Biblio

In this paper, we consider a wireless federated learning (FL) system concerning differential privacy (DP) guarantee, where multiple edge devices collaboratively train a shared model under the coordination of a central base station (BS) through over-the-air computation (AirComp). However, due to the heterogeneity of wireless links, it is difficult to achieve the optimal trade-off between model privacy and accuracy during the FL model aggregation. To address this issue, we propose to utilize the reconfigurable intelligent surface (RIS) technology to mitigate the communication bottleneck in FL by reconfiguring the wireless propagation environment. Specifically, we aim to minimize the model optimality gap while strictly meeting the DP and transmit power constraints. This is achieved by jointly optimizing the device transmit power, artificial noise, and phase shifts at RIS, followed by developing a two-step alternating minimization framework. Simulation results will demonstrate that the proposed RIS-assisted FL model achieves a better trade-off between accuracy and privacy than the benchmarks.

In order to guarantee the normal operation of the power Internet of things devices, the zero-trust idea was used for studying the security protection strategies of devices from four aspects: user authentication, equipment trust, application integrity and flow baselines. Firstly, device trust is constructed based on device portrait; then, verification of device application integrity based on MD5 message digest algorithm to achieve device application trustworthiness. Next, the terminal network traffic baselines are mined from OpenFlow, a southbound protocol in SDN. Finally, according to the dynamic user trust degree attribute access control model, the comprehensive user trust degree was obtained by weighting the direct trust degree. It obtained from user authentication and the trust degree of user access to terminal communication traffic. And according to the comprehensive trust degree, users are assigned the minimum authority to access the terminal to realize the security protection of the terminal. According to the comprehensive trust degree, the minimum permissions for users to access the terminal were assigned to achieve the security protection of the terminal. The research shows that the zero-trust mechanism is applied to the terminal security protection of power Internet of Things, which can improve the reliability of the safe operation of terminal equipment.

The emergence of the Internet of Things (IoT) is not only a global revolution in the information industry, but also brought tremendous changes to our lives. With the development of the technology and means of the IoT, information security issues have gradually emerged, and intrusion attacks have become one of the main problems of the IoT network security. The network layer of the IoT is the key connecting the platform and sensors or controllers of the IoT, and it is also the most standardized, the strongest and the most mature part of the whole physical network architecture. Its large-scale development has led to the network layer's security issues will receive more attention and face more challenges. This paper proposes an intrusion detection algorithm deployed on the network layer of the IoT, which uses the BPSO algorithm to extract features from the NSL-KDD dataset, and applies support vector machines (SVM) as the core model of the algorithm to detect and identify abnormal data, especially DoS attacks. Experimental results show that the model's detection rate of abnormal data and DoS attacks are significantly improved.

Network function virtualization (NFV / VNF) and information-centric networking (ICN) are two trending technologies that have attracted expert's attention. NFV is a technique in which network functions (NF) are decoupling from commodity hardware to run on to create virtual communication services. The virtualized class nodes can bring several advantages such as reduce Operating Expenses (OPEX) and Capital Expenses (CAPEX). On the other hand, ICN is a technique that breaks the host-centric paradigm and shifts the focus to “named information” or content-centric. ICN provides highly efficient content retrieval network architecture where popular contents are cached to minimize duplicate transmissions and allow mobile users to access popular contents from caches of network gateways. This paper investigates the implementation of NFV in ICN. Besides, reviewing and discussing the weaknesses and strengths of each architecture in a critical analysis manner of both network architectures. Eventually, highlighted the current issues and future challenges of both architectures.

Cyber-Physical Systems (CPS) tightly couple information technology with physical processes, which rises new vulnerabilities such as physical attacks that are beyond conventional cyber attacks. Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This issue is even emphasized with the increasing autonomy in CPS. While this fact has motivated many defense mechanisms against sensor attacks, a clear vision on the timing and usability (or the false alarm rate) of attack detection still remains elusive. Existing works tend to pursue an unachievable goal of minimizing the detection delay and false alarm rate at the same time, while there is a clear trade-off between the two metrics. Instead, we argue that attack detection should bias different metrics when a system sits in different states. For example, if the system is close to unsafe states, reducing the detection delay is preferable to lowering the false alarm rate, and vice versa. To achieve this, we make the following contributions. In this paper, we propose a real-time adaptive sensor attack detection framework. The framework can dynamically adapt the detection delay and false alarm rate so as to meet a detection deadline and improve the usability according to different system status. The core component of this framework is an attack detector that identifies anomalies based on a CUSUM algorithm through monitoring the cumulative sum of difference (or residuals) between the nominal (predicted) and observed sensor values. We augment this algorithm with a drift parameter that can govern the detection delay and false alarm. The second component is a behavior predictor that estimates nominal sensor values fed to the core component for calculating the residuals. The predictor uses a deep learning model that is offline extracted from sensor data through leveraging convolutional neural network (CNN) and recurrent neural network (RNN). The model relies on little knowledge of the system (e.g., dynamics), but uncovers and exploits both the local and complex long-term dependencies in multivariate sequential sensor measurements. The third component is a drift adaptor that estimates a detection deadline and then determines the drift parameter fed to the detector component for adjusting the detection delay and false alarms. Finally, we implement the proposed framework and validate it using realistic sensor data of automotive CPS to demonstrate its efficiency and efficacy.

The data revolution will continue in the near future and move from centralized big data to "small" datasets. This trend stimulates the emergence not only new machine learning methods but algorithms for processing data at the point of their origin. So the Compressed Sensing Problem must be investigated in some technology fields that produce the data flow for decision making in real time. In the paper, we compare the random and constant frequency deviation and highlight some circumstances where advantages of the random deviation become more obvious. Also, we propose to use the differential transformations aimed to restore a signal form by discrets of the differential spectrum of the received signal. In some cases for the investigated model, this approach has an advantage in the compress of information.

Knowledge Graph can describe the concepts in the objective world and the relationships between these concepts in a structured way, and identify, discover and infer the relationships between things and concepts. It has been developed in the field of medical and health care. In this paper, the method of natural language processing has been used to build chronic disease knowledge graph, such as named entity recognition, relationship extraction. This method is beneficial to forecast analysis of chronic disease, network monitoring, basic education, etc. The research of this paper can greatly help medical experts in the treatment of chronic disease treatment, and assist primary clinicians with making more scientific decision, and can help Patients with chronic diseases to improve medical efficiency. In the end, it also has practical significance for clinical scientific research of chronic disease.

Blockchain started with Bitcoin, but it is higher than Bitcoin. With the deepening of applied research on blockchain technology, this new technology has brought new vitality to many industries. People admire the decentralized nature of the blockchain and hope to solve the problems caused by the operation of traditional centralized institutions in a more fair and effective way. Of course, as an emerging technology, blockchain has many areas for improvement. This article explains the blockchain technology from many aspects. Starting from the typical architecture of the blockchain, the data structure and system model of the blockchain are first introduced. Then it expounds the development of consensus algorithms and compares typical consensus algorithms. Later, the focus will be on smart contracts and their application platforms. After analyzing some of the challenges currently faced by the blockchain technology, some scenarios where the blockchain is currently developing well are listed. Finally, it summarizes and looks forward to the blockchain technology.

Smart energy management system in smart grid carries a large number of sensitive data, which needs encryption algorithm to ensure the security of system communication. At present, most of the terminal devices of smart grid are embedded devices with limited computing resources, and their communication encryption mostly relies on AES encryption algorithm. It is difficult in key management and key distribution. Therefore, this paper proposes an improved ECC-AES hybrid encryption algorithm. Firstly, ECC algorithm is improved to improve the speed of encryption and decryption, and then the improved ECC algorithm is used as a supplement to AES algorithm. ECC is used to encrypt the AES key, which improves the security of the algorithm. At the same time, the experimental simulation also proves that the improved ECC algorithm has obvious performance improvement in computing time, CPU occupancy and memory usage.

Cyber-physical systems, which are ubiquitous in modern critical infrastructure, oftentimes rely on sending actuation commands and sensor measurements over a network, subjecting this information to potential man-in-the-middle attacks. These attacks can take the form of denial of service attacks or integrity attacks. Previous approaches at ensuring the resiliency of the overall control system against these types of attacks have leveraged functional redundancy in the system, including resilient estimation and reconfigurable control. However, these approaches are only able to ensure resiliency up to a particular subset of the actuator commands and sensor measurements being compromised. In contrast, we introduce a resiliency mechanism in this paper that can ensure safety for the overall system when all the actuator commands and sensor measurements are compromised. In addition, this approach does not require the implementation of any detection algorithm. We leverage communication redundancy in the number of pathways across the network to guarantee safety when up to a certain percentage of those pathways are compromised. The conditions under which safety is guaranteed are presented along with the resiliency mechanism itself, and our results are illustrated via simulation.

Regular expression denial of service (ReDoS)— which exploits the super-linear running time of matching regular expressions against carefully crafted inputs—is an emerging class of DoS attacks to web services. One challenging question for a victim web service under ReDoS attacks is how to quickly recover its normal operation after ReDoS attacks, especially these zero-day ones exploiting previously unknown vulnerabilities.In this paper, we present RegexNet, the first payload-based, automated, reactive ReDoS recovery system for web services. RegexNet adopts a learning model, which is updated constantly in a feedback loop during runtime, to classify payloads of upcoming requests including the request contents and database query responses. If detected as a cause leading to ReDoS, RegexNet migrates those requests to a sandbox and isolates their execution for a fast, first-measure recovery.We have implemented a RegexNet prototype and integrated it with HAProxy and Node.js. Evaluation results show that RegexNet is effective in recovering the performance of web services against zero-day ReDoS attacks, responsive on reacting to attacks in sub-minute, and resilient to different ReDoS attack types including adaptive ones that are designed to evade RegexNet on purpose.

Under the background of increasingly severe security situation, the new working mode of power mobile internet business anytime and anywhere has greatly increased the complexity of network interaction. At the same time, various means of breaking through the boundary protection and moving laterally are emerging in an endless stream. The existing boundary-based mobility The security protection architecture is difficult to effectively respond to the current complex and diverse network attacks and threats, and faces actual combat challenges. This article first analyzes the security risks faced by the existing power mobile Internet services, and conducts a collaborative analysis of the key points of zero-trust based security protection from multiple perspectives such as users, terminals, and applications; on this basis, from identity security authentication, continuous trust evaluation, and fine-grained access The dimension of control, fine-grained access control based on identity trust, and the design of a zero-trust-based power mobile interconnection business security protection framework to provide theoretical guidance for power mobile business security protection.

In order to solve the problem of difficult verification of query results in searchable encryption, we used the idea of Shamir-secret sharing, combined with game theory, to construct a randomly verifiable multi-cloud server searchable encryption scheme to achieve the correctness of the query results in the cloud storage environment verify. Firstly, we using the Shamir-secret sharing technology, the encrypted data is stored on each independent server to construct a multi-cloud server model to realize the secure distributed storage and efficient query of data. Secondly, combined with game theory, a game tree of query server and verification server is constructed to ensure honesty while being efficient, and solve the problem of difficulty in returning search results to verify under the multi-cloud server model. Finally, security analysis and experimental analysis show that this solution effectively protects data privacy while significantly reducing retrieval time.

With the advancement of network physical social system (npss), a large amount of data privacy has become the targets of hacker attacks. Due to the complex and changeable attack methods of hackers, network security threats are becoming increasingly severe. As an important type of active defense, honeypots use the npss as a carrier to ensure the security of npss. However, traditional honeynet structures are relatively fixed, and it is difficult to trap hackers in a targeted manner. To bridge this gap, this paper proposes a recommendation method for LSTM prediction trap components based on attention mechanism. Its characteristic lies in the ability to predict hackers' attack interest, which increases the active trapping ability of honeynets. The experimental results show that the proposed prediction method can quickly and effectively predict the attacking behavior of hackers and promptly provide the trapping components that hackers are interested in.

Redaction of personal, confidential and sensitive information from documents is becoming increasingly important for individuals and organizations. In past years, there have been many well-publicized cases of data leaks from various popular companies. When the data contains sensitive information, these leaks pose a serious threat. To protect and conceal sensitive information, many companies have policies and laws about processing and sanitizing sensitive information in business documents.The traditional approach of manually finding and matching millions of words and then redacting is slow and error-prone. This paper examines different models to automate the identification and redaction of personal and sensitive information contained within the documents using named entity recognition. Sensitive entities example person’s name, bank account details or Aadhaar numbers targeted for redaction, are recognized based on the file’s content, providing users with an interactive approach to redact the documents by changing selected sensitive terms.

With the development of cloud computing, remote attestation of virtual machines has received extensive attention. However, the current schemes mainly concentrate on the single prover, and the attestation of a large-scale virtualization environment will cause TPM bottleneck and network congestion, resulting in low efficiency of attestation. This paper proposes CloudTA, an extensible remote attestation architecture. CloudTA groups all virtual machines on each cloud server and introduces an integrity measurement group (IMG) to measure virtual machines and generate trusted evidence by a group. Subsequently, the cloud server reports the physical platform and VM group's trusted evidence for group verification, reducing latency and improving efficiency. Besides, CloudTA designs a hybrid high concurrency communication framework for supporting remote attestation of large-scale virtual machines by combining active requests and periodic reports. The evaluation results suggest that CloudTA has good efficiency and scalability and can support remote attestation of ten thousand virtual machines.

In order to meet the requirements of secure start-up of embedded devices, this paper designs a secure and trusted circuit to realize the secure and trusted start-up of the system. This paper analyzes the principle and method of the circuit design, and verifies the preset information of the embedded device before the start of the embedded device, so as to ensure that the start process of the embedded device is carried out according to the predetermined way, and then uses the security module to measure the integrity of the data in the start process, so as to realize a trusted embedded system. The experimental results show that the security module has stronger security features and low latency. The integrity measurement is implemented in the trusted embedded system to realize the safe startup of embedded devices.

As the China government already putting forward the strategic objectives to peak carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060, social consensus of green low carbon has promoted the development of integrated energy services. As an emerging format, integrated energy services break the trade and technical barriers between different varieties of energy. As a carrier of integrated energy services, integrated energy service companies still have many problems in their own optimized operation. This paper studies the risk mechanism of energy chain considering the risk preference of energy service companies, and analyses the correlation between the risk preference of energy service companies and the risk of energy chain. Based on set pair theory and interval number, the paper establishes an energy chain risk assessment model to overcome the shortcomings of traditional evaluation methods, which is able to characterize risk appetite and uncertainties. Finally, the results of simulation and tests verify the effectiveness of the proposed method of the novel.

Due to the large number of terminal nodes and wide deployment of power IOT, it is vulnerable to attacks such as physical hijacking, communication link theft and replay. In order to sense and measure the security risks and threats of massive power IOT terminals in real time, a security threat assessment for power IOT terminals based on knowledge graph was proposed. Firstly, the basic data, operation data and alarm threat data of power IOT terminal equipment are extracted and correlated, and the power IOT terminal based on knowledge graph is constructed. Then, the real-time monitoring data of the power IOT terminal is preprocessed. Based on the knowledge graph of the power IOT terminal, the safety analysis and operation analysis of the terminal are carried out, and the threat index of the power IOT terminal is perceived in real time. Finally, security operation and maintenance personnel make disposal decisions on the terminals according to the threat index of power IOT terminals to ensure the safe and stable operation of power IOT terminal nodes. The experimental results show that compared with the traditional IPS, the method can effectively detect the security threat of the power IOT terminal and reduce the alarm vulnerability rate.

Although DC microgrids using a distributed cooperative control architecture can avoid the instability or shutdown issues caused by a single-point failure as compared to the centralized approach, limited global information in the former makes it difficult to detect cyber attacks. Here, we present a false data injection attack (FDIA)–-termed as a local control input attack–-targeting voltage observers in the secondary controllers and control loops in the primary controllers. Such an attack cannot be detected by only observing the performance of the estimated voltage of each agent, thereby posing a potential threat to the system operation. To address this, a detection method using the outputs of the voltage observers is developed to identify the exact location of an FDIA. The proposed approach is based on the characteristics of the distributed cooperative network and avoids heavy dependency on the system model parameters. Next, an event-driven mitigation approach is deployed to substitute the attacked element with a reconstructed signal upon the detection of an attack. Finally, the effectiveness of the proposed resilient scheme is validated using simulation results.

With the continuous development of world economy, the trade and connection between countries are getting closer, in which ports are playing an increasingly important role. However, due to the inherent complexity of port operational environment, ports are exposed to various types of hazards and more likely to encounter risks with high frequency and serious consequences. Therefore, proper and effective risk management of ports is particularly essential and necessary. In this research, literature from three aspects including risk assessment of port operations and service, safety management of dangerous goods, and port supply chain risk management was collected and investigated, in order to put forward the future research direction related to the risk management of port operations. The research results show that, firstly, most of the current research mainly focuses on the operational risk of traditional ports and a lot of relevant achievements have been seen. However, few scholars have studied the risk issues of smart ports which are believed to be the trend of future with the rapid development and application of high and new technologies. Thus, it is suggested that more attention should be shifted to the identification and assessment of operational risks of smart ports considering their characteristics. Secondly, although the risk evaluation systems of port operational safety have been established and widely studied, more efforts are still needed in terms of the suitability and effectiveness of the proposed indicators, especially when dangerous goods are involved. Thirdly, risk management of port supply chain is another popular topic, in which, one of the main difficulties lies on the collection of risk related statistics data due to the fact that port supply chain systems are usually huge and complex. It is inevitably that the evaluation results will lack objectivity to some extent. Therefore, it calls for more research on the risk assessment of port supply chains in a quantitative manner. In addition, resilience, as an emerging concept in the transportation field, will provide a new angle on the risk management of port supply chains.

The increasing volume of domestic and foreign trade brings new challenges to the efficiency and safety supervision of transportation. With the rapid development of Internet technology, it has opened up a new era of intelligent Internet of Things and the modern marine Internet of Vessels. Radio Frequency Identification technology strengthens the intelligent navigation and management of ships through the unique identification function of “label is object, object is label”. Intelligent Internet of Vessels can achieve the function of “limited electronic monitoring and unlimited electronic deterrence” combined with marine big data and Cyber Physical Systems, and further improve the level of modern maritime supervision and service.

There are numerous areas relied on random numbers. As one knows, in Cryptography, randomness plays a vital role from key generation to encrypting the systems. If randomness is not created effectively, the whole system is vulnerable to security threats where an outsider can easily predict the algorithm used to generate the random numbers in the system. Another main application where one would not touch is the role of random numbers in different devices mainly storage-related like Solid State Drives, Universal Serial Bus (USB), Secure Digital (SD) cards random performance testing. This paper focuses on various novel algorithms to generate random numbers for efficient performance evaluation of different drives. The main metrics for performance testing is random read and write performance. Here, the biggest challenge to test the random performance of the drive is not only the extent to which randomness is created but also the testing should cover the entire device (say complete NAND, NOR, etc.). So, the random number generator should generate in such a way that the random numbers should not be able to be predicted and must generate the numbers covering the entire range. This paper proposes different methods for such generators and towards the end, discusses the implementation in Field Programmable Gate Array (FPGA).

This paper presents a randomized Montgomery Powering Ladder Modular Exponentiation (RMPLME) scheme for side channel attacks (SCA) resistant Rivest-Shamir-Adleman (RSA) and its leakage resilience analysis. This method randomizes the computation time of square-and-multiply operations for each exponent bit of the Montgomery Powering Ladder (MPL) based RSA exponentiation using various radices (Radix – 2, 22, and 24) based Montgomery Modular multipliers (MMM) randomly. The randomized computations of RMPLME generates non-uniform timing channels information and power traces thus protecting against SCA. In this work, we have developed and implemented a) an unmasked right-to-left Montgomery Modular Exponentiation (R-L MME), b) MPL exponentiation and c) the proposed RMPLME schemes for RSA decryption. All the three realizations have been assessed for side channel leakage using Welch’s t-test and analyzed for secured realizations based on degree of side channel information leakage. RMPLME scheme shows the least side-channel leakage and resilient against SPA, DPA, C-Safe Error, CPA and Timing Attacks.

This article is about the current situation of cybercrime activity in the world. Research was planned to seek the possible protection measures taking into account the last events which might create an appropriate background for increasing of ransomware damages and cybercrime attacks. Nowadays, the most spread types of cybercrimes are fishing, theft of personal or payment data, cryptojacking, cyberespionage and ransomware. The last one is the most dangerous. It has ability to spread quickly and causes damages and sufficient financial loses. The major problem of this ransomware type is unpredictability of its behavior. It could be overcome only after the defined ransom was paid. This conditions created an appropriate background for the activation of cyber criminals’ activity even the organization of cyber gangs – professional, well-organized and well-prepared (tactical) group. So, researches conducted in this field have theoretical and practical value in the scientific sphere of research.