Biblio

This paper proposes an artificial immune information security protection technology architecture for embedded system of Electric power equipment. By simulating the three functions of human immunity, namely "immune homeostasis", "immune surveillance" and "immune defense", the power equipment is endowed with the ability of human like active immune security protection. Among them, "immune homeostasis" is constructed by trusted computing technology components to establish a trusted embedded system running environment. Through fault-tolerant component construction, "immune surveillance" and "immune defense" realize illegal data defense, business logic legitimacy check and equipment status evaluation, realize real-time perception and evaluation of power equipment's own security status, as well as fault emergency handling and event backtracking record, so that power equipment can realize self recovery from abnormal status. The proposed technology architecture is systematic, scientific and rich in scalability, which can significantly improve the information security protection ability of electric power equipment.

Neural networks with deep architectures have been used to construct state-of-the-art classifiers that can match human level accuracy in areas such as image classification. However, many of these classifiers can be fooled by examples slightly modified from their original forms. In this work, we propose a novel approach for generating adversarial examples that makes use of only attribution information of the features and perturbs only features that are highly influential to the output of the classifier. We call this approach Attribution Based Adversarial Generation (ABAG). To demonstrate the effectiveness of this approach, three somewhat arbitrary algorithms are proposed and examined. In the first algorithm all non-zero attributions are utilized and associated features perturbed; in the second algorithm only the top-n most positive and top-n most negative attributions are used and corresponding features perturbed; and in the third algorithm the level of perturbation is increased in an iterative manner until an adversarial example is discovered. All of the three algorithms are implemented and experiments are performed on the well-known MNIST dataset. Experiment results show that adversarial examples can be generated very efficiently, and thus prove the validity and efficacy of ABAG - utilizing attributions for the generation of adversarial examples. Furthermore, as shown by examples, ABAG can be adapted to provides a systematic searching approach to generate adversarial examples by perturbing a minimum amount of features.

Alloy is a declarative modeling language based on a first-order relational logic. Its constraint-based analysis has enabled a wide range of applications in software engineering, including configuration synthesis, bug finding, test-case generation, and security analysis. Certain types of analysis tasks in these domains involve finding an optimal solution. For example, in a network configuration problem, instead of finding any valid configuration, it may be desirable to find one that is most permissive (i.e., it permits a maximum number of packets). Due to its dependence on SAT, however, Alloy cannot be used to specify and analyze these types of problems. We propose AlloyMax, an extension of Alloy with a capability to express and analyze problems with optimal solutions. AlloyMax introduces (1) a small addition of language constructs that can be used to specify a wide range of problems that involve optimality and (2) a new analysis engine that leverages a Maximum Satisfiability (MaxSAT) solver to generate optimal solutions. To enable this new type of analysis, we show how a specification in a first-order relational logic can be translated into an input format of MaxSAT solvers—namely, a Boolean formula in weighted conjunctive normal form (WCNF). We demonstrate the applicability and scalability of AlloyMax on a benchmark of problems. To our knowledge, AlloyMax is the first approach to enable analysis with optimality in a relational modeling language, and we believe that AlloyMax has the potential to bring a wide range of new applications to Alloy.

The international Supply Chains (SC) have expanded rapidly over the decades and also consist of many entities and business partners. The increasing complexity of supply chain makes it more vulnerable to a security threat. Therefore, it is necessary to evaluate security management systems to ensure the flow of goods in SC. In this paper we used international standards to assess the security of the company's supply chain compliance with ISO 28001. Supply chain security that needs to be assessed includes all inbound logistics activities to outbound logistics. The aim of this research is to analyse the security management system by identifying security threat, consequences, and likelihood to develop adequate countermeasures for the security of the company's supply chain. Security risk assessment was done using methodology compliance with ISO 28001 which are identify scope of security assessment, conduct security assessment, list applicable threat scenario, determine consequences, determine likelihood, determine risk score, risk evaluation using risk matrix, determine counter measures, and estimation of risk matrix after countermeasures. This research conducted in one of the leather factory in Indonesia. In this research we divided security threat into five category: asset security, personnel security, information security, goods and conveyance security, and closed cargo transport units. The security assessment was conducted by considering the performance review according to ISO 28001: 2007 and the results show that there are 22 security threat scenarios in the company's supply chain. Based upon a system of priorities by risk score, countermeasures are designed to reduce the threat into acceptable level.

In the information age, computer network technology has covered different areas of social life and involved various fields, and artificial intelligence, as an emerging technology with a very rapid development momentum in recent years, is important in promoting the development of computer network systems. This article explains the concept of artificial intelligence technology, describes the problems faced by computer networks, further analyses the advantages of artificial intelligence and the inevitability of application in network technology, and then studies the application of artificial intelligence in computer network technology.

Asymmetric warfare and anti-terrorist war have become a new style of military struggle in the new century, which will inevitably have an important impact on the military economy of various countries and catalyze the innovation climax of military logistics theory and practice. The war in the information age is the confrontation between systems, and “comprehensive integration” is not only the idea of information war ability construction, but also the idea of deterrence ability construction in the information age. Looking at the local wars under the conditions of modern informationization, it is not difficult to see that the status and role of light weapons and equipment have not decreased, on the contrary, higher demands have been put forward for their combat performance. From a forward-looking perspective, based on our army's preparation and logistics support for future asymmetric operations and anti-terrorist military struggle, this strategic issue is discussed in depth.

Despite the high directivity of THz beams, THz wireless links may still suffer compromising emissions when propagate through atmospheric turbulence and suffers scattering. In this work, we investigate the eavesdropping risks of a line-of-sight (LOS) THz link `in atmospheric turbulence with an eavesdropper located close to but outside of the beam path. A theoretical model considering the turbulence induced losses, gaseous absorption and beam divergence is conducted. Theoretical estimations agree well with our measured data. The secrecy capacity and outage probability dependent on the carrier frequency, turbulence strength, eavesdropper’s position and receiver sensitivity are analyzed.

Named Data Network (NDN) is considered to be the future of Internet architecture. The nature of NDN is to disseminate data based on the naming scheme rather than the location of the node. This feature caters to the need of vehicular applications, resulting in Vehicular Named Data Networks (VNDN). Although it is still in the initial stages of research, the collaboration has assured various advantages which attract the researchers to explore the architecture further. VNDN face challenges such as intermittent connectivity, mobility of nodes, design of efficient forwarding and naming schemes, among others. In order to develop effective forwarding strategies, behavior of data and interest packets under various circumstances needs to be studied. In this paper, propagation behavior of data and interest packets is analyzed by considering metrics such as Interest Satisfaction Ratio (ISR), Hop Count Difference (HCD) and Copies of Data Packets Processed (CDPP). These metrics are evaluated under network conditions such as varying network size, node mobility and amount of interest produced by each node. Simulation results show that data packets do not follow the reverse path of interest packets.

Research on post-quantum cryptography (PQC) to improve the security against quantum computers has been actively conducted. In 2020, NIST announced the final PQC candidates whose design rationales rely on NP-hard or NP-complete problems. It is believed that cryptography based on NP-hard problem might be secure against attacks using quantum computers. N. Koblitz introduced the concept of public-key cryptography using a 3-regular graph with a perfect dominating set in the 1990s. The proposed cryptosystem is based on NP-complete problem to find a perfect dominating set in the given graph. Later, S. Yoon proposed a variant scheme using a perfect minus dominating function. However, their works have not received much attention since these schemes produce huge ciphertexts and are hard to implement efficiently. Also, the security parameters such as key size and plaintext-ciphertext size have not been proposed yet. We conduct security and performance analysis of their schemes and discuss the practical range of security parameters. As an application, the scheme with one-wayness property can be used as an encoding method in the white-box cryptography (WBC).

The subgraph matching problem arises in a number of modern machine learning applications including segmented images and meshes of 3D objects for pattern recognition, bio-chemical reactions and security applications. This graph-based problem can have a very large and complex solution space especially when the world graph has many more nodes and edges than the template. In a real use-case scenario, analysts may need to query additional information about template nodes or world nodes to reduce the problem size and the solution space. Currently, this query process is done by hand, based on the personal experience of analysts. By analogy to the well-known active learning problem in machine learning classification problems, we present a machine-based active learning problem for the subgraph match problem in which the machine suggests optimal template target nodes that would be most likely to reduce the solution space when it is otherwise overly large and complex. The humans in the loop can then include additional information about those target nodes. We present some case studies for both synthetic and real world datasets for multichannel subgraph matching.

Secret-related carrier intelligent management and control system uses the Internet of Things and artificial intelligence to solve the transformation of secret-related carrier management and control from manual operation to automatic detection, precise monitoring, and intelligent decision-making, and use technical means to resolve security risks. However, the coexistence of multiple heterogeneous networks will lead to various network security problems in the secret carrier intelligent management and control. Aiming at the actual requirements of the intelligent management and control of secret-related carriers, this paper proposes a system structure including device domain, network domain, platform domain and user domain, and conducts a detailed system security analysis, and introduces intelligent agent technology, and proposes a distributed system. The hierarchical system structure of the secret-related carrier intelligent management and control security protection model has good robustness and portability.

In recent years, blockchain-based cryptocurren-cies have attracted much attention. Attacks targeting cryptocurrencies and related services directly profit an attacker if successful. Related studies have reported attacks targeting configuration-vulnerable nodes in Ethereum using a method called honeypots to observe malicious user attacks. They have analyzed 380 million observed requests and showed that attacks had to that point taken at least 4193 Ether. However, long-term observations using honeypots are difficult because the cost of maintaining honeypots is high. In this study, we analyze the behavior of malicious users using our honeypot system. More precisely, we clarify the pre-investigation that a malicious user performs before attacks. We show that the cost of maintaining a honeypot can be reduced. For example, honeypots need to belong in Ethereum's P2P network but not to the mainnet. Further, if they belong to the testnet, the cost of storage space can be reduced.

Over the years, human errors have been identified as one of the most critical factors impacting cybersecurity in an organization that has had a substantial impact. The research uses recent articles published on human resources and information cybersecurity. This research focuses on the vulnerabilities and the best solution to mitigate these threats based on literature review methodology. The study also focuses on identifying the human attitude and behavior towards cybersecurity and how that would impact the organization's financial impact. With the help of the Two-factor Taxonomy of the security behavior model developed in past research, the research aims to identify the best practices and compare the best practices with that of the attitude-behavior found and matched to the model. Finally, the study would compare the difference between best practices and the current practices from the model. This would help provide the organization with specific recommendations that would help change their attitude and behavior towards cybersecurity and ensure the organization is not fearful of the cyber threat of human error threat.

With the rapid development of urbanization, community security and public security have become an important social issue. As conventional patrol methods can not effectively ensure effective supervision, this paper studies the application of NFC (Near Field Communication) technology in intelligent security system, designs and constructs a set of intelligent security system suitable for public security patrol or security patrol combined with current cloud service technology. The system can not only solve the digital problem of patrol supervision in the current public security, but also greatly improve the efficiency of security and improve the service quality of the industry through the application of intelligent technology.

Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inefficient. To address this problem, we propose an automated security assessment framework for IoT networks. Our framework first leverages machine learning and natural language processing to analyze vulnerability descriptions for predicting vulnerability metrics. The predicted metrics are then input into a two-layered graphical security model, which consists of an attack graph at the upper layer to present the network connectivity and an attack tree for each node in the network at the bottom layer to depict the vulnerability information. This security model automatically assesses the security of the IoT network by capturing potential attack paths. We evaluate the viability of our approach using a proof-of-concept smart building system model which contains a variety of real-world IoT devices and poten-tial vulnerabilities. Our evaluation of the proposed framework demonstrates its effectiveness in terms of automatically predicting the vulnerability metrics of new vulnerabilities with more than 90% accuracy, on average, and identifying the most vulnerable attack paths within an IoT network. The produced assessment results can serve as a guideline for cybersecurity professionals to take further actions and mitigate risks in a timely manner.

In dynamic control centers, conventional SCADA systems are enhanced with novel assistance functionalities to increase existing monitoring and control capabilities. To achieve this, different key technologies like phasor measurement units (PMU) and Digital Twins (DT) are incorporated, which give rise to new cyber-security challenges. To address these issues, a four-stage threat analysis approach is presented to identify and assess system vulnerabilities for novel dynamic control center architectures. For this, a simplified risk assessment method is proposed, which allows a detailed analysis of the different system vulnerabilities considering various active and passive cyber-attack types. Qualitative results of the threat analysis are presented and discussed for different use cases at the control center and substation level.

In some cases, the confidentiality of cryptographic algorithms used in digital communication is related to computational complexity mathematical problems, such as calculating the discrete logarithm, the knapsack problem, decomposing a composite number into prime divisors etc. This article describes the application of insolvability of factorization of a large composite number, and reviews previous work integer factorization using either the deterministic or the bio-inspired algorithms. This article focuses on the possibility of using bio-inspired methods to solve the problem of cryptanalysis of asymmetric encryption algorithms, which ones based on factorization of composite numbers. The purpose of this one is to reviewing previous work in integer factorization algorithms, developing a prototype of either the deterministic and the bio-inspired algorithm and the effectiveness of the developed algorithms and recommendations are made for future research paths.

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.

Reinforcement learning (R.L.) is an effective and practical means for resolving problems where the broker possesses no information or knowledge about the environment. The agent acquires knowledge that is conditioned on two components: trial-and-error and rewards. An R.L. agent determines an effective approach by interacting directly with the setting and acquiring information regarding the circumstances. However, many modern R.L.-based strategies neglect to theorise considering there is an enormous rift within the simulation and the physical world due to which policy-learning tactics displease that stretches from simulation to physical world Even if design learning is achieved in the physical world, the knowledge inadequacy leads to failed generalization policies from suiting to test circumstances. The intention of robust adversarial reinforcement learning(RARL) is where an agent is instructed to perform in the presence of a destabilizing opponent(adversary agent) that connects impedance to the system. The combined trained adversary is reinforced so that the actual agent i.e. the protagonist is equipped rigorously.

With the widespread application of cloud computing technology, data privacy security problem becomes more serious. The recent studies related to searchable encryption (SE) area have shown that the data owners can share their private data with efficient search function and high-strength security. However, the search method has yet to be perfected, compared with the plaintext search mechanism. In this paper, based LSSS matrix, we give a new searchable algorithm, which is suitable for many search method, such as exact search, Boolean search and range search. In order to improve the search efficiency, the 0, 1-coding theory is introduced in the process of ciphertext search. Meanwhile it is shown that multi-search mechanism can improve the efficiency of data sharing. Finally, the performance analysis is presented, which prove our scheme is secure, efficient, and human-friendly.

Robustness, both to accident and to malevolent perturbations, is a crucial determinant of the successful deployment of deep convolutional neural network based SAR ATR systems in various security-sensitive applications. This paper performs a detailed adversarial robustness evaluation of deep convolutional neural network based SAR ATR models across two public available SAR target recognition datasets. For each model, seven different adversarial perturbations, ranging from gradient based optimization to self-supervised feature distortion, are generated for each testing image. Besides adversarial average recognition accuracy, feature attribution techniques have also been adopted to analyze the feature diffusion effect of adversarial attacks, which promotes the understanding of vulnerability of deep learning models.

Aiming at the disadvantages of traditional methods such as low penetration path generation efficiency and low attack type recognition accuracy, an optimal penetration path generation algorithm based on the knowledge map power WEB system unknown attack is proposed. First, establish a minimum penetration path test model. And use the model to test the unknown attack of the penetration path under the power WEB system. Then, the ontology of the knowledge graph is designed. Finally, the design of the optimal penetration path generation algorithm based on the knowledge graph is completed. Experimental results show that the algorithm improves the efficiency of optimal penetration path generation, overcomes the shortcomings of traditional methods that can only describe known attacks, and can effectively guarantee the security of power WEB systems.

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.

Recently, the Distributed Denial of Service (DDOS) attacks has been used for different aspects to denial the number of services for the end-users. Therefore, there is an urgent need to design an effective detection method against this type of attack. A fuzzy inference system offers the results in a more readable and understandable form. This paper introduces an anomaly-based Intrusion Detection (IDS) system using fuzzy logic. The fuzzy logic inference system implemented as a detection method for Distributed Denial of Service (DDOS) attacks. The suggested method was applied to an open-source DDOS dataset. Experimental results show that the anomaly-based Intrusion Detection system using fuzzy logic obtained the best result by utilizing the InfoGain features selection method besides the fuzzy inference system, the results were 91.1% for the true-positive rate and 0.006% for the false-positive rate.

Authorship attribution is the problem of identifying the author of texts based on the author's writing style. It is usually assumed that the writing style contains traits inaccessible to conscious manipulation and can thus be safely used to identify the author of a text. Several style markers have been proposed in the literature, nevertheless, there is still no consensus on which best represent the choices of authors. Here we assume an agnostic viewpoint on the dispute for the best set of features that represents an author's writing style. We rather investigate how different sources of information may unveil different aspects of an author's style, complementing each other to improve the overall process of authorship attribution. For this we model authorship attribution as a multi-view learning task. We assess the effectiveness of our proposal applying it to a set of well-studied corpora. We compare the performance of our proposal to the state-of-the-art approaches for authorship attribution. We thoroughly analyze how the multi-view approach improves on methods that use a single data source. We confirm that our approach improves both in accuracy and consistency of the methods and discuss how these improvements are beneficial for linguists and domain specialists.