Biblio

This paper presents a definition of a secure system and design principles, which help govern security policies within an embedded system. By understanding a secure system, a common system on chip (SoC) architecture is evaluated and their vulnerabilities explored. This effort helped define requirements for a framework for a secure and isolated SoC architecture for users to develop in. Throughout this paper, a SoC architecture framework for isolated domains has been proposed and its robustness verified against different attack scenarios. To support different levels of criticality and complexity in developing user applications, three computing domains were proposed: security and safety critical (SSC) domain, high performance (HP) domain, and sandbox domain. These domains allow for complex applications to be realized with varying levels of security. Isolation between different computing domains is established using consumer off the shelf (COTS) techniques and architectural components provided by the Zynq Ultrascale+ (ZU+) multiprocessor SoC (MPSoC). To the best of our knowledge, this is the first work that implements a secure system design on the ZU+ platform. There have been many other implementations in hardware security to mitigate certain attack scenarios such as side channel attacks, temporal attacks, hardware trojans, etc. However, our work is different than others, as it establishes the framework for isolated computing domains for secure applications and also verifies system security by attacking one domain from the others.

In order to prevent malicious environment, more and more applications use anti-sandbox technology to detect the running environment. Malware often uses this technology against analysis, which brings great difficulties to the analysis of applications. Research on anti-sandbox countermeasure technology based on application virtualization can solve such problems, but there is no good solution for sensor simulation. In order to prevent detection, most detection systems can only use real device sensors, which brings great hidden dangers to users’ privacy. Aiming at this problem, this paper proposes and implements a sensor anti-sandbox countermeasure technology for Android system. This technology uses the CNN-LSTM model to identify the activity of the real machine sensor data, and according to the recognition results, the real machine sensor data is classified and stored, and then an automatic data simulation algorithm is designed according to the stored data, and finally the simulation data is sent back by using the Hook technology for the application under test. The experimental results show that the method can effectively simulate the data characteristics of the acceleration sensor and prevent the triggering of anti-sandbox behaviors.

To train new staff to be efficient and ready for the tasks assigned is vital. They must be equipped with knowledge and skills so that they can carry out their responsibility to ensure smooth daily working activities. As transitioning to IPv6 has taken place for more than a decade, it is understood that having a dual-stack network is common in any organization or enterprise. However, many Internet users may not realize the importance of IPv6 security due to a lack of awareness and knowledge of cyber and computer security. Therefore, this paper presents an approach to educating people by introducing a security mechanisms model that can be applied in handling security challenges via network sandboxing by setting up an isolated dual stack network testbed using GNS3 to perform network security analysis. The finding shows that applying security mechanisms such as access control lists (ACLs) and host-based firewalls can help counter the attacks. This proves that knowledge and skills to handle dual-stack security are crucial. In future, more kinds of attacks should be tested and also more types of security mechanisms can be applied on a dual-stack network to provide more information and to provide network engineers insights on how they can benefit from network sandboxing to sharpen their knowledge and skills.

Network security isolation technology is an important means to protect the internal information security of enterprises. Generally, isolation is achieved through traditional network devices, such as firewalls and gatekeepers. However, the security rules are relatively rigid and cannot better meet the flexible and changeable business needs. Through the double sandbox structure created for each user, each user in the virtual machine is isolated from each other and security is ensured. By creating a virtual disk in a virtual machine as a user storage sandbox, and encrypting the read and write of the disk, the shortcomings of traditional network isolation methods are discussed, and the application of cloud desktop network isolation technology based on VMwarer technology in universities is expounded.

Using multi-UAV systems to accomplish both civil and military missions is becoming a popular trend. With the development of software and hardware technologies, Unmanned aerial vehicles (UAVs) are now able to operate autonomously at edge. However, the remote control of manned systems, e.g., ground control station (GCS), remains essential to mission success, and the system's control and non-payload communication (CNPC) are facing severe cyber threats caused by smart attacks. To avoid hijacking, in this paper, we propose a secure mechanism that reduces such security risks for multi-UAV systems. We introduce friendly jamming from UAVs to block eavesdropping on the remote control channel. The trade-off between security and energy consumption is optimized by three approaches designed for UAV and GCS under algorithms of different complexities. Numerical results show the approach efficiency under different mission conditions and security demands, and demonstrate the features of the proposed mechanism for various scenarios.

ROS 2 is rapidly becoming a standard in the robotics industry. Built upon DDS as its default communication middleware and used in safety-critical scenarios, adding secu-rity to robots and ROS computational graphs is increasingly becoming a concern. The present work introduces SROS2, a series of developer tools and libraries that facilitate adding security to ROS 2 graphs. Focusing on a usability-centric approach in SROS2, we present a methodology for securing graphs systematically while following the DevSecOps model. We also demonstrate the use of our security tools by presenting an application case study that considers securing a graph using the popular Navigation2 and SLAM Toolbox stacks applied in a TurtieBot3 robot. We analyse the current capabilities of SROS2 and discuss the shortcomings, which provides insights for future contributions and extensions. Ultimately, we present SROS2 as usable security tools for ROS 2 and argue that without usability, security in robotics will be greatly impaired.

Internet of Medical Things (IoMT) is a rapidly growing branch of IoT (Internet of Things), which requires special treatment to cyber security due to confidentiality of healthcare data and patient health threat. Healthcare data and automated medical devices might become vulnerable targets of malicious cyber-attacks. While a large number of robotic applications, including medical and healthcare, employ robot operating system (ROS) as their backbone, not enough attention is paid for ROS security. The paper discusses a security of ROS-based swing doors automation in the context of a robotic hospital framework, which should be protected from cyber-attacks.

Systems for relative localization in multi-robot systems based on ultra-wideband (UWB) ranging have recently emerged as robust solutions for GNSS-denied environments. Scalability remains one of the key challenges, particularly in adhoc deployments. Recent solutions include dynamic allocation of active and passive localization modes for different robots or nodes in the system. with larger-scale systems becoming more distributed, key research questions arise in the areas of security and trustability of such localization systems. This paper studies the potential integration of collaborative-decision making processes with distributed ledger technologies. Specifically, we investigate the design and implementation of a methodology for running an UWB role allocation algorithm within smart contracts in a blockchain. In previous works, we have separately studied the integration of ROS2 with the Hyperledger Fabric blockchain, and introduced a new algorithm for scalable UWB-based localization. In this paper, we extend these works by (i) running experiments with larger number of mobile robots switching between different spatial configurations and (ii) integrating the dynamic UWB role allocation algorithm into Fabric smart contracts for distributed decision-making in a system of multiple mobile robots. This enables us to deliver the same functionality within a secure and trustable process, with enhanced identity and data access management. Our results show the effectiveness of the UWB role allocation for continuously varying spatial formations of six autonomous mobile robots, while demonstrating a low impact on latency and computational resources of adding the blockchain layer that does not affect the localization process.

Robot Operating System 2 (ROS2) is the latest release of a framework for enabling robot applications. Data Distribution Service (DDS) middleware is used for communication between nodes in a ROS2 cluster. The DDS middleware provides a distributed discovery system, message definitions and serialization, and security. In ROS2, the DDS middleware is accessed through an abstraction layer, making it easy to switch from one implementation to another. The existing middleware implementations differ in a number of ways, e.g., in how they are supported in ROS2, in their support for the security features, their ease of use, their performance, and their interoperability. In this work, the focus is on the ease of use, interoperability, and security features aspects of ROS2 DDS middleware. We compare the ease of installation and ease of use of three different DDS middleware, and test the interoperability of different middleware combinations in simple deployment scenarios. We highlight the difference that enabling the security option makes to interoperability, and conduct performance experiments that show the effect that turning on security has on the communication performance. Our results provide guidelines for choosing and deploying DDS middleware on a ROS2 cluster.

AbuSaif is a human-like social robot designed and built at the UAE University's Artificial Intelligence and Robotics Lab. AbuSaif was initially operated by a classical personal computer (PC), like most of the existing social robots. Thus, most of the robot's functionalities are limited to the capacity of that mounted PC. To overcome this, in this study, we propose a web-based platform that shall take the benefits of clustering in cloud computing. Our proposed platform will increase the operational capability and functionality of AbuSaif, especially those needed to operate artificial intelligence algorithms. We believe that the robot will become more intelligent and autonomous using our proposed web platform.

The Robotic Operating System (ROS) is a popular framework for robotics research and development. It's a system that provides hardware abstraction with low-level device management to handle communications and services. ROS is a distributed system, which allows various nodes in a network to communicate using a method such as message passing. When integrating systems using ROS, it is vital to consider the security and privacy of the data and information shared across ROS nodes, which is considered to be one of the most challenging aspects of ROS systems. The goal of this study is to examine the ROS architecture, primary components, and versions, as well as the types of vulnerabilities that might compromise the system. In order to achieve the CIA's three fundamental security criteria on a ROS-based platform, we categorized these vulnerabilities and looked into various security solutions proposed by researchers. We provide a comparative analysis of the ROS-related security solutions, the security threats and issues they addressed, the targeted architecture of the protection or defense system, the solution's evaluation methodology and the evaluation metric, and the limitations that might be viewed as unresolved issues for the future course of action. Finally, we look into future possibilities and open challenges to assist researchers to develop more secure and efficient ROS systems.

Multi robot systems are defined as a collection of two or more robots that are capable of working autonomously while coordinating with each other. Three challenges emerge while designing any multi robot system. The robots have to coordinate their path planning or trajectory planning in order to avoid collision during the course of navigation, while collaborating tasks with other robots to achieve a specific end goal for the system. The other challenge, which is the focus of this paper, is the security of the entire multi robot system. Since robots have to coordinate with each other, any one of them being malicious due to any kind of security threat, can lead to a chain reaction that may compromise the entire system. Such security threats can be fatal if not dealt with immediately. This paper proposes the use of a Hybridized Blockchain Model (HBM) to identify such security threats and take necessary actions in real time so that the system does not encounter any catastrophic failure. The proposed security architecture uses ROS (Robot operating system) to decentralize the information collected by robot clients and HBM to monitor the clients and take necessary real time actions.

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.