Biblio

Internet of Things (IoT) connects various nodes such as sensor devices. For users from foreign networks, their direct access to the data of sensor devices is restricted because of security threats. Therefore, a ticket-based authentication scheme was proposed, which can mutually authenticate a mobile device and a sensor device. This scheme with new features fills a gap in IoT authentication, but the scheme has not been verified formally. Hence, it is important to study the security and reliability of the scheme from the perspective of formal methods.In this paper, we model this scheme using Communicating Sequential Processes (CSP). Considering the possibility of key leakage caused by security threats in IoT networks, we also build models where one of the keys used in the scheme is leaked. With the model checker Process Analysis Toolkit (PAT), we verify four properties (deadlock freedom, data availability, data security, and data authenticity) and find that the scheme cannot satisfy the last two properties with key leakage. Thus, we propose two improved models. The verification results show that the first improved model can guarantee data security, and the second one can ensure both data security and data authenticity.

Analyzing safety and security requirements remains a difficult task in the development of real-life network protocols. Although numerous modeling and analyzing methods have been proposed in the past decades, most of them handle safety and security requirements separately without considering their interplay. In this work, we propose a collaborative modeling framework that enables co-analysis of safety and security requirements for network protocols. Our modeling framework is based on a well-defined type system and supports modeling of network topology, message flows, protocol behaviors and attacker behaviors. It also supports the specification of safety requirements as temporal logical formulae and typical security requirements as queries, and leverages on the existing verification tools for formal safety and security analysis via model transformations. We have implemented this framework in a prototype tool CMSS, and illustrated the capability of CMSS by using the 5G AKA initialization protocol as a case study.

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 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.

5G smart grid applications rely on its high-performance transmission and bearer network. With the help of complex network theory, this paper first analyzes the complex network characteristic parameters of 5G smart grid, and explains the necessity and supporting significance of network vulnerability analysis for efficient transmission of 5G network. Then the node importance analysis algorithm based on node degree and clustering coefficient (NIDCC) is proposed. According to the results of simulation analysis, the power network has smaller path length and higher clustering coefficient in terms of static parameters, which indicates that the speed and breadth of fault propagation are significantly higher than that of random network. It further shows the necessity of network vulnerability analysis. By comparing with the other two commonly used algorithms, we can see that NIDCC algorithm can more accurately estimate and analyze the weak links of the network. It is convenient to carry out the targeted transformation of the power grid and the prevention of blackout accidents.

The power cyber-physical system combines the cyber network with the traditional electrical power network, which can monitor and control the operation of the power grid stably and efficiently. Since the system's structure and function is complicated and large, it becomes fragile as a result. Therefore, establishing a reasonable and effective CPS model and discussing its vulnerability performance under external attacks is essential and vital for power grid operation. This paper uses the theory of complex networks to establish a independent system model by IEEE-118-node power network and 200-node scale-free information network, introducing information index to identify and sort important nodes in the network, and then cascade model of the power cyber-physical system based on the node load capacity is constructed and the vulnerability assessment analysis is carried out. The simulation shows that the disintegration speed of the system structure under deliberate attacks is faster than random attacks; And increasing the node threshold can effectively inhibit the propagation of failure.

The vulnerability assessment of the cyber-physical power system based on complex network theory is applied in this paper. The influence of the power system statistics upon the system vulnerability is studied based on complex network theory. The electrical betweenness is defined to suitably describe the power system characteristics. The real power systems are utilized as examples to analyze the distribution of the degree and betweenness of the power system as a complex network. The topology model of the cyber-physical power system is formed, and the static analysis is implemented to the study of the cyber-physical power system structural vulnerability. The IEEE 300 bus test system is selected to verify the model.

With the rapid increase of practical problem complexity and code scale, the threat of software security is increasingly serious. Consequently, it is crucial to pay attention to the analysis of software source code vulnerability in the development stage and take efficient measures to detect the vulnerability as soon as possible. Machine learning techniques have made remarkable achievements in various fields. However, the application of machine learning in the domain of vulnerability static analysis is still in its infancy and the characteristics and performance of diverse methods are quite different. In this survey, we focus on a source code-oriented static vulnerability analysis method using machine learning techniques. We review the studies on source code vulnerability analysis based on machine learning in the past decade. We systematically summarize the development trends and different technical characteristics in this field from the perspectives of the intermediate representation of source code and vulnerability prediction model and put forward several feasible research directions in the future according to the limitations of the current approaches.

As a large amount of renewable energy is injected into the power grid, the source side of the power grid becomes extremely uncertain. Traditional static safety analysis methods based on pure physical models can no longer quickly and reliably give analysis results. Therefore, this paper proposes a deep learning-based static security analytical method. First, the static security assessment index of the power grid under the N-1 principle is proposed. Secondly, a neural network model and its input and output data for static safety analysis problems are designed. Finally, the validity of the proposed method was verified by IEEE grid data. Experiments show that the proposed method can quickly and accurately give the static security analysis results of the source-side high uncertainty grid.

Along with technological developments in the mobile environment, mobile devices are used in many areas like banking, social media and communication. The common characteristic of applications in these fields is that they contain personal or financial information of users. These types of applications are developed for Android or IOS operating systems and have become the target of attackers. To detect weakness, security analysts, perform mobile penetration tests using security analysis tools. These analysis tools have advantages and disadvantages to each other. Some tools can prioritize static or dynamic analysis, others not including these types of tests. Within the scope of the current model, we are aim to gather security analysis tools under the penetration testing framework, also contributing analysis results by data fusion algorithm. With the suggested model, security analysts will be able to use these types of analysis tools in addition to using the advantage of fusion algorithms fed by analysis tools outputs.

Secure multi-party computation(SMPC) is an important research field in cryptography, secure multi-party computation has a wide range of applications in practice. Accordingly, information security issues have arisen. Aiming at security issues in Secure multi-party computation, we consider that semi-honest participants have malicious operations such as collusion in the process of information interaction, gaining an information advantage over honest parties through collusion which leads to deviations in the security of the protocol. To solve this problem, we combine information entropy to propose an n-round information exchange protocol, in which each participant broadcasts a relevant information value in each round without revealing additional information. Through the change of the uncertainty of the correct result value in each round of interactive information, each participant cannot determine the correct result value before the end of the protocol. Security analysis shows that our protocol guarantees the security of the output obtained by the participants after the completion of the protocol.

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.

Ransomware is a major malware attack experienced by large corporations and healthcare services. Ransomware employs the idea of cryptovirology, which uses cryptography to design malware. The goal of ransomware is to extort ransom by threatening the victim with the destruction of their data. Ransomware typically involves a 3-step process: analyzing the victim’s network traffic, identifying a vulnerability, and then exploiting it. Thus, the detection of ransomware has become an important undertaking that involves various sophisticated solutions for improving security. To further enhance ransomware detection capabilities, this paper focuses on an Application Programming Interface (API)-based ransomware detection approach in combination with machine learning (ML) techniques. The focus of this research is (i) understanding the life cycle of ransomware on the Windows platform, (ii) dynamic analysis of ransomware samples to extract various features of malicious code patterns, and (iii) developing and validating machine learning-based ransomware detection models on different ransomware and benign samples. Data were collected from publicly available repositories and subjected to sandbox analysis for sampling. The sampled datasets were applied to build machine learning models. The grid search hyperparameter optimization algorithm was employed to obtain the best fit model; the results were cross-validated with the testing datasets. This analysis yielded a high ransomware detection accuracy of 99.18% for Windows-based platforms and shows the potential for achieving high-accuracy ransomware detection capabilities when using a combination of API calls and an ML model. This approach can be further utilized with existing multilayer security solutions to protect critical data from ransomware attacks.

Considering the influence of load, This paper proposes a robust analysis method of cyber-physical power system based on the evolution of adjacency matrix. This method uses the load matrix to detect whether the system has overload failure, utilizes the reachable matrix to detect whether the system has unconnected failure, and uses the dependency matrix to reveal the cascading failure mechanism in the system. Finally, analyze the robustness of the cyber-physical power system. The IEEE30 standard node system is taken as an example for simulation experiment, and introduced the connectivity index and the load loss ratio as evaluation indexes. The robustness of the system is evaluated and analyzed by comparing the variation curves of connectivity index and load loss ratio under different tolerance coefficients. The results show that the proposed method is feasible, reduces the complexity of graph-based attack methods, and easy to research and analyze.

This paper presents conceptual modelling of the criticality of critical infrastructure (CI) nth order dependency effect using neural networks. Incidentally, critical infrastructures are usually not stand-alone, they are mostly interconnected in some way thereby creating a complex network of infrastructures that depend on each other. The relationships between these infrastructures can be either unidirectional or bidirectional with possible cascading or escalating effect. Moreover, the dependency relationships can take an nth order, meaning that a failure or disruption in one infrastructure can cascade to nth interconnected infrastructure. The nth-order dependency and criticality problems depict a sequential characteristic, which can result in chronological cyber effects. Consequently, quantifying the criticality of infrastructure demands that the impact of its failure or disruption on other interconnected infrastructures be measured effectively. To understand the complex relational behaviour of nth order relationships between infrastructures, we model the behaviour of nth order dependency using Neural Network (NN) to analyse the degree of dependency and criticality of the dependent infrastructure. The outcome, which is to quantify the Criticality Index Factor (CIF) of a particular infrastructure as a measure of its risk factor can facilitate a collective response in the event of failure or disruption. Using our novel NN approach, a comparative view of CIFs of infrastructures or organisations can provide an efficient mechanism for Critical Information Infrastructure Protection and resilience (CIIPR) in a more coordinated and harmonised way nationally. Our model demonstrates the capability to measure and establish the degree of dependency (or interdependency) and criticality of CIs as a criterion for a proactive CIIPR.

While various encryption algorithms ensure data security, it is essential to determine the accuracy and loss values and performance status in the analyzes made to determine encrypted data by deep learning. In this research, the analysis steps made by applying deep learning methods to encrypted cifar10 picture data are presented practically. The data was tried to be estimated by training with VGG16, VGG19, ResNet50 deep learning models. During this period, the network’s performance was tried to be measured, and the accuracy and loss values in these calculations were shown graphically.

As the traditional grids are transitioning to the smart grid, they are getting more prone to cyber-attacks. Among all the cyber-attack one of the most dangerous attack is false data injection attack. When this attack is performed with historical information of the data packet the attack goes undetected. As the false data is included for training and testing the model, the accuracy is decreased, and decision making is affected. In this paper we analyzed the impact of the false data injection attack(FDIA) on AI based smart grid. These analyses were performed using two different multi-layer perceptron architectures with one of the independent variables being compared and modified by the attacker. The root-mean squared values were compared with different models.

CAPTCHA or Completely Automated Public Turing test to Tell Computers and Humans Apart is a technique to distinguish between humans and computers by generating and evaluating tests that can be passed by humans but not computer bots. However, captchas are not foolproof, and they can be bypassed which raises security concerns. Hence, sites over the internet remain open to such vulnerabilities. This research paper identifies the vulnerabilities found in some of the commonly used captcha schemes by cracking them using Deep Learning techniques. It also aims to provide solutions to safeguard against these vulnerabilities and provides recommendations for the generation of secure captchas.

With the developing of computer technology, Convolutional Neural Network (CNN) has made big development in both application region and research field. However, CAPTCHA (one Turing Test to tell difference between computer and human) technology is also widely used in many websites verification process and it has received great attention from researchers. In this essay, we introduced the CNN based on tensorflow framework and use the MINIST data set which is used in handwritten digit recognition to analyze the parameters and the structure of the CNN model. Moreover, we use different activation functions and compares them with different epochs. We also analyze many problems during the experiment to make the original data and the result more accurate.

This paper considers methods of fingerprint protection in biometric authentication systems. Including methods of protecting fingerprint templates using zero digital watermarks and cryptography techniques. The paper considers a secure authentication model using cryptography and digital watermarks.

Data and veracious information are an important feature of any organization; it takes special care as a like asset of the organization. Cloud computing system main target to provide service to the user like high-speed access user data for storage and retrieval. Now, big concern is data protection in cloud computing technology as because data leaking and various malicious attacks happened in cloud computing technology. This study provides user data protection in the cloud storage device. The article presents the architecture of a data security hybrid infrastructure that protects and stores the user data from the unauthenticated user. In this hybrid model, we use a different type of security model.

The need for cyber resilience is increasingly important in our technology-dependent society, where computing systems, devices and data will continue to be the target of cyber attackers. Hence, we propose a conceptual framework called ‘Human-in-the-Loop Explainable-AI-Enabled Vulnerability Detection, Investigation, and Mitigation’ (HXAI-VDIM). Specifically, instead of resolving complex scenario of security vulnerabilities as an output of an AI/ML model, we integrate the security analyst or forensic investigator into the man-machine loop and leverage explainable AI (XAI) to combine both AI and Intelligence Assistant (IA) to amplify human intelligence in both proactive and reactive processes. Our goal is that HXAI-VDIM integrates human and machine in an interactive and iterative loop with security visualization that utilizes human intelligence to guide the XAI-enabled system and generate refined solutions.

In human-robot collaboration (HRC), human trust in the robot is the human expectation that a robot executes tasks with desired performance. A higher-level trust increases the willingness of a human operator to assign tasks, share plans, and reduce the interruption during robot executions, thereby facilitating human-robot integration both physically and mentally. However, due to real-world disturbances, robots inevitably make mistakes, decreasing human trust and further influencing collaboration. Trust is fragile and trust loss is triggered easily when robots show incapability of task executions, making the trust maintenance challenging. To maintain human trust, in this research, a trust repair framework is developed based on a human-to-robot attention transfer (H2R-AT) model and a user trust study. The rationale of this framework is that a prompt mistake correction restores human trust. With H2R-AT, a robot localizes human verbal concerns and makes prompt mistake corrections to avoid task failures in an early stage and to finally improve human trust. User trust study measures trust status before and after the behavior corrections to quantify the trust loss. Robot experiments were designed to cover four typical mistakes, wrong action, wrong region, wrong pose, and wrong spatial relation, validated the accuracy of H2R-AT in robot behavior corrections; a user trust study with 252 participants was conducted, and the changes in trust levels before and after corrections were evaluated. The effectiveness of the human trust repairing was evaluated by the mistake correction accuracy and the trust improvement.

Internet of Battlefield Things (IoBT) is the application of Internet of Things (IoT) to a battlefield environment. IoBT networks operate in difficult conditions due to high mobility and unpredictable nature of battle fields and securing them is a challenge. There is increasing interest to use deception techniques to enhance the security of IoBT networks. A honeypot is a system installed on a network as a trap to attract the attention of an attacker and it does not store any valuable data. In this work, we introduce IoBT dual sensor gateways. We propose a Reinforcement Learning (RL)-assisted scheme, in which the IoBT dual sensor gateways intelligently switch between honeypot and real function based on a threshold. The optimal threshold is determined using reinforcement learning approach that adapts to nodes reputation. To focus on the impact of the mobile and uncertain behavior of IoBT networks on the proposed scheme, we consider the nodes as moving vehicles. We statistically analyze the results of our RL-based scheme obtained using ns-3 network simulation, and optimize value of the threshold.

With the increasing number of electric vehicles, the scale of the market also increases. In the past, the electric vehicle market had problems such as opaque information, numerous levels and data leakage, which were criticized for the impact of the overall development and policies of the electric vehicle industry. In view of the problems existing in the transparency and security of big data management transactions of the Internet of vehicles, this paper combs the commercial operation framework of the Internet of Vehicles Platform, analyses the feasibility and necessity of establishing the token system of the Internet of Vehicles Platform, and constructs the token economic system architecture of the Internet of Vehicles Platform and its development path.