Biblio

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.

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.

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.

Game theory is a branch of modern mathematics, which is a mathematical method to study how decision-makers should make decisions in order to strive for the maximum interests in the process of competition. In this paper, from the perspective of offensive and defensive confrontation, using game theory for reference, we build a dynamic evaluation model of information system security risk based on static game model. By using heisani transformation, the uncertainty of strategic risk of offensive and defensive sides is transformed into the uncertainty of each other's type. The security risk of pure defense strategy and mixed defense strategy is analyzed quantitatively, On this basis, an information security risk assessment algorithm based on static game model is designed.

The fusion of peer-to-peer (P2P) fog network and the traditional three-tier fog computing architecture allows fog devices to conjointly pool their resources together for improved service provisioning and better bandwidth utilization. However, any unauthorized access to the fog network may have calamitous consequences. In this paper, a new lightweight two-party authenticated and key agreement (AKA) protocol is proposed for fog-to-fog collaboration. The security analysis of the protocol reveals that it is resilient to possible attacks. Moreover, the validation of the protocol conducted using the broadly-accepted Automated Verification of internet Security Protocols and Applications (AVISPA) shows that it is safe for practical deployment. The performance evaluation in terms of computation and communication overheads demonstrates its transcendence over the state-of-the-art protocols.

Due to the shortage of resources and services, agents are often in competition with each other. Excessive competition will lead to a social dilemma. Under the viewpoint of breaking social dilemma, we present a novel trust-based logic framework called Trust Computation Logic (TCL) for measure method to find the best partners to collaborate and automatically verifying trust in Multi-Agent Systems (MASs). TCL starts from defining trust state in Multi-Agent Systems, which is based on contradistinction between behavior in trust behavior library and in observation. In particular, a set of reasoning postulates along with formal proofs were put forward to support our measure process. Moreover, we introduce symbolic model checking algorithms to formally and automatically verify the system. Finally, the trust measure method and reported experimental results were evaluated by using DeepMind’s Sequential Social Dilemma (SSD) multi-agent game-theoretic environments.

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.

The course of operating system's labs usually fall behind the state of art technology. In this paper, we propose a Software Diversity-Assisted Defense (SDAD) lab based on software diversity, mainly targeting for students majoring in cyber security and computer science. This lab is consisted of multiple modules and covers most of the important concepts and principles in operating systems. Thus, the knowledge learned from the theoretical course will be deepened with the lab. For students majoring in cyber security, they can learn this new software diversity-based defense technology and understand how an exploit works from the attacker's side. The experiment is also quite stretchable, which can fit all level students.

With the development of 5G technology and intelligent terminals, the future direction of the Industrial Internet of Things (IIoT) evolution is Pervasive Edge Computing (PEC). In the pervasive edge computing environment, intelligent terminals can perform calculations and data processing. By migrating part of the original cloud computing model's calculations to intelligent terminals, the intelligent terminal can complete model training without uploading local data to a remote server. Pervasive edge computing solves the problem of data islands and is also successfully applied in scenarios such as vehicle interconnection and video surveillance. However, pervasive edge computing is facing great security problems. Suppose the remote server is honest but curious. In that case, it can still design algorithms for the intelligent terminal to execute and infer sensitive content such as their identity data and private pictures through the information returned by the intelligent terminal. In this paper, we research the problem of honest but curious remote servers infringing intelligent terminal privacy and propose a differential privacy collaborative deep learning algorithm in the pervasive edge computing environment. We use a Gaussian mechanism that meets the differential privacy guarantee to add noise on the first layer of the neural network to protect the data of the intelligent terminal and use analytical moments accountant technology to track the cumulative privacy loss. Experiments show that with the Gaussian mechanism, the training data of intelligent terminals can be protected reduction inaccuracy.

Recommender systems have been proved to be effective techniques to provide users with better experiences. However, when a recommender knows the user's preference characteristics or gets their sensitive information, then a series of privacy concerns are raised. A amount of solutions in the literature have been proposed to enhance privacy protection degree of recommender systems. Although the existing solutions have enhanced the protection, they led to a decrease in recommendation accuracy simultaneously. In this paper, we propose a security-aware hybrid recommendation method by combining the factorization and regression techniques. Specifically, the differential privacy mechanism is integrated into data pre-processing for data encryption. Firstly data are perturbed to satisfy differential privacy and transported to the recommender. Then the recommender calculates the aggregated data. However, applying differential privacy raises utility issues of low recommendation accuracy, meanwhile the use of a single model may cause overfitting. In order to tackle this challenge, we adopt a fusion prediction model by combining linear regression (LR) and matrix factorization (MF) for collaborative recommendation. With the MovieLens dataset, we evaluate the recommendation accuracy and regression of our recommender system and demonstrate that our system performs better than the existing recommender system under privacy requirement.

In Social Aware Network (SAN) model, the elementary actions focus on investigating the attributes and behaviors of the customer. This analysis of customer attributes facilitate in the design of highly active and improved protocols. In specific, the recommender systems are highly vulnerable to the shilling attack. The recommender system provides the solution to solve the issues like information overload. Collaborative filtering based recommender systems are susceptible to shilling attack known as profile injection attacks. In the shilling attack, the malicious users bias the output of the system's recommendations by adding the fake profiles. The attacker exploits the customer reviews, customer ratings and fake data for the processing of recommendation level. It is essential to detect the shilling attack in the network for sustaining the reliability and fairness of the recommender systems. This article reviews the most prominent issues and challenges of shilling attack. This paper presents the literature survey which is contributed in focusing of shilling attack and also describes the merits and demerits with its evaluation metrics like attack detection accuracy, precision and recall along with different datasets used for identifying the shilling attack in SAN network.

The data-driven period produces more and more security-related challenges that even experts can hardly deal with. One of the most complex threats is ransomware, which is very taxing and devastating to detect and mainly prevent. Our research methods showed significant results in identifying ransomware processes using the honeypot concept augmented with symbolic linking to reduce damage made to the file system. The CIA (confidentiality, integrity, availability) metrics have been adhered to. We propose to optimize the malware process termination procedure and introduce an artificial intelligence-human collaboration to enhance ransomware classification and detection.

Cyber-security training has evolved into an imperative need, aiming to provide cyber-security professionals with the knowledge and skills required to confront cyber-attacks that are increasing in number and sophistication. Training activities are typically associated with evaluation means, aimed to assess the extent to which the trainee has acquired the knowledge and skills whose development is targeted by the training programme, while cyber-security awareness and skill level evaluation means may be used to support additional security-related aspects of organizations. In this paper, we review trainee performance assessment metrics in cyber-security training, aiming to assist designers of cyber-security training activities to identify the most prominent trainee performance assessment means for their training programmes, while additional research directions involving cyber-security training evaluation metrics are also identified.

Cyber resilience has become a strategic point of information security in recent years. In the face of complex attack means and severe internal and external threats, it is difficult to achieve 100% protection against information systems. It is necessary to enhance the continuous service of information systems based on network resiliency and take appropriate compensation measures in case of protection failure, to ensure that the mission can still be achieved under attack. This paper combs the definition, cycle, and state of cyber resilience, and interprets the cyber resiliency engineering framework, to better understand cyber resilience. In addition, we also discuss the evolution of security architecture and analyze the impact of cyber resiliency on security architecture. Finally, the strategies and schemes of enhancing cyber resilience represented by zero trust and endogenous security are discussed.

Cyber ranges are proven to be effective towards the direction of cyber security training. Nevertheless, the existing literature in the area of cyber ranges does not cover, to our best knowledge, the field of 5G security training. 5G networks, though, reprise a significant field for modern cyber security, introducing a novel threat landscape. In parallel, the demand for skilled cyber security specialists is high and still rising. Therefore, it is of utmost importance to provide all means to experts aiming to increase their preparedness level in the case of an unwanted event. The EU funded SPIDER project proposes an innovative Cyber Range as a Service (CRaaS) platform for 5G cyber security testing and training. This paper aims to present the evaluation framework, followed by SPIDER, for the extraction of the user requirements. To validate the defined user requirements, SPIDER leveraged of questionnaires which included both closed and open format questions and were circulated among the personnel of telecommunication providers, vendors, security service providers, managers, engineers, cyber security personnel and researchers. Here, we demonstrate a selected set of the most critical questions and responses received. From the conducted analysis we reach to some important conclusions regarding 5G testing and training capabilities that should be offered by a cyber range, in addition to the analysis of the different perceptions between cyber security and 5G experts.

Professional training is essential for organizations to successfully defend their assets against cyber-attacks. Successful detection and prevention of security incidents demands that personnel is not just aware about the potential threats, but its security expertise goes far beyond the necessary background knowledge. To fill-in the gap for competent security professionals, platforms offering realistic training environments and scenarios are designed that are referred to as cyber-ranges. Multiple cyber-ranges listed under a common platform can simulate more complex environments, referred as cyber-range federations. Security education approaches often implement gamification mechanics to increase trainees’ engagement and maximize the outcome of the training process. Scoring is an integral part of a gamification scheme, allowing both the trainee and the trainer to monitor the former’s performance and progress. In this article, a novel scoring model is presented that is designed to be agnostic with respect to the source of information: either a CR or a variety of different CRs being part of a federated environment.

Higher education is increasingly called upon to enhance cyber education, including hands-on "experiential" training. The good news is that additional tools and techniques are becoming more available, both in-house and through third parties, to provide cyber training environments and simulations at various features and price points. However, the training thus far has only focused on "traditional" Cybersecurity that lightly touches on wireless in undergraduate and master's degree programs, and certifications. The purpose of this research is to identify and recognize nascent cyber training emphasizing a broader spectrum of wireless security and encourage curricular development that includes critical experiential training. Experiential wireless security training is important to keep pace with the growth in wireless communication mediums and associated Internet of Things (IoT) and Cyber Physical System (CPS) applications. Cyber faculty at a university offering undergraduate and master's Cybersecurity degrees authored this paper; both degrees are offered to resident as well as online students.

The current supply of a highly specialized cyber security professionals cannot meet the demands for societies seeking digitization. To close the skill gap, there is a need for introducing students in higher education to cyber security, and to combine theoretical knowledge with practical skills. This paper presents how the cyber security training platform Haaukins, initially developed to increase interest and knowledge of cyber security among high school students, was further developed to support the need for training in higher education. Based on the differences between the existing and new target audiences, a set of design principles were derived which shaped the technical adjustments required to provide a suitable platform - mainly related to dynamic tooling, centralized access to exercises, and scalability of the platform to support courses running over longer periods of time. The implementation of these adjustments has led to a series of teaching sessions in various institutions of higher education, demonstrating the viability for Haaukins for the new target audience.

Modbus is a popular industrial communication protocol supported by most automation devices. Despite its popularity, it is not a secure protocol because when it was developed, security was not a concern due to closed environments of industrial control systems. With the convergence of information technology and operational technology in recent years, the security of industrial control systems has become a serious concern. Due to the high availability requirements, it is not practical or feasible to do security experimentation of production systems. We present an implementation of cyber-physical systems with Modbus/TCP communication for real-time security testing. The proposed architecture consists of a process simulator, an IEC 61131-3 compliant programmable logic controller, and a human-machine interface, all communicating via Modbus/TCP protocol. We use Simulink as the process simulator. It does not have built-in support for the Modbus protocol. A contribution of the proposed work is to extend the functionality of Simulink with a custom block to enable Modbus communication. We use two case studies to demonstrate the utility of the cyber-physical system architecture. We can model complex industrial processes with this architecture, can launch cyber-attacks, and develop protection mechanisms.

With China's overall national strength, the education of studying in China has entered a period of rapid development, and China has become one of the important destination countries for international student mobility. With political stability, rapid economic development, and continuous improvement in the quality of higher education, the educational value of studying in China is increasingly recognized by international students. International students study and live in the same way as domestic students. While the development of the Internet has brought convenience to people, it has also created many security risks. How to protect the information security of international students is the focus of this paper. This paper introduces the classification, characteristics and security risks of international students' personal information. In order to protect the private data of international students from being leaked, filtering rules are set in the campus network through WinRoute firewall to effectively prevent information from being leaked, tampered or deleted, which can be used for reference by other universities.

The use of gamified learning platforms as a method of introducing cyber security education, training and awareness has risen greatly. With this rise, the availability of platforms to create, host or otherwise provide the challenges that make up the foundation of this education has also increased. In order to identify the best of these platforms, we need a method to compare their feature sets. In this paper, we compare related work on identifying the best platforms for a gamified cyber security learning platform as well as contemporary literature that describes the most needed feature sets for an ideal platform. We then use this to develop a metric for comparing these platforms, before then applying this metric to popular current platforms.

In this study, it is aimed to reduce False-Alarm levels and increase the correct detection rate in order to reduce this uncertainty. Within the scope of the study, 13157 SQLi and XSS type malicious and 10000 normal HTTP Requests were used. All HTTP requests were received from the same web server, and it was observed that normal requests and malicious requests were close to each other. In this study, a novel approach is presented via both digitization and expressing the data with words in the data preprocessing stages. LSTM, MLP, CNN, GNB, SVM, KNN, DT, RF algorithms were used for classification and the results were evaluated with accuracy, precision, recall and F1-score metrics. As a contribution of this study, we can clearly express the following inferences. Each payload even if it seems different which has the same impact maybe that we can clearly view after the preprocessing phase. After preprocessing we are calculating euclidean distances which brings and gives us the relativity between expressions. When we put this relativity as an entry data to machine learning and/or deep learning models, perhaps we can understand the benign request or the attack vector difference.

With the developing technology, cyber threats are developing rapidly, and the motivations and targets of cyber attackers are changing. In order to combat these threats, cyber threat information that provides information about the threats and the characteristics of the attackers is needed. In addition, it is of great importance to cooperate with other stakeholders and share experiences so that more information about threat information can be obtained and necessary measures can be taken quickly. In this context, in this study, it is stated that the establishment of a cooperation mechanism in which cyber threat information is shared will contribute to the cyber security capacity of organizations. And using the Zack Information Gap analysis, the deficiency of organizations in sharing threat information were determined and suggestions were presented. In addition, there are cooperation mechanisms in the USA and the EU where cyber threat information is shared, and it has been evaluated that it would be beneficial to establish a similar mechanism in our country. Thus, it is evaluated that advanced or unpredictable cyber threats can be detected, the cyber security capacities of all stakeholders will increase and a safer cyber ecosystem will be created. In addition, it is possible to collect, store, distribute and share information about the analysis of cyber incidents and malware analysis, to improve existing cyber security products or to encourage new product development, by carrying out joint R&D studies among the stakeholders to ensure that domestic and national cyber security products can be developed. It is predicted that new analysis methods can be developed by using technologies such as artificial intelligence and machine learning.

One of the topics that have successful applications in engineering technologies and computer science is chaos theory. The remarkable area among these successful applications has been especially the subject of chaos-based cryptology. Many practical applications have been proposed in a wide spectrum from image encryption algorithms to random number generators, from block encryption algorithms to hash functions based on chaotic systems. Logistics map is one of the chaotic systems that has been the focus of attention of researchers in these applications. Since, Logistic map can be shown as the most widely used chaotic system in chaos-based cryptology studies due to its simple mathematical structure and its characterization as a strong entropy source. However, in some studies, researchers stated that the behavior displayed in relation to the dynamics of the Logistic map may pose a problem for cryptology applications. For this reason, alternative studies have been carried out using different chaotic systems. In this study, it has been investigated which one is more suitable for cryptographic applications for five different derivatives of the Logistic map. In the study, a substitution box generator program has been implemented using the Logistic map and its five different derivatives. The generated outputs have been tested for five basic substitution box design criteria. Analysis results showed that the proposals for maps derived from Logistic map have a more robust structure than many studies in the literature.

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.