Biblio

Continuous development of Internet of Things, big data and other emerging technologies has brought new challenges to the reliability of security-critical system products in various industries. Fault detection and evaluation in the early stage of software plays an important role in improving the reliability of software. However, fault prediction and evaluation, which are currently focused on the early stage of software, hardly provide high guidance for actual project development. In this study, a fault diagnosis method based on object-oriented Bayesian network (OOBN) is proposed. Starting from the time dimension and internal logic, a two-dimensional metric fault propagation model is established to calculate the failure rate of each early stage of software respectively, and the fault relationship of each stage is analyzed to find out the key fault units. In particular, it explores and validates the relationship between the failure rate of code phase and the failure caused by faults in requirement analysis stage and design stage in a train control system, to alert the developer strictly accordance with the industry development standards for software requirements analysis, design and coding, so as to reduce potential faults in the early stage. There is evidence that the study plays a crucial role to optimize the cost of software development and avoid catastrophic consequences.

The importance of the security of industrial control network has become increasingly prominent. Aiming at the defects of main security protection system in the intelligent manufacturing industrial control network, we propose a security attack risk detection and defense, and emergency processing capability synchronization technology system suitable for the intelligent manufacturing industrial control system. Integrating system control and network security theories, a flexible and reconfigurable system-wide security architecture method is proposed. On the basis of considering the high availability and strong real-time of the system, our research centers on key technologies supporting system-wide security analysis, defense strategy deployment and synchronization, including weak supervision system reinforcement and pattern matching, etc.. Our research is helpful to solve the problem of industrial control network of “old but full of loopholes” caused by the long-term closed development of the production network of important parts, and alleviate the contradiction between the high availability of the production system and the relatively backward security defense measures.

With the development of wireless sensor networks (WSNs), WSNs have been widely used in various fields such as animal habitat detection, military surveillance, etc. This paper focuses on protecting the source location privacy (SLP) in WSNs. Existing algorithms perform poorly in non-uniform networks which are common in reality. In order to address the performance degradation problem of existing algorithms in non-uniform networks, this paper proposes a robust fixed path-based random routing scheme (RFRR), which guarantees the path diversity with certainty in non-uniform networks. In RFRR, the data packets are sent by selecting a routing path that is highly differentiated from each other, which effectively protects SLP and resists the backtracking attack. The experimental results show that RFRR increases the difficulty of the backtracking attack while safekeeping the balance between security and energy consumption.

Mobile devices have been widely used to deploy security-sensitive applications such as mobile payments, mobile offices etc. SM2 digital signature technology is critical in these applications to provide the protection including identity authentication, data integrity, action non-repudiation. Since mobile devices are prone to being stolen or lost, several server-aided SM2 cooperative signature schemes have been proposed for the mobile scenario. However, existing solutions could not well fit the high-concurrency scenario which needs lightweight computation and communication complexity, especially for the server sides. In this paper, we propose a SM2 cooperative signature algorithm (SM2-CSA) for the high-concurrency scenario, which involves only one-time client-server interaction and one elliptic curve addition operation on the server side in the signing procedure. Theoretical analysis and practical tests shows that SM2-CSA can provide better computation and communication efficiency compared with existing schemes without compromising the security.

Current network assessment models often ignore the impact of attack-defense timing on network security, making it difficult to characterize the dynamic game of attack-defense effectively. To effectively manage the network security risks and reduce potential losses, in this article, we propose a selection strategy for network defense based on a time game model. By analyzing the attack-defense status by analogy with the SIR infectious disease model, construction of an optimal defense strategy model based on time game, and calculation of the Nash equilibrium of the the attacker and the defender under different strategies, we can determine an optimal defense strategy. With the Matlab simulation, this strategy is verified to be effective.

At present, most smart contract vulnerability detection use manually-defined patterns, which is time-consuming and far from satisfactory. To address this issue, researchers attempt to deploy deep learning techniques for automatic vulnerability detection in smart contracts. Nevertheless, current work mostly relies on a single code representation such as AST (Abstract Syntax Tree) or code tokens to learn vulnerability characteristics, which might lead to incompleteness of learned semantics information. In addition, the number of available vulnerability datasets is also insufficient. To address these limitations, first, we construct a dataset covering most typical types of smart contract vulnerabilities, which can accurately indicate the specific row number where a vulnerability may exist. Second, for each single code representation, we propose a novel way called AFS (AST Fuse program Slicing) to fuse code characteristic information. AFS can fuse the structured information of AST with program slicing information and detect vulnerabilities by learning new vulnerability characteristic information.

The widespread application of industrial Internet identifiers has increased the security risks of industrial Internet and identifier resolution system. In order to improve the security capabilities of identifier resolution system, this paper analyzes the security challenges faced by identifier resolution system at this stage, and in line with the concept of layered security defense in depth, divides the security domains of identifier resolution system and proposes a multi-level security strategy based on security domains by deploying appropriate protective measures in each security domain.

Software trustworthiness includes many attributes. Reasonable weight allocation of trustworthy attributes plays a key role in the software trustworthiness measurement. In practical application, attribute weight usually comes from experts' evaluation to attributes and hidden information derived from attributes. Therefore, when the weight of attributes is researched, it is necessary to consider weight from subjective and objective aspects. Firstly, a novel weight allocation method is proposed by combining the Fuzzy Analytical Hierarchy Process (FAHP) method and the Criteria Importance Though Intercrieria Correlation (CRITIC) method. Secondly, based on the weight allocation method, the trustworthiness measurement models of component-based software are established according to the four combination structures of components. Thirdly, some metric criteria of the model are proved to verify the reasonability. Finally, a case is used to illustrate the practicality of the model.

Data is one of the most valuable assets of an organization and has a tremendous impact on its long-term success and decision-making processes. Typically, organizational data error and outlier detection processes perform manually and reactively, making them time-consuming and prone to human errors. Additionally, rich data types, unlabeled data, and increased volume have made such data more complex. Accordingly, an automated anomaly detection approach is required to improve data management and quality control processes. This study introduces an unsupervised anomaly detection approach based on models comparison, consensus learning, and a combination of rules of thumb with iterative hyper-parameter tuning to increase data quality. Furthermore, a domain expert is considered a human in the loop to evaluate and check the data quality and to judge the output of the unsupervised model. An experiment has been conducted to assess the proposed approach in the context of a case study. The experiment results confirm that the proposed approach can improve the quality of organizational data and facilitate anomaly detection processes.

With the rapid improvement of China's industrial production level, there are an increasing number of industrial enterprises and kinds of products. The quality and safety supervision of industrial products is an important step to ensure people's livelihood safety. The current supervision includes a number of processes, such as risk monitoring, public opinion analysis, supervision, spot check and postprocessing. The lack of effective information integration and sharing between the above processes cannot support the implementation of whole-chain regulation well. This paper proposes a whole-chain supervision method of industrial product quality and safety based on a knowledge graph, which integrates massive and complex data of the whole chain and visually displays the relationships between entities in the regulatory process. This method can effectively solve the problem of information islands and track and locate the quality problems of large-scale industrial products.

Online news articles are an important source of information for decisions makers to understand the causal relation of events that happened. However, understanding the causality of an event or between events by traditional machine learning-based techniques from natural language text is a challenging task due to the complexity of the language to be comprehended by the machines. In this study, the Knowledge-oriented convolutional neural network (K-CNN) technique is used to extract the causal relation from online news articles related to the South China Sea (SCS) dispute. The proposed K-CNN model contains a Knowledge-oriented channel that can capture the causal phrases of causal relationships. A Data-oriented channel that captures the position information was added to the K-CNN model in this phase. The online news articles were collected from the national news agency and then the sentences which contain relation such as causal, message-topic, and product-producer were extracted. Then, the extracted sentences were annotated and converted into lower form and base form followed by transformed into the vector by looking up the word embedding table. A word filter that contains causal keywords was generated and a K-CNN model was developed, trained, and tested using the collected data. Finally, different architectures of the K-CNN model were compared to find out the most suitable architecture for this study. From the study, it was found out that the most suitable architecture was the K-CNN model with a Knowledge-oriented channel and a Data-oriented channel with average pooling. This shows that the linguistic clues and the position features can improve the performance in extracting the causal relation from the SCS online news articles. Keywords-component; Convolutional Neural Network, Causal Relation Extraction, South China Sea.

Wireless Sensor Network consists of hundreds to thousands of tiny sensor nodes deployed in the large field of the target phenomenon. Sensor nodes have advantages for its size, multifunctional, and inexpensive features; unfortunately, the resources are limited in terms of memory, computational, and in energy, especially. Network transmission between nodes and base station (BS) needs to be carefully designed to prolong the network life cycle. As the data transmission is energy consuming compared to data processing, designing sensor nodes into hierarchical network architecture is preferable because it can limit the network transmission. LEACH is one of the hierarchical network protocols known for simple and energy saving protocols. There are lots of modification made since LEACH was introduced for more energy efficient purposed. In this paper, hybridization of LEACH-C and LEACH-R and the modification have been presented for a more energy saving LEACH called LEACH-CR. Experimental result was compared with previous LEACH variant and showed to has advantages over the existing LEACH protocols in terms of energy consumption, dead/alive nodes, and the packet sent to Base Station. The result reflects that the consideration made for residual energy to select the cluster head and proximity transmission lead to a better energy consumption in the network.

Recently, with the increasing number of large-scale remote sensing images, the demand for large-scale remote sensing image object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has widely solve the problem of large-scale remote sensing image. Supervised hashing methods mainly leverage the label information of remote sensing image to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remote sensing image. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications.

Aiming at the problem of low detection accuracy in traditional power system network intrusion detection methods, in order to improve the performance of power system network intrusion detection, a power system network intrusion detection method based on random forest algorithm is proposed. Firstly, the power system network intrusion sub sample is selected to construct the random forest decision tree. The random forest model is optimized by using the edge function. The accuracy of the vector is judged by the minimum state vector of the power system network, and the measurement residual of the power system network attack is calculated. Finally, the power system network intrusion data set is clustered by Gaussian mixture clustering Through the design of power system network intrusion detection process, the power system network intrusion detection is realized. The experimental results show that the power system network intrusion detection method based on random forest algorithm has high network intrusion detection performance.

In this paper, we mainly introduce the security protection technology of smart grid based on edge computing and propose an edge computing security protection architecture based on multi-service flexible mechanism. Aiming at the real time requirements of heterogeneous energy terminal access and power edge computing business in multiple interactive environment, a real-time and strong compatibility terminal security access mechanism integrating physical characteristics and lightweight cryptographic mechanism is proposed. According to different power terminal security data requirements, the edge computing data transmission, processing security and privacy protection technology are proposed. In addition, in the power system of distribution, microgrid and advanced metering system, the application of edge computing has been well reflected. Combined with encryption technology, access authentication, the security defense of edge data, edge equipment and edge application is carried out in many aspects, which strengthens the security and reliability of business penetration and information sharing at the edge of power grid, and realizes the end-to-end and end-to-system security prevention and control of power grid edge computing.

The Internet of Things (IoT) provides significant benefits for industry due to connect the devices together through the internet. Attribute-Based Encryption (ABE) is a technique can enforce an access control over data to guarantee the data security. In this paper, we propose an ABE scheme for data in industrial IoT. The scheme achieves both security and high performance. When there is a shared subpolicy among the access policies of a sensor, the scheme optimizes the encryption of the messages. Through analysis and simulation, we show that our solution is security and efficient.

With the rapid development of the Internet, cyberspace security has become a potentially huge problem. At the same time, the disclosure of cyberspace vulnerabilities is getting faster and faster. Traditional protection methods based on known features cannot effectively defend against new network attacks. Network attack is no more a single vulnerability exploit, but an APT attack based on multiple complicated methods. Cyberspace attacks have become ``rationalized'' on the surface. Currently, there are a lot of researches about visualization of attack paths, but there is no an overall plan to reproduce the attack path. Most researches focus on the detection and characterization individual based on single behavior cyberspace attacks, which loose it's abilities to help security personnel understand the complete attack behavior of attackers. The key factors of this paper is to collect the attackers' aggressive behavior by reverse retrospective method based on the actual shooting range environment. By finding attack nodes and dividing offensive behavior into time series, we can characterize the attacker's behavior path vividly and comprehensively.

With the rapid development of 5G, the Internet of Things (IoT) and edge computing technologies dramatically improve smart industries' efficiency, such as healthcare, smart agriculture, and smart city. IoT is a data-driven system in which many smart devices generate and collect a massive amount of user privacy data, which may be used to improve users' efficiency. However, these data tend to leak personal privacy when people send it to the Internet. Differential privacy (DP) provides a method for measuring privacy protection and a more flexible privacy protection algorithm. In this paper, we study an estimation problem and propose a new frequency estimation algorithm named MFEA that redesigns the publish process. The algorithm maps a finite data set to an integer range through a hash function, then initializes the data vector according to the mapped value and adds noise through the randomized response. The frequency of all interference data is estimated with maximum likelihood. Compared with the current traditional frequency estimation, our approach achieves better algorithm complexity and error control while satisfying differential privacy protection (LDP).

Aiming at the cyber security problem of the advanced metering infrastructure(AMI), an anomaly detection method based on edge computing framework for the AMI is proposed. Due to the characteristics of the edge node of data concentrator, the data concentrator has the capability of computing a large amount of data. In this paper, distributing the intrusion detection model on the edge node data concentrator of the AMI instead of the metering center, meanwhile, two-way communication of distributed local model parameters replaces a large amount of data transmission. The proposed method avoids the risk of privacy leakage during the communication of data in AMI, and it greatly reduces communication delay and computational time. In this paper, KDDCUP99 datasets is used to verify the effectiveness of the method. The results show that compared with Deep Convolutional Neural Network (DCNN), the detection accuracy of the proposed method reach 99.05%, and false detection rate only gets 0.74%, and the results indicts the proposed method ensures a high detection performance with less communication rounds, it also reduces computational consumption.

This paper studies the differential privacy-preserving problem of discrete-time multi-agent systems (MASs) with antagonistic information, where the connected signed graph is structurally balanced. First, we introduce the bipartite consensus definitions in the sense of mean square and almost sure, respectively. Second, some criteria for mean square and almost sure bipartite consensus are derived, where the eventualy value is related to the gauge matrix and agents’ initial states. Third, we design the ε-differential privacy algorithm and characterize the tradeoff between differential privacy and system performance. Finally, simulations validate the effectiveness of the proposed algorithm.

Most of the existing identity systems are built on top of centralized storage systems. Storing identity data on these types of centralized storage platforms(e.g cloud storage, central servers) becomes a major privacy concern since various types of attacks and data breaches can happen. With this research, we are proposing blockchain and self-sovereign identity based digital identity (KYC - Know Your Customer) platform “Casper” to address the issues on centralized identity systems. “Casper ” is an Android/iOS based mobile identity wallet application that combines the integration of blockchain and a self-sovereign identity-based approach. Unlike centralized identity systems, the actual identities of the customer/users are stored in the customers’ mobile wallet application. The proof of these identities is stored in the blockchain-based decentralized storage as a self-sovereign identity proof. Casper platforms’ Self-Sovereign Identity(SSI)-based system provides a Zero Knowledge Proof(ZKP) mechanism to verify the identity information. Casper platform can be adopted in various domains such as healthcare, banking, government organization etc. As a use case, we have discussed building a digital identity wallet for banking customers with the Casper platform. Casper provides a secure, decentralized and ZKP verifiable identity by using blockchain and SSI based approach. It addresses the common issues in centralized/cloud-based identity systems platforms such as the lack of data immutability, lack of traceability, centralized control etc.

The task of malware labeling on real datasets faces huge challenges—ever-changing datasets and lack of ground-truth labels—owing to the rapid growth of malware. Clustering malware on their respective families is a well known tool used for improving the efficiency of the malware labeling process. In this paper, we addressed the challenge of clustering email malware, and carried out a cluster analysis on a real dataset collected from email campaigns over a 13-month period. Our main original contribution is to analyze the usefulness of email’s header information for malware clustering (a novel approach proposed by Burton [1]), and compare it with features collected from the malware directly. We compare clustering based on email header’s information with traditional features extracted from varied resources provided by VirusTotal [2], including static and dynamic analysis. We show that email header information has an excellent performance.

The emerging vehicular edge computing (VEC) technology has the potential to bring revolutionary development to vehicular ad hoc network (VANET). However, the edge computing servers (ECSs) are subjected to a variety of security threats. One of the most dangerous types of security attacks is the Sybil attack, which can create fabricated virtual vehicles (called Sybil vehicles) to significantly overload ECSs' limited computation resources and thus disrupt legitimate vehicles' edge computing applications. In this paper, we present a novel Sybil attack detection system on ECSs that is based on the design of a credibility enhanced temporal graph convolutional network. Our approach can identify the malicious vehicles in a dynamic traffic environment while preserving the legitimate vehicles' privacy, particularly their local position information. We evaluate our proposed approach in the SUMO simulator. The results demonstrate that our proposed detection system can accurately identify most Sybil vehicles while maintaining a low error rate.

The correctness of security control system strategy is very important to ensure the stability of power system. Aiming at the problem that the current security control strategy verification method is not enough to match the increasingly complex large power grid, this paper proposes a cyclic verification method of security control system strategy table based on constraints and whole process dynamic simulation. Firstly, the method is improved based on the traditional security control strategy model to make the strategy model meet certain generalization ability; And on the basis of this model, the cyclic dynamic verification of the strategy table is realized based on the constraint conditions and the whole process dynamic simulation, which not only ensures the high accuracy of strategy verification for the security control strategy of complex large power grid, but also ensures that the power system is stable and controllable. Finally, based on a certain regional power system, the optimal verification of strategy table verification experiment is realized. The experimental results show that the average processing time of the proposed method is 10.32s, and it can effectively guarantee the controllability and stability of power grid.

As a typical cyber-physical system, the power system utilizes advanced information and communication technologies to transmit crucial control signals in communication channels. However, many adversaries can construct false data injection attacks (FDIA) to circumvent traditional bad data detection and break the stability of the power grid. In this paper, we proposed a threat-maximizing FDIA model from the view of attackers. The proposed FDIA can not only circumvent bad data detection but can also cause a terrible fluctuation in the power system. Furthermore, in order to eliminate potential attack threats, the Spatio-temporal correlations of measurement matrices are considered. To extract the Spatio-temporal features, a data-driven detection method using a deep convolutional neural network was proposed. The effectiveness of the proposed FDIA model and detection are assessed by a simulation on the New England 39 bus system. The results show that the FDIA can cause a negative effect on the power system’s stable operation. Besides, the results reveal that the proposed FDIA detection method has an outstanding performance on Spatio-temporal features extraction and FDIA recognition.