Biblio

The outages and power shortages are common occurrences in today's world and they have a significant economic impact. These failures can be minimized by making the power grid topologically robust. Therefore, the vulnerability assessment in power systems has become a major concern. This paper considers both pure and extended topological method to analyse the vulnerability of the power system to single line failures. The lines are ranked based on four spectral graph metrics: spectral radius, algebraic connectivity, natural connectivity, and effective graph resistance. A correlation is established between all the four metrics. The impact of load uncertainty on the component ranking has been investigated. The vulnerability assessment has been done on IEEE 9-bus system. It is observed that load variation has minor impact on the ranking.

While digitization of distribution grids through information and communications technology brings numerous benefits, it also increases the grid's vulnerability to serious cyber attacks. Unlike conventional systems, attacks on many industrial control systems such as power grids often occur in multiple stages, with the attacker taking several steps at once to achieve its goal. Detection mechanisms with situational awareness are needed to detect orchestrated attack steps as part of a coherent attack campaign. To provide a foundation for detection and prevention of such attacks, this paper addresses the detection of multi-stage cyber attacks with the aid of a graph-based cyber intelligence database and alert correlation approach. Specifically, we propose an approach to detect multi-stage attacks by lever-aging heterogeneous data to form a knowledge base and employ a model-based correlation approach on the generated alerts to identify multi-stage cyber attack sequences taking place in the network. We investigate the detection quality of the proposed approach by using a case study of a multi-stage cyber attack campaign in a future-orientated power grid pilot.

This paper presents a scalable single-input-multiple-output DC/DC converter targeting load transient response and security improvement for low-power System-on-Chips (SoCs). A two-stage modular architecture is introduced to enable scalability. The shared switched-capacitor pre-charging circuits are implemented to improve load transient response and decouple correlations between inputs and outputs. The demo version of the converter has three identical outputs, each supporting 0.3V to 0.9V with a maximum load current of 150mA. Based on post-layout simulation results in 32nm CMOS process, the converter output provides 19.3V/μs reference tracking speed and 27mA/ns workload transitions with negligible voltage droops or spikes. No cross regulation is observed at any outputs with a worst-case voltage ripple of 68mV. Peak efficiency reaches 85.5% for each output. With variable delays added externally, the input-output correlations can change 10 times and for steady-state operation, such correlation factors are always kept below 0.05. The converter is also scaled to support 6 outputs with only 0.56mm2 more area and maintains same load transient response performance.

Due to the difficulty of obtaining a database of original images that are required in the classification process to detect tampering, this paper presents a technique for detecting image tampering such as image steganography in the spatial domain. The system depends on deriving the auto-correlation function of the image histogram, then applying a high-pass filter with a threshold. This technique can be used to decide which image is cover or a stego image, without adopting the original image. The results have eventually revealed the validity of this system. Although this study has focused on least-significant-bit (LSB) steganography, we expect that it could be extended to other types of image tapering.

The Internet of Things (IoT) technology is being utilized in endless applications nowadays and the security of these applications is of great importance. Image based IoT applications serve a wide variety of fields such as medical application and smart cities. Steganography is a great threat to these applications where adversaries can use the images in these applications to hide malicious messages. Therefore, this paper presents an image steganalysis technique that employs Convolutional Neural Networks (CNN) to detect the infamous JPEG steganography technique: JPEG universal wavelet relative distortion (J-UNIWARD). Several experiments were conducted to determine the breaking point of J-UNIWARD, whether the hiding technique relies on correlation of the images, and the effect of utilizing Discrete Cosine Transform (DCT) on the performance of the CNN. The results of the CNN display that the breaking point of J-UNIWARD is 1.5 (bpnzAC), the correlation of the database affects the detection accuracy, and DCT increases the detection accuracy by 13%.

With the rapid development of the internet of things and cloud computing, users can instantly transmit a large amount of data to various fields, with the development of communication technology providing convenience for people's life, information security is becoming more and more important. Therefore, it is of great significance to study the technology of image hiding information detection. This paper mainly uses the support vector machine learning algorithm to detect the hidden information of the image, based on a standard image library, randomly selecting images for embedding secret information. According to the bit-plane correlation and the gradient energy change of a single bit-plane after encryption of an image LSB matching algorithm, gradient energy change is selected as characteristic change, and the gradient energy change is innovatively applied to a support vector machine classifier algorithm, and has very good detection effect and good stability on the dense image with the embedding rate of more than 40 percent.

Cyber-Physical Systems(CPS) have been experiencing a fast-growing process in recent decades, and related security issues also have become more important than ever before. To design an efficient defensive policy for operators and controllers is the utmost task to be considered. In this paper, a stochastic game-theoretic model is developed to study a CPS security problem by considering the interdependence between cyber and physical spaces of a CPS. The game model is solved with Minimax Q-learning for finding the mixed strategies equilibria. The numerical simulation revealed that the defensive factors and attack cost can affect the policies adopted by the system. From the perspective of the operator of a CPS, increasing successful defense probability in the phrase of disruption will help to improve the probability of defense strategy when there is a correlation between the cyber layer and the physical layer in a CPS. On the contrary side, the system defense probability will decrease as the total cost of the physical layer increases.

Identity resolution system is the primary technical research problem to set up the data collection capability of industrial internet, and the configuration resolution of complex assets is an application difficulty. To implement the particular requirements of complex equipment configuration management, an industry-oriented identity resolution architecture and the configuration resolution service were designed. In accordance with the technical information management of high-speed train, corresponding handle structures was proposed to describe the configuration structure and related components information of EMU (Electric Multiple Unit). A distributed processing algorithm for configuration resolution and the hit-ratio evaluation method of handle service sites was proposed. The performance, stability, and resolution consistency of the handle system in this paper are proved by experiments, which is also great significant to the intelligent identity applications in other industries.

Information systems and applications provide indispensable services at every stage of life, enabling us to carry out our activities more effectively and efficiently. Today, information technology systems produce many alarm and event records. These produced records often have a relationship with each other, and when this relationship is captured correctly, many interruptions that will harm institutions can be prevented before they occur. For example, an increase in the disk I/O speed of a server or a problem may cause the business software running on that server to slow down and cause different results in this slowness. Here, an institution’s accurate analysis and management of all event records, and rule-based analysis of the resulting records in certain time periods and depending on certain rules will ensure efficient and effective management of millions of alarms. In addition, it will be possible to prevent possible problems by removing the relationships between events. Events that occur in IT systems are a kind of footprint. It is also vital to keep a record of the events in question, and when necessary, these event records can be analyzed to analyze the efficiency of the systems, harmful interferences, system failure tendency, etc. By understanding the undesirable situations such as taking the necessary precautions, possible losses can be prevented. In this study, the model developed for fault prediction in systems by performing event log analysis in information systems is explained and the experimental results obtained are given.

In this paper, we propose a numerical simulation investigation of the wavelength division multiplexing mechanism between a chaotic secure channel and a traditional fiber channel using the advanced modulation method DP-16QAM at the bitrate of 80Gbps, the fiber length of 80 km and 100 GHz channel spacing in C-band. Our paper investigates correlation coefficients between the transmitter and also the receiver for two forms of communication channels. Our simulation results demonstrate that, in all cases, BER is always below 2.10-4 even when we have not used the forward-error-correction method. Besides, cross-interaction between the chaotic channel and also the non-chaotic channel is negligible showing a highly independent level between two channels.

We propose and numerically demonstrate a novel secure key distribution scheme based on the chaos synchronization of two semiconductor lasers (SLs) subject to symmetrical double chaotic injections, which are outputted by two mutually-coupled semiconductor lasers. The results show that high quality chaos synchronization can be observed between two local SLs with suitable injection strength and identical injection time delays for Alice and Bob. On the basis of satisfactory chaos synchronization and a post-processing technology, identical secret keys for Alice and Bob are successfully generated with bit error ratio (BER) below the HD-FEC threshold of $^\textrm-3\$$\$.

{With the rapid development of cyberspace, cyber security incidents are increasing, and the means and types of network attacks are becoming more and more complex and refined, which brings greater challenges to security risk control. First, the knowledge graph technology is used to construct a cyber security knowledge graph based on ontology to realize multi-source heterogeneous security big data fusion calculation, and accurately express the complex correlation between different security entities. Furthermore, for cyber security risk control, a key node assessment method for security risk diffusion is proposed. From the perspectives of node communication correlation and topological level, the calculation method of node communication importance based on improved PageRank Algorithm and based on the improved K-shell Algorithm calculates the importance of node topology are studied, and then organically combine the two calculation methods to calculate the importance of different nodes in security risk defense. Experiments show that this method can evaluate the importance of nodes more accurately than the PageRank algorithm and the K-shell algorithm.

Recent projects regarding the exploration of the functions of microbiomes within communities brought about a plethora of new data. That specific field of study is called Metagenomics and one of its more advancing approach is the application of network analysis. The paper introduces NAMData which is a web-application tool for the network analysis of microbiome data. The system handles the compositionality and sparsity nature of microbiome data by applying taxa filtration, normalization, and zero treatment. Furthermore, compositionally aware correlation estimators were used to compute for the correlation between taxa and the system divides the network into the positive and negative correlation network. NAMData aims to capitalize on the unique network features namely network visualization, centrality scores, and community detection. The system enables researchers to include network analysis in their analysis pipelines even without any knowledge of programming. Biological concepts can be integrated with the network findings gathered from the system to either support existing facts or form new insights.

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.

The security of the power grid is the first element of its operation. This paper aims at finding the vulnerability nodes in the power grid to prevent it from being destroyed. A novel comprehensive vulnerability index is proposed to the singleness of evaluation indicators for existing literature by integrating the power grid's topology information and operating state. Taking IEEE-118 as an example, the simulation analysis proves that the proposed vulnerability index has certain discriminative advantages and the best weighting factor is obtained through correlation analysis.

A threat of eavesdropping display screen image of information device is caused by unintended EM leakage emanation. QR bar-code is capable of error correction, and its information is possibly read from a damaged screen image from EM leakage. A new design of QR bar-code proposed in this paper uses selected colors in consideration of correlation between the EM wave leakage and display color. Proposed design of QR bar-code keeps error correction of displayed image, and makes it difficult to read information on the eavesdropped image.

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.

Consider the identification (ID) via channels problem, where a receiver wants to decide whether the transmitted identifier is its identifier, rather than decoding the identifier. This model allows to transmit identifiers whose size scales doubly-exponentially in the blocklength, unlike common transmission (or channel) codes whose size scales exponentially. It suffices to use binary constant-weight codes (CWCs) to achieve the ID capacity. By relating the parameters of a binary CWC to the minimum distance of a code and using higher-order correlation moments, two upper bounds on the binary CWC size are proposed. These bounds are shown to be upper bounds also on the identifier sizes for ID codes constructed by using binary CWCs. We propose two code constructions based on optical orthogonal codes, which are used in optical multiple access schemes, have constant-weight codewords, and satisfy cyclic cross-correlation and autocorrelation constraints. These constructions are modified and concatenated with outer Reed-Solomon codes to propose new binary CWCs optimal for ID. Improvements to the finite-parameter performance of both our and existing code constructions are shown by using outer codes with larger minimum distance vs. blocklength ratios. We also illustrate ID performance regimes for which our ID code constructions perform significantly better than existing constructions.

Human-automation shared control is proposed to reduce the risk of driver disengagement in Level-3 autonomous vehicles. Although previous studies have approved shared control strategy is effective to keep a driver in the loop and improve the driver's performance, over- and under-trust may affect the cooperation between the driver and the automation system. This study conducted a human-in-the-loop driving simulation experiment to assess the effects of trust on driver's behavior of shared control. An expert shared control strategy with longitudinal and lateral driving assistance was proposed and implemented in the experiment platform. Based on the experiment (N=24), trust in shared control was evaluated, followed by a correlation analysis of trust and behaviors. Moderating effects of trust on the relationship between gaze focalization and minimum of time to collision were then explored. Results showed that self-reported trust in shared control could be evaluated by three subscales respectively: safety, efficiency and ease of control, which all show stronger correlations with gaze focalization than other behaviors. Besides, with more trust in ease of control, there is a gentle decrease in the human-machine conflicts of mean brake inputs. The moderating effects show trust could enhance the decrease of minimum of time to collision as eyes-off-road time increases. These results indicate over-trust in automation will lead to unsafe behaviors, particularly monitoring behavior. This study contributes to revealing the link between trust and behavior in the context of human-automation shared control. It can be applied in improving the design of shared control and reducing risky behaviors of drivers by further trust calibration.

Trust in autonomous teammates has been shown to be a key factor in human-autonomy team (HAT) performance, and anthropomorphism is a closely related construct that is underexplored in HAT literature. This study investigates whether perceived anthropomorphism can be measured from team communication behaviors in a simulated remotely piloted aircraft system task environment, in which two humans in unique roles were asked to team with a synthetic (i.e., autonomous) pilot agent. We compared verbal and self-reported measures of anthropomorphism with team error handling performance and trust in the synthetic pilot. Results for this study show that trends in verbal anthropomorphism follow the same patterns expected from self-reported measures of anthropomorphism, with respect to fluctuations in trust resulting from autonomy failures.

The concealment and confusion nature of insider threat makes it a challenging task for security analysts to identify insider threat from log data. To detect insider threat, we propose a novel gated recurrent unit (GRU) and multi-autoencoder based insider threat detection method, which is an unsupervised anomaly detection method. It takes advantage of the extremely unbalanced characteristic of insider threat data and constructs a normal behavior autoencoder with low reconfiguration error through multi-level filter behavior learning, and identifies the behavior data with high reconfiguration error as abnormal behavior. In order to achieve the high efficiency of calculation and detection, GRU and multi-head attention are introduced into the autoencoder. Use dataset v6.2 of the CERT insider threat as validation data and threat detection recall as evaluation metric. The experimental results show that the effect of the proposed method is obviously better than that of Isolation Forest, LSTM autoencoder and multi-channel autoencoders based insider threat detection methods, and it's an effective insider threat detection technology.

The reuse of libraries in software development has become prevalent for improving development efficiency and software quality. However, security vulnerabilities of reused libraries propagated through software project dependency pose a severe security threat, but they have not yet been well studied. In this paper, we present the first large-scale empirical study of project dependencies with respect to security vulnerabilities. We developed PDGraph, an innovative approach for analyzing publicly known security vulnerabilities among numerous project dependencies, which provides a new perspective for assessing security risks in the wild. As a large-scale software collection in dependency, we find 337,415 projects and 1,385,338 dependency relations. In particular, PDGraph generates a project dependency graph, where each node is a project, and each edge indicates a dependency relationship. We conducted experiments to validate the efficacy of PDGraph and characterized its features for security analysis. We revealed that 1,014 projects have publicly disclosed vulnerabilities, and more than 67,806 projects are directly dependent on them. Among these, 42,441 projects still manifest 67,581 insecure dependency relationships, indicating that they are built on vulnerable versions of reused libraries even though their vulnerabilities are publicly known. During our eight-month observation period, only 1,266 insecure edges were fixed, and corresponding vulnerable libraries were updated to secure versions. Furthermore, we uncovered four underlying dependency risks that can significantly reduce the difficulty of compromising systems. We conducted a quantitative analysis of dependency risks on the PDGraph.

Translation studies is one of the fastest growing interdisciplinary research fields in the world today. Business English is an urgent research direction in the field of translation studies. To some extent, the quality of business English translation directly determines the success or failure of international trade and the economic benefits. On the basis of sequence information encoding and decoding model of LSTM, this paper proposes a strategy combining attention mechanism with bidirectional LSTM model to handle the question of feature extraction of text information. The proposed method reduces the semantic complexity and improves the overall correlation accuracy. The experimental results show its advantages.

One of the significant difficulties in network safety is the arrangement of a mechanized and viable digital danger's location strategy. This paper presents an AI procedure for digital dangers recognition, in light of fake neural organizations. The proposed procedure changes large number of gathered security occasions over to singular occasion profiles and utilize a profound learning-based discovery strategy for upgraded digital danger identification. This research work develops an AI-SIEM framework dependent on a blend of occasion profiling for information preprocessing and distinctive counterfeit neural organization techniques by including FCNN, CNN, and LSTM. The framework centers around separating between obvious positive and bogus positive cautions, consequently causing security examiners to quickly react to digital dangers. All trials in this investigation are performed by creators utilizing two benchmark datasets (NSLKDD and CICIDS2017) and two datasets gathered in reality. To assess the presentation correlation with existing techniques, tests are carried out by utilizing the five ordinary AI strategies (SVM, k-NN, RF, NB, and DT). Therefore, the exploratory aftereffects of this examination guarantee that our proposed techniques are fit for being utilized as learning-based models for network interruption discovery and show that despite the fact that it is utilized in reality, the exhibition beats the traditional AI strategies.

With the evolution of the communication technology, fast and efficient tools for secure exchanged data are highly required. Through this research work, we introduce a simplified and fast chaos-based scheme for multimedia data encryption and in particular for color image encryption application. The new algorithm is based on an extracted four-dimension (4-D) discrete time map. The proposed 4-D chaos system includes seven (07) nonlinear terms and four (04) controllers to generate a robust chaos that can satisfy the encryption requirements. The performance of this image encryption algorithm are analyzed with the help of four important factors which are key space, correlation, complexity and running time. Results of the security analysis compared to some of similar proposals, show that our encryption scheme is more effective in terms of key stream cipher space, correlation, complexity and running time.