Biblio

Security is a critical aspect in the process of designing, developing, and testing software systems. Due to the increasing need for security-related skills within software systems, there is a growing demand for these skills to be taught in computer science. A series of security modules was developed not only to meet the demand but also to assess the impact of these modules on teaching critical cyber security topics in computer science courses. This full paper in the innovative practice category presents the outcomes of six security modules in a freshman-level course at two institutions. The study adopts a Model-Eliciting Activity (MEA) as a project for students to demonstrate an understanding of the security concepts. Two experimental studies were conducted: 1) Teaching effectiveness of implementing cyber security modules and MEA project, 2) Students’ experiences in conceptual modeling tasks in problem-solving. In measuring the effectiveness of teaching security concepts with the MEA project, students’ performance, attitudes, and interests as well as the instructor’s effectiveness were assessed. For the conceptual modeling tasks in problem-solving, the results of student outcomes were analyzed. After implementing the security modules with the MEA project, students showed a great understanding of cyber security concepts and an increased interest in broader computer science concepts. The instructor’s beliefs about teaching, learning, and assessment shifted from teacher-centered to student-centered during their experience with the security modules and MEA project. Although 64.29% of students’ solutions do not seem suitable for real-world implementation, 76.9% of the developed solutions showed a sufficient degree of creativity.

Web evolution and Web 2.0 social media tools facilitate communication and support the online economy. On the other hand, these tools are actively used by extremist, terrorist and criminal groups. These malicious groups use these new communication channels, such as forums, blogs and social networks, to spread their ideologies, recruit new members, market their malicious goods and raise their funds. They rely on anonymous communication methods that are provided by the new Web. This malicious part of the web is called the “dark web”. Dark web analysis became an active research area in the last few decades, and multiple research studies were conducted in order to understand our enemy and plan for counteract. We have conducted a systematic literature review to identify the state-of-art and open research areas in dark web analysis. We have filtered the available research papers in order to obtain the most relevant work. This filtration yielded 28 studies out of 370. Our systematic review is based on four main factors: the research trends used to analyze dark web, the employed analysis techniques, the analyzed artifacts, and the accuracy and confidence of the available work. Our review results have shown that most of the dark web research relies on content analysis. Also, the results have shown that forum threads are the most analyzed artifacts. Also, the most significant observation is the lack of applying any accuracy metrics or validation techniques by most of the relevant studies. As a result, researchers are advised to consider using acceptance metrics and validation techniques in their future work in order to guarantee the confidence of their study results. In addition, our review has identified some open research areas in dark web analysis which can be considered for future research work.

Using the methods of literature and interview, this paper analyzes the current situation of performance evaluation of volunteers in large-scale games based on mobile Internet, By analyzing the popularity of mobile Internet, the convenience of performance evaluation, the security and privacy of performance evaluation, this paper demonstrates the necessity of performance evaluation of volunteers in large-scale games based on mobile Internet, This paper puts forward the Countermeasures of performance evaluation of volunteers in large-scale games based on mobile Internet.

The current face recognition technology has rapidly come into the public life, from unlocking cell phone face to mobile payment, which has brought a lot of convenience to life. However, it is undeniable that it also brings security challenges. Based on this paper, we will discuss the risks of face recognition in the mobile payment and put forward relevant suggestions.

Forming a secure autonomous vehicle group is extremely challenging since we have to consider threats and vulnerability of autonomous vehicles. Existing studies focus on communications among risk-free autonomous vehicles, which lack metrics to measure passenger security and cargo values. This work proposes a novel autonomous vehicle group formation method. We introduce risk assessment scoring to assess passenger security and cargo values, and propose an autonomous vehicle group formation method based on it. Our vehicle group is composed of a master node, and a number of core and border ones. Finally, the extensive simulation results show that our method is better than a Connectivity Prediction-based Dynamic Clustering model and a Low-InDependently clustering architecture in terms of node survival time, average change count of master nodes, and average risk assessment scoring.

Controller Area Network with Flexible Data-rate(CAN FD) has the advantages of high bandwidth and data field length to meet the higher communication requirements of parallel in-vehicle applications. If the CAN FD lacking the authentication security mechanism is used, it is easy to make it suffer from masquerade attack. Therefore, a two-stage method based on message authentication is proposed to enhance the security of it. In the first stage, an anti-exhaustive message exchange and comparison algorithm is proposed. After exchanging the message comparison sequence, the lower bound of the vehicle application and redundant message space is obtained. In the second stage, an enhanced round accumulation algorithm is proposed to enhance security, which adds Message Authentication Codes(MACs) to the redundant message space in a way of fewer accumulation rounds. Experimental examples show that the proposed two-stage approach enables both small-scale and large-scale parallel in-vehicle applications security to be enhanced. Among them, in the Adaptive Cruise Control Application(ACCA), when the laxity interval is 1300μs, the total increased MACs is as high as 388Bit, and the accumulation rounds is as low as 40 rounds.

Nowadays, 5G has been widely used in various fields. People are starting to turn their attention to 6G. Therefore, at the beginning, this paper describes in detail the principle and performance of 6G, and introduces the key technologies of 6G, Cavity technology and THz technology. Based on the high-performance indicators of 6G, we then study the possible application changes brought by 6G, for example, 6G technology will make remote surgery and remote control possible. 6G technology will make remote surgery and remote control possible. 6G will speed up the interconnection of everything, allowing closer and faster connection between cars. Next, virtual reality is discussed. 6G technology will enable better development of virtual reality technology and enhance people's immersive experience. Finally, we present the issues that need to be addressed with 6G technology, such as cybersecurity issues and energy requirements. As well as the higher challenges facing 6G technology, such as connectivity and communication on a larger social plane.

In construction machinery, connectivity delivers higher advantages in terms of higher productivity, lower costs, and most importantly safer work environment. As the machinery grows more dependent on internet-connected technologies, data security and product cybersecurity become more critical than ever. These machines have more cyber risks compared to other automotive segments since there are more complexities in software, larger after-market options, use more standardized SAE J1939 protocol, and connectivity through long-distance wireless communication channels (LTE interfaces for fleet management systems). Construction machinery also operates throughout the day, which means connected and monitored endlessly. Till today, construction machinery manufacturers are investigating the product cybersecurity challenges in threat monitoring, security testing, and establishing security governance and policies. There are limited security testing methodologies on SAE J1939 CAN protocols. There are several testing frameworks proposed for fuzz testing CAN networks according to [1]. This paper proposes security testing methods (Fuzzing, Pen testing) for in-vehicle communication protocols in construction machinery.

The electromagnetic energy harvesting technology is a new and effective way to supply power to the condition monitoring sensors installed on or near the transmission line. We will use Computer Simulation Technology Software to simulate the different designs of stand-alone electromagnetic energy harvesters The power generated by energy harvesters of different design structures is compared and analyzed through simulation and experimental results. We then propose an improved design of energy harvester.

Currently, many organizations are moving to new digital management systems, which is accompanied not only by the introduction of new approaches based on the use of information technology, but also by a change in the organizational and management environment. Risk management is a process necessary to maintain the competitive advantage of an organization, but it can also become involved in the course of digitalization itself, which means that risk management also needs to change to meet modern conditions and ensure the effectiveness of the organization. This article discusses the risk management process in the digital environment. The main approach to the organization of this process is outlined, taking into account the use of information tools, together with the stages of this process, which directly affect the efficiency of the company. The risks that are specific to a digital organization are taken into account. Modern requirements for risk management for organizations are studied, ways of their implementation are outlined. The result is a risk management process that functions in a digital organization.

Haptic communications and 5G networks in conjunction with AI and robotics will augment the human user experience by enabling real-time task performance via the control of objects remotely. This represents a paradigm shift from content delivery-based networks to task-oriented networks for remote skill set delivery. The transmission of user skill sets in remote task performance marks the advent of a haptic-enabled Internet of Skills (IoS), through which the transmission of touch and actuation sensations will be possible. In this proposed research, a conceptual Task-Technology Fit (TTF) model of a haptic-enabled IoS is developed to link human users and haptic-enabled technologies to technology use and task performance between master (control) and remote (controlled) domains to provide a Quality of Experience (QoE) and Quality of Task (QoT) oriented perspective of a Haptic Internet. Future 5G-enabled applications promise the high availability, security, fast reaction speeds, and reliability characteristics required for the transmission of human user skills over large geographical distances. The 5G network and haptic-enabled IoS considered in this research will support a number of critical applications. One such novel scenario in which a TTF of a Haptic Internet can be modelled is the use case of remote-controlled Unmanned Aerial Vehicles (UAVs). This paper is a contribution towards the realization of a 5G network and haptic-enabled QoE-QoT-centric IoS for augmented user task performance. Future empirical results of this research will be useful to understanding the role that varying degrees of a fit between context-specific task and technology characteristics play in influencing the impact of haptic-enabled technology use for real-time immersive remote UAV (drone) control task performance.

Homomorphic Encryption (HE) [11] draws significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Among numerous FHE schemes [2]–[4], [8], [9], HE for Arithmetic of Approximate Numbers (HEAAN [3]), which is also known as CKKS (Cheon-Kim-Kim-Song), is rapidly gaining popularity [10] as it supports computation on real numbers. A critical shortcoming of HE is the high computational complexity of ciphertext arithmetic, especially, HE multiplication (HE Mul). For example, the execution time for computation on encrypted data (ciphertext) increases from 100s to 10,000s of times compared to that on native, unen-crypted messages. However, a large body of HE acceleration studies, including ones exploiting GPUs and FPGAs, lack a rigorous analysis of computational complexity and data access patterns of HE Mul with large parameter sets on CPUs, the most popular computing platform.

To ensure comprehensive information protection, it is necessary to use various means of information protection, distributed by levels and segments of the information system. This creates a contradiction, which consists in the presence of many different means of information protection and the inability to ensure their joint coordinated application in ensuring the protection of information due to the lack of an automated control system. One of the tasks that contribute to the solution of this problem is the task of generating a feasible organizational structure and the structure of such an automated control system, the results of which would provide these options and choose the one that is optimal under given initial parameters and limitations. The problem is solved by reducing the General task with particular splitting the original graph of the automated cyber defense control system into subgraphs. As a result, the organizational composition and the automated cyber defense management system structures will provide a set of acceptable variants, on the basis of which the optimal choice is made under the given initial parameters and restrictions. As a result, admissible variants for the formation technique of organizational structure and structure by the automated control system of cyber defense is received.

5G and beyond 5G low power wireless networks make Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications capable of serving massive amounts of devices and machines. Due to the broadcast nature of wireless networks, it is crucial to secure the communication between these devices and machines from spoofing and interception attacks. This paper is concerned with the security of carrier frequency offset (CFO) based continuous physical layer authentication. The interaction between an attacker and a defender is modeled as a dynamic discrete leader-follower game with imperfect information. In the considered model, a legitimate user (Alice) communicates with the defender/operator (Bob) and is authorized by her CFO continuously. The attacker (Eve), by listening/eavesdropping the communication between Alice and Bob, tries to learn the CFO characteristics of Alice and aims to inject malicious packets to Bob by impersonating Alice. First, by showing that the optimal attacker strategy is a threshold policy, an optimization problem of the attacker with exponentially growing action space is reduced to a tractable integer optimization problem with a single parameter, then the corresponding defender cost is derived. Extensive simulations illustrate the characteristics of optimal strategies/utilities of the players depending on the actions, and show that the defender’s optimal false positive rate causes attack success probabilities to be in the order of 0.99. The results show the importance of the parameters while finding the balance between system security and efficiency.

Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce trust in media. Therefore detection of fake videos has garnered immense interest in academia and industry. Recently developed Deepfake detection methods rely on Deep Neural Networks (DNNs) to distinguish AI-generated fake videos from real videos. In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods. We further demonstrate that our adversarial perturbations are robust to image and video compression codecs, making them a real-world threat. We present pipelines in both white-box and black-box attack scenarios that can fool DNN based Deepfake detectors into classifying fake videos as real.

The Internet of vehicles is emerging as an exciting application to improve safety and providing better services in the form of active road signs, pay-as-you-go insurance, electronic toll, and fleet management. Internet connected vehicles are exposed to new attack vectors in the form of cyber threats and with the increasing trend of cyber attacks, the success of autonomous vehicles depends on their security. Existing techniques for IoV security are based on the un-realistic assumption that all the vehicles are equipped with the same hardware (at least in terms of computational capabilities). However, the hardware platforms used by various vehicle manufacturers are highly heterogeneous. Therefore, a security protocol designed for IoVs should be able to detect the computational capabilities of the underlying platform and adjust the security primitives accordingly. To solve this issue, this paper presents a technique for algorithm-hardware separation for IoV security. The proposed technique uses an iterative routine and the corresponding execution time to detect the computational capabilities of a hardware platform using an artificial intelligence based inference engine. The results on three different commonly used micro-controllers show that the proposed technique can effectively detect the type of hardware platform with up to 100% accuracy.

Authentication, forms an important step in any security system to allow access to resources that are to be restricted. In this paper, we propose a novel artificial intelligence-assisted risk-based two-factor authentication method. We begin with the details of existing systems in use and then compare the two systems viz: Two Factor Authentication (2FA), Risk-Based Two Factor Authentication (RB-2FA) with each other followed by our proposed AIA-RB-2FA method. The proposed method starts by recording the user features every time the user logs in and learns from the user behavior. Once sufficient data is recorded which could train the AI model, the system starts monitoring each login attempt and predicts whether the user is the owner of the account they are trying to access. If they are not, then we fallback to 2FA.

The article describes an analytical model of the actions of an information security violator for the secret extraction of confidential information processed on the protected object in terms of the theory of Markov random processes. The characteristics of the existing models are given, as well as the requirements that are imposed on the model for simulating the process. All model states are described in detail, as well as the data flow that is used in the process simulation. The model is represented as a directed state graph. It also describes the option for evaluating the data obtained during modeling. In the modern world, with the developing methods and means of covert extraction of information, the problem of assessing the damage that can be caused by the theft of the organization's data is acute. This model can be used to build a model of information security threats.

Currently, statistical tests for random number generators (RNGs) are widely used in practice, and some of them are even included in information security standards. But despite the popularity of RNGs, consistent tests are known only for stationary ergodic deviations of randomness (a test is consistent if it detects any deviations from a given class when the sample size goes to infinity). However, the model of a stationary ergodic source is too narrow for some RNGs, in particular, for generators based on physical effects. In this article, we propose computable consistent tests for some classes of deviations more general than stationary ergodic and describe some general properties of statistical tests. The proposed approach and the resulting test are based on the ideas and methods of information theory.

This paper presents a method to improve the diagnosability of power network under multiple faults. In this paper, the steps of fault diagnosis are as follows: first, constructing finite automata model of the diagnostic system; then, a fault diagnoser model is established through coupling operation and trajectory reasoning mechanism; finally, the diagnosis results are obtained through this model. In this paper, the judgment basis of diagnosability is defined. Then, based on the existing diagnosis results, the information available can be increased by adding sensor devices, to achieve the purpose of diagnosability in the case of multiple faults of the system.

The report examines approaches to assessing the information security of data transmission networks (DTN). The analysis of methods for quantitative assessment of information security risks is carried out. A methodological approach to the assessment of IS DTN based on the risk-oriented method is presented. A method for assessing risks based on the mathematical apparatus of the queening systems (QS) is considered and the problem of mathematical modeling is solved.

This project is an extension of ongoing research on Fully Homomorphic Encryption - Arithmetic Circuit Homomorphic Encryption. This paper focus on the implementation of pairing algorithm Supersingular Isogeny Diffie Hellman Key Exchange into Arithmetic Circuit Homomorphic Encryption as well as comparison and analyse with Elliptic Curve Diffie Hellman. Next, the paper will discuss on the latencies incurred due to pairing sessions between machines, key generations, key sizes, CPU usage and overall latency for the two respective key exchange methods to be compared against each other.

This paper studies the technology of generating DDPG (deep deterministic policy gradient) by using the deep dual network and experience pool network structure, and puts forward the sampling strategy gradient algorithm to randomly select actions according to the learned strategies (action distribution) in the continuous action space, based on the dispatching control system of the power dispatching control center of a super city power grid, According to the actual characteristics and operation needs of urban power grid, The developed refined artificial intelligence on-line security analysis and emergency response plan intelligent generation function realize the emergency response auxiliary decision-making intelligent generation function. According to the hidden danger of overload and overload found in the online safety analysis, the relevant load lines of the equipment are searched automatically. Through the topology automatic analysis, the load transfer mode is searched to eliminate or reduce the overload or overload of the equipment. For a variety of load transfer modes, the evaluation index of the scheme is established, and the optimal load transfer mode is intelligently selected. Based on the D5000 system of Metropolitan power grid, a multi-objective and multi resource coordinated security risk decision-making assistant system is implemented, which provides integrated security early warning and decision support for the main network and distribution network of city power grid. The intelligent level of power grid dispatching management and dispatching operation is improved. The state reality network can analyze the joint state observations from the action reality network, and the state estimation network uses the actor action as the input. In the continuous action space task, DDPG is better than dqn and its convergence speed is faster.

The Internet of Vehicles (IoV) can offer safe and comfortable driving experiences with the cooperation communications between central servers and cache-enabled road side units (RSUs) as edge severs, which also can provide high-speed, high-quality and high-stability communication access for vehicle users (VUs). However, due to the huge popular traffic volume, the burden of backhaul link will be seriously enlarged, which will greatly degrade the service experience of the IoV. In order to alleviate the backhaul load of IoV network, in this paper, we propose an asynchronous coded caching strategy composed of two phases, i.e., content placement and asynchronous coded transmission. The asynchronous request and request deadline are closely considered to design our asynchronous coded transmission algorithm. Also, we derive the close-form expression of average backhaul load under the nonuniform demands of IoV users. Finally, we formulate an optimization problem of minimizing average backhaul load and obtain the optimized content placement vector. Simulation results verify the feasibility of our proposed strategy under the asynchronous situation.

Advances in artificial intelligence, machine learning, and robotics have profoundly impacted the field of autonomous navigation and driving. However, sensor spoofing attacks can compromise critical components and the control mechanisms of mobile robots. Therefore, understanding vulnerabilities in autonomous driving and developing countermeasures remains imperative for the safety of unmanned vehicles. Hence, we demonstrate cross-validation techniques for detecting spoofing attacks on the sensor data in autonomous driving in this work. First, we discuss how visual and inertial odometry (VIO) algorithms can provide a root-of-trust during navigation. Then, we develop examples for sensor data spoofing attacks using the open-source driving dataset. Next, we design an attack detection technique using VIO algorithms that cross-validates the navigation parameters using the IMU and the visual data. Following, we consider hardware-dependent attack survival mechanisms that support an autonomous system during an attack. Finally, we also provide an example of spoofing survival technique using on-board hardware oscillators. Our work demonstrates the applicability of classical mobile robotics algorithms and hardware security primitives in defending autonomous vehicles from targeted cyber attacks.