. WSN-UT can be configured according to different scenario requirements. A three-level subsystem and a configuration and service subsystem constitute the WSN-UT network frame, and the service/interface and protocol algorithms for every subsystem level are designed for WSN-UT.

Vehicle electrification is envisioned to be a significant component of the forthcoming smart grid. In this paper, a smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles; 2) infrastructure; 3) travelers; 4) systems, operations, and scenarios; 5) communications; and 6) social, economic, and political.