Biblio

Contact tracing is a particularly important part of health care and is often overlooked or forgotten up until right when it is needed the most. With the wave of technological achievements in the last decade, a digital perspective for aid in contact tracing was a natural development from traditional contact tracing. When COVID-19 was categorized as a pandemic, the need for modernized contact tracing solutions became apparent, and highly sought after. Solutions using the Bluetooth protocol and/or Global Positioning System data (GPS) were hastily made available to the public in nations all over the world. These solutions quickly became criticized by privacy experts as being potential tools for tracking.

Network security has always been the most important of enterprise informatization construction and development, and the security assessment of network system is the basis for enterprises to make effective security defense strategies. Aiming at the relevance of security factors and subjectivity of evaluation results in the process of enterprise network system security assessment, a security assessment method combining Analytic Network Process and evidence theory is proposed. Firstly, we built a complete security assessment index system and network analysis structure model for enterprise network, and determined the converged security index weights by calculating hypermatrix, limit hypermatrix and stable limit hypermatrix; then, we used the evidence theory on data fusion of the evaluation opinions of multiple experts to eliminate the conflict between evidences. Finally, according to the principle of maximum membership degree, we realized the assessment of enterprise network security level using weighted average. The example analysis showed that the model not only weighed the correlation influence among the security indicators, but also effectively reduced the subjectivity of expert evaluation and the fuzziness and uncertainty in qualitative analysis, which verified the effectiveness of the model and method, and provided an important basis for network security management.

The contemporary struggle that rests upon security risk assessment of Information Systems is its feasibility in the presence of an indeterminate environment when information is insufficient, conflicting, generic or ambiguous. But as pointed out by the security experts, most of the traditional approaches to risk assessment of information systems security are no longer practicable as they fail to deliver viable support on handling uncertainty. Therefore, to address this issue, we have anticipated a comprehensive risk assessment model based on Bayesian Belief Network (BBN) and Fuzzy Inference Scheme (FIS) process to function in an indeterminate environment. The proposed model is demonstrated and further comparisons are made on the test results to validate the reliability of the proposed model.

Edge computing brings processing and storage capabilities closer to the data sources, to reduce network latency, save bandwidth, and preserve data locality. Despite the clear benefits, this paradigm brings unprecedented cyber risks due to the combination of the security issues and challenges typical of cloud and Internet of Things (IoT) worlds. Notwithstanding an increasing interest in edge security by academic and industrial communities, there is still no discernible industry consensus on edge computing security best practices, and activities like threat analysis and countermeasure selection are still not well established and are completely left to security experts.In order to cope with the need for a simplified yet effective threat modeling process, which is affordable in presence of limited security skills and economic resources, and viable in modern development approaches, in this paper, we propose an automated threat modeling and countermeasure selection strategy targeting edge computing systems. Our approach leverages a comprehensive system model able to describe the main involved architectural elements and the associated data flow, with a focus on the specific properties that may actually impact on the applicability of threats and of associated countermeasures.

This article analyzes the possibilities of using cognitive approaches in forming expert assessments for solving information security problems. The experts use the contextual approach by A.Yu. Khrennikov’s as a basic model for the mathematical description of the quantum decision-making method. In the cognitive view, expert assessments are proposed to be considered as conditional probabilities with regard to the fulfillment of a set of certain conditions. However, the conditions in this approach are contextual, but not events like in Boolean algebra.

The paper considers the issue of assessing threats to information security in industrial automation and telecommunication systems in order to improve the efficiency of their security systems. A method for determining a quantitative indicator of threats is proposed, taking into account the probabilistic nature of the process of implementing negative impacts on objects of both industrial and telecommunications systems. The factors that contribute and (or) initiate them are also determined, the dependences of the formal definition of the quantitative indicator of threats are obtained. Methods for a quantitative threat assessment as well as the degree of this threat are presented in the form of a mathematical model in order to substantiate and describe the method for determining a threat to industrial automation systems. Recommendations necessary for obtaining expert assessments of negative impacts on the informatisation objects and information security systems counteracting are formulated to facilitate making decisions on the protection of industrial and telecommunication systems.

Cyber security is a topic of increasing relevance in relation to industrial networks. The higher intensity and intelligent use of data pushed by smart technology (Industry 4.0) together with an augmented integration between the operational technology (production) and the information technology (business) parts of the network have considerably raised the level of vulnerabilities. On the other hand, many industrial facilities still use serial networks as underlying communication system, and they are notoriously limited from a cyber security perspective since protection mechanisms available for ТСР/IР communication do not apply. Therefore, an attacker gaining access to a serial network can easily control the industrial components, potentially causing catastrophic incidents, jeopardizing assets and human lives. This study proposes a framework to act as an anomaly detection system (ADS) for industrial serial networks. It has three ingredients: an unsupervised К-means component to analyse message content, a knowledge-based Expert System component to analyse message metadata, and a voting process to generate alerts for security incidents, anomalous states, and faults. The framework was evaluated using the Proflbus-DP, a network simulator which implements a serial bus system. Results for the simulated traffic were promising: 99.90% for accuracy, 99,64% for precision, and 99.28% for F1-Score. They indicate feasibility of the framework applied to serial-based industrial networks.

This paper addresses security and risk management of hardware and embedded systems across several applications. There are three companies involved in the research. First is an energy technology company that aims to leverage electric- vehicle batteries through vehicle to grid (V2G) services in order to provide energy storage for electric grids. Second is a defense contracting company that provides acquisition support for the DOD's conventional prompt global strike program (CPGS). These systems need protections in their production and supply chains, as well as throughout their system life cycles. Third is a company that deals with trust and security in advanced logistics systems generally. The rise of interconnected devices has led to growth in systems security issues such as privacy, authentication, and secure storage of data. A risk analysis via scenario-based preferences is aided by a literature review and industry experts. The analysis is divided into various sections of Criteria, Initiatives, C-I Assessment, Emergent Conditions (EC), Criteria-Scenario (C-S) relevance and EC Grouping. System success criteria, research initiatives, and risks to the system are compiled. In the C-I Assessment, a rating is assigned to signify the degree to which criteria are addressed by initiatives, including research and development, government programs, industry resources, security countermeasures, education and training, etc. To understand risks of emergent conditions, a list of Potential Scenarios is developed across innovations, environments, missions, populations and workforce behaviors, obsolescence, adversaries, etc. The C-S Relevance rates how the scenarios affect the relevance of the success criteria, including cost, schedule, security, return on investment, and cascading effects. The Emergent Condition Grouping (ECG) collates the emergent conditions with the scenarios. The generated results focus on ranking Initiatives based on their ability to negate the effects of Emergent Conditions, as well as producing a disruption score to compare a Potential Scenario's impacts to the ranking of Initiatives. The results presented in this paper are applicable to the testing and evaluation of security and risk for a variety of embedded smart devices and should be of interest to developers, owners, and operators of critical infrastructure systems.

Safety- and security-critical developers have long recognized the importance of applying a high degree of scrutiny to a system’s (or subsystem’s) I/O messages. However, lack of care in the development of message-handling components can lead to an increase, rather than a decrease, in the attack surface. On the DARPA Cyber-Assured Systems Engineering (CASE) program, we have focused our research effort on identifying cyber vulnerabilities early in system development, in particular at the Architecture development phase, and then automatically synthesizing components that mitigate against the identified vulnerabilities from high-level specifications. This approach is highly compatible with the goals of the LangSec community. Advances in formal methods have allowed us to produce hardware/software implementations that are both performant and guaranteed correct. With these tools, we can synthesize high-assurance “building blocks” that can be composed automatically with high confidence to create trustworthy systems, using a method we call Security-Enhancing Architectural Transformations. Our synthesis-focused approach provides a higherleverage insertion point for formal methods than is possible with post facto analytic methods, as the formal methods tools directly contribute to the implementation of the system, without requiring developers to become formal methods experts. Our techniques encompass Systems, Hardware, and Software Development, as well as Hardware/Software Co-Design/CoAssurance. We illustrate our method and tools with an example that implements security-improving transformations on system architectures expressed using the Architecture Analysis and Design Language (AADL). We show how message-handling components can be synthesized from high-level regular or context-free language specifications, as well as a novel specification language for self-describing messages called Contiguity Types, and verified to meet arithmetic constraints extracted from the AADL model. Finally, we guarantee that the intent of the message processing logic is accurately reflected in the application binary code through the use of the verified CakeML compiler, in the case of software, or the Restricted Algorithmic C toolchain with ACL2-based formal verification, in the case of hardware/software co-design.

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.

Security patterns are proven solutions to recurring problems in software development. The growing importance of secure software development has introduced diverse research efforts on security patterns that mostly focused on classification schemes, evolution and evaluation of the patterns. Despite a huge mature history of research and popularity among researchers, security patterns have not fully penetrated software development practices. Besides, software security education has not been benefited by these patterns though a commonly stated motivation is the dissemination of expert knowledge and experience. This is because the patterns lack a simple embodiment to help students learn about vulnerable code, and to guide new developers on secure coding. In order to address this problem, we propose to conduct intelligent data mining in the context of software engineering to discover learner-friendly software security patterns. Our proposed model entails knowledge discovery from large scale published real-world vulnerability histories in software applications. We harness association rule mining for frequent pattern discovery to mine easily comprehensible and explainable learner-friendly rules, mainly of the type "flaw implies fix" and "attack type implies flaw", so as to enhance training in secure coding which in turn would augment secure software development. We propose to build a learner-friendly intelligent tutoring system (ITS) based on the newly discovered security patterns and rules explored. We present our proposed model based on association rule mining in secure software development with the goal of building this ITS. Our proposed model and prototype experiments are discussed in this paper along with challenges and ongoing work.

Performing a live digital forensics investigation on a running system is challenging due to the time pressure under which decisions have to be made. Newly proliferating and frequently applied types of malware (e.g., fileless malware) increase the need to conduct digital forensic investigations in real-time. In the course of these investigations, forensic experts are confronted with a wide range of different forensic tools. The decision, which of those are suitable for the current situation, is often based on the cyber forensics experts’ experience. Currently, there is no reliable automated solution to support this decision-making. Therefore, we derive requirements for visually supporting the decision-making process for live forensic investigations and introduce a research prototype that provides visual guidance for cyber forensic experts during a live digital forensics investigation. Our prototype collects relevant core information for live digital forensics and provides visual representations for connections between occurring events, developments over time, and detailed information on specific events. To show the applicability of our approach, we analyze an exemplary use case using the prototype and demonstrate the support through our approach.

The rapid expansion and diversity of technology throughout society have impacted the growing knowledge gap in conducting analysis on IoT devices. The IoT digital forensic field lacks the necessary tools and guidance to perform digital forensics on these devices. This is mainly attributed to their level of complexity and heterogeneity that is abundant within IoT devices-making the use of a JTAG technique one of the only ways to acquire information stored on an IoT device effectively. Nonetheless, utilizing a JTAG technique can be challenging, especially when having multiple devices with each possibly having its own configuration. To alleviate these issues within the field, we propose the development of an Internet of Things - Forensics Recovery Assistant (IoT-FRA). The IoT-FRA will offer the capabilities of an expert system to assist inexperienced users in performing forensics recovery of IoT devices through a JTAG technique and analysis on the device's capabilities to develop an organized method that will prioritize IoT devices to be analyzed.

Analysis of the state of development of research on the protection of valuable scientific and educational databases, library resources, information centers, publishers show the importance of information security, especially in corporate information networks and systems for data exchange. Corporate library networks include dozens and even hundreds of libraries for active information exchange, and they (libraries) are equipped with information security tools to varying degrees. The purpose of the research is to create effective methods and tools to protect the databases of the scientific and educational resources from unauthorized access in libraries and library networks using fuzzy logic methods.

The article deals with the development and implementation of a method for synthesizing structures of threats and risks to information security based on a fuzzy approach. We consider a method for modeling threat structures based on structural abstractions: aggregation, generalization, and Association. It is shown that the considered forms of structural abstractions allow implementing the processes of Ascending and Descending inheritance. characteristics of the threats. A database of fuzzy rules based on procedural abstractions has been developed and implemented in the fuzzy logic tool environment Fussy Logic.

Technology advancement also increases the risk of a computer's security. As we can have various mechanisms to ensure safety but still there have flaws. The main concerned area is user authentication. For authentication, various biometric applications are used but once authentication is done in the begging there was no guarantee that the computer system is used by the authentic user or not. The intrusion detection system (IDS) is a particular procedure that is used to identify intruders by analyzing user behavior in the system after the user logged in. Host-based IDS monitors user behavior in the computer and identify user suspicious behavior as an intrusion or normal behavior. This paper discusses how an expert system detects intrusions using a set of rules as a pattern recognized engine. We propose a PIDE (Pattern Based Intrusion Detection) model, which is verified previously implemented SBID (Statistical Based Intrusion Detection) model. Experiment results indicate that integration of SBID and PBID approach provides an extensive system to detect intrusion.

Reliable and secure grid operations become more and more challenging in context of increasing IT/OT convergence and decreasing dynamic margins in today's power systems. To ensure the correct operation of monitoring and control functions in control centres, an intelligent assessment of the different information sources is necessary to provide a robust data source in case of critical physical events as well as cyber-attacks. Within this paper, a holistic data stream assessment methodology is proposed using an expert knowledge based cyber-physical situational awareness for different steady and transient system states. This approach goes beyond existing techniques by combining high-resolution PMU data with SCADA information as well as Digital Twin and AI based anomaly detection functionalities.

The objective of this paper is to propose a model of a distributed intrusion detection system based on the multi-agent paradigm and the distributed file system (HDFS). Multi-agent systems (MAS) are very suitable to intrusion detection systems as they can address the issue of geographic data security in terms of autonomy, distribution and performance. The proposed system is based on a set of autonomous agents that cooperate and collaborate with each other to effectively detect intrusions and suspicious activities that may impact geographic information systems. Our system allows the detection of known and unknown computer attacks without any human intervention (Security Experts) unlike traditional intrusion detection systems that rely on knowledge bases as a mechanism to detect known attacks. The proposed model allows a real time detection of known and unknown attacks within large networks hosting geographic data.

Due to geographical locations of Indonesia, some technology such as hydro and solar photovoltaics are very attractive to be used and developed. Distribution Energy Resources (DER) is the appropriate schemes implemented to achieve optimal operation respecting the location and capacity of the plant. The Gorontalo sub-system network was chosen as a case study considering both of micro-hydro and PV as contributed to supply the grid. The needs of a smart electrical system are required to improve reliability, power quality, and adaptation to any circumstances during DER application. While the topology was changing over time, intermittent of DER output and bidirectional power flow can be overcome with smart grid systems. In this study, an automation algorithm has been conducted to aid the engineers in solving the protection problems caused by DER implementation. The Protection Security Assessment (PSA) method is used to evaluate the state of the protection system. Determine the relay settings using an adaptive rule-based method on expert systems. The application with a Graphical User Interface (GUI) has been developed to make user easier to get the specific relay settings and locations which are sensitive, fast, reliable, and selective.

Safety and security of complex critical infrastructures is very important for economic, environmental and social reasons. The interdisciplinary and inter-system dependencies within these infrastructures introduce difficulties in the safety and security design. Late discovery of safety and security design weaknesses can lead to increased costs, additional system complexity, ineffective mitigation measures and delays to the deployment of the systems. Traditionally, safety and security assessments are handled using different methods and tools, although some concepts are very similar, by specialized experts in different disciplines and are performed at different system design life-cycle phases.The methodology proposed in this paper supports a concurrent safety and security Defense in Depth (DiD) assessment at an early design phase and it is designed to handle safety and security at a high level and not focus on specific practical technologies. It is assumed that regardless of the perceived level of security defenses in place, a determined (motivated, capable and/or well-funded) attacker can find a way to penetrate a layer of defense. While traditional security research focuses on removing vulnerabilities and increasing the difficulty to exploit weaknesses, our higher-level approach focuses on how the attacker's reach can be limited and to increase the system's capability for detection, identification, mitigation and tracking. The proposed method can assess basic safety and security DiD design principles like Redundancy, Physical separation, Functional isolation, Facility functions, Diversity, Defense lines/Facility and Computer Security zones, Safety classes/Security Levels, Safety divisions and physical gates/conduits (as defined by the International Atomic Energy Agency (IAEA) and international standards) concurrently and provide early feedback to the system engineer. A prototype tool is developed that can parse the exported project file of the interdisciplinary model. Based on a set of safety and security attributes, the tool is able to assess aspects of the safety and security DiD capabilities of the design. Its results can be used to identify errors, improve the design and cut costs before a formal human expert inspection. The tool is demonstrated on a case study of an early conceptual design of a complex system of a nuclear power plant.

This work describes a top down systems security requirements analysis approach for understanding and eliciting security requirements for a notional small unmanned aerial system (SUAS). More specifically, the System-Theoretic Process Analysis approach for Security (STPA-Sec) is used to understand and elicit systems security requirements. The effort employs STPA-Sec on a notional SUAS system case study to detail the development of functional-level security requirements, design-level engineering considerations, and architectural-level security specification criteria early in the system life cycle when the solution trade-space is largest rather than merely examining components and adding protections during system operation or sustainment. These details were elaborated during a semester independent study research effort by two United States Air Force Academy Systems Engineering cadets, guided by their instructor and a series of working group sessions with UAS operators and subject matter experts. This work provides insight into a viable systems security requirements analysis approach which results in traceable security, safety, and resiliency requirements that can be designed-for, built-to, and verified with confidence.

The paper considers an expert system that provides an assessment of the state of information security in authorities and organizations of various forms of ownership. The proposed expert system allows to evaluate the state of compliance with the requirements of both organizational and technical measures to ensure the protection of information, as well as the level of compliance with the requirements of the information protection system in general. The expert assessment method is used as a basic method for assessing the state of information protection. The developed expert system provides a significant reduction in routine operations during the audit of information security. The results of the assessment are presented quite clearly and provide an opportunity for the leadership of the authorities and organizations to make informed decisions to further improve the information protection system.

Humans are a key part of software development, including customers, designers, coders, testers and end users. In this keynote talk I explain why incorporating human-centric issues into software engineering for next-generation applications is critical. I use several examples from our recent and current work on handling human-centric issues when engineering various `smart living' cloud- and edge-based software systems. This includes using human-centric, domain-specific visual models for non-technical experts to specify and generate data analysis applications; personality impact on aspects of software activities; incorporating end user emotions into software requirements engineering for smart homes; incorporating human usage patterns into emerging edge computing applications; visualising smart city-related data; reporting diverse software usability defects; and human-centric security and privacy requirements for smart living systems. I assess the usefulness of these approaches, highlight some outstanding research challenges, and briefly discuss our current work on new human-centric approaches to software engineering for smart living applications.

Internet of Things (IoT) systems are becoming widely used, which makes them to be a high-value target for both hackers and crackers. From gaining access to sensitive information to using them as bots for complex attacks, the variety of advantages after exploiting different security vulnerabilities makes the security of IoT devices to be one of the most challenging desideratum for cyber security experts. In this paper, we will propose a new IoT system, designed to ensure five data principles: confidentiality, integrity, availability, authentication and authorization. The innovative aspects are both the usage of a web-based communication and a custom dynamic data request structure.

Human behaviors are often prohibited, or permitted by social norms. Therefore, if autonomous agents interact with humans, they also need to reason about various legal rules, social and ethical social norms, so they would be trusted and accepted by humans. Inverse Reinforcement Learning (IRL) can be used for the autonomous agents to learn social norm-compliant behavior via expert demonstrations. However, norms are context-sensitive, i.e. different norms get activated in different contexts. For example, the privacy norm is activated for a domestic robot entering a bathroom where a person may be present, whereas it is not activated for the robot entering the kitchen. Representing various contexts in the state space of the robot, as well as getting expert demonstrations under all possible tasks and contexts is extremely challenging. Inspired by recent work on Modularized Normative MDP (MNMDP) and early work on context-sensitive RL, we propose a new IRL framework, Context-Sensitive Norm IRL (CNIRL). CNIRL treats states and contexts separately, and assumes that the expert determines the priority of every possible norm in the environment, where each norm is associated with a distinct reward function. The agent chooses the action to maximize its cumulative rewards. We present the CNIRL model and show that its computational complexity is scalable in the number of norms. We also show via two experimental scenarios that CNIRL can handle problems with changing context spaces.