Recommender systems (RS) are an efficient tool to reduce information overload when one has an overwhelming choice of resources. Embedding context-awareness into RS is found to increase accuracy and user satisfaction by allowing systems to consider users current situation (context). Context-aware recommender system (CARS) has applications in various areas, including education, where it can help learners by suggesting learning resources, peers to collaborate with, and more. When CARS is used in a learning context, it adds to the issue of lack of trust in the information, source, and intention as one builds knowledge through it. Further, embedding context-awareness adds to the trust issue due to the additional layer of automated context detection and context interpretation without users involvement. I investigate how to build trust in CARS in an educational setting. My investigation will be threefold (a) Understanding users perceptions of CARS; (b) Investigating design interventions to build trust in CARS; (c) Designing and evaluating a multidimensional approach to build trust in CARS.

Connected, Cooperative, and Autonomous Mobility (CCAM) will take intelligent transportation to a new level of complexity. CCAM systems can be thought of as complex Systems-of-Systems (SoSs). They pose new challenges to security as consequences of vulnerabilities or attacks become much harder to assess. In this paper, we propose the use of a specific type of a trust model, called subjective trust network, to model and assess trustworthiness of data and nodes in an automotive SoS. Given the complexity of the topic, we illustrate the application of subjective trust networks on a specific example, namely Cooperative Intersection Management (CIM). To this end, we introduce the CIM use-case and show how it can be modelled as a subjective trust network. We then analyze how such trust models can be useful both for design time and run-time analysis, and how they would allow us a more precise quantitative assessment of trust in automotive SoSs. Finally, we also discuss the open research problems and practical challenges that need to be addressed before such trust models can be applied in practice.

As industrial networks continue to expand and connect more devices and users, they face growing security challenges such as unauthorized access and data breaches. This paper delves into the crucial role of security and trust in industrial networks and how trust management systems (TMS) can mitigate malicious access to these networks.The TMS presented in this paper leverages distributed ledger technology (blockchain) to evaluate the trustworthiness of blockchain nodes, including devices and users, and make access decisions accordingly. While this approach is applicable to blockchain, it can also be extended to other areas. This approach can help prevent malicious actors from penetrating industrial networks and causing harm. The paper also presents the results of a simulation to demonstrate the behavior of the TMS and provide insights into its effectiveness.

The principles of social networking and the Internet of Things were combined to create the Social Internet of Things (SIoT) paradigm. Therefore, this paradigm cannot become widely adopted to the point where it becomes a well-established technology without a security mechanism to assure reliable interactions between SIoT nodes. A Trust Management (TM) model becomes a major challenge in SIoT systems to create a trust score for the network nodes ranking. Regarding the defined TM models methodology, this score will persist for the subsequent transaction and will only be changed after some time has passed or after another transaction. However, a trust evaluation methodology must be able to consider the different constraints of the SIoT environments (dynamism and scalability) when building trust scores. Based on both event-driven and time-driven methods for trust update solutions, this model can identify which damaging nodes should be eliminated based on their changing problematic behaviors over time. The effectiveness of our proposed model has been validated by a number of simulation-based experiments that were conducted on various scenarios.

The prediction of human trust in machines within decision-aid systems is crucial for improving system performance. However, previous studies have only measured machine performance based on its decision history, failing to account for the machine’s current decision state. This delay in evaluating machine performance can result in biased trust predictions, making it challenging to enhance the overall performance of the human-machine system. To address this issue, this paper proposes incorporating machine estimated performance scores into a human-machine trust prediction model to improve trust prediction accuracy and system performance. We also provide an explanation for how this model can enhance system performance.To estimate the accuracy of the machine’s current decision, we employ the KNN(K-Nearest Neighbors) method and obtain a corresponding performance score. Next, we report the estimated score to humans through the human-machine interaction interface and obtain human trust via trust self-reporting. Finally, we fit the trust prediction model parameters using data and evaluate the model’s efficacy through simulation on a public dataset. Our ablation experiments show that the model reduces trust prediction bias by 3.6\% and significantly enhances the overall accuracy of human-machine decision-making.

Learning through web browsing, often termed Search-as-Learning (SaL), can create information overload, due to thousands of search results. SaL can be made more efficient by developing context-aware tools that recommend items to the user and minimize information overload. However, to use context-aware recommender systems (CARS) users need to trust it. Literature has proposed explanations as a feature that helps to build trust. We investigate the impact of explanation on user trust and user experience for using CARS for SaL. Our study results show that people trust a CARS without explanation more during the first use, but for a CARS with explanations, user trust is significant only after multiple uses. Through interviews, we also uncovered the interesting paradox that even though users do not perceive that explanations add to their learning outcomes, they still prefer to use a CARS with explanations over one without.

Educational recommender systems (RS) have become widely popular with the paradigm shift to online learning and the availability of a wide variety of learning resources. Educational RS in various education platforms use a wide variety of filtering techniques. This has led to the development of multiple types of RS. Context-aware recommender systems (CARS) are identified as an emerging type of RS that uses users context for filtering recommendations, which makes recommendations more relevant to the user s current situation. CARS may face initial distrust compared to other RS due to the additional automation layer of context awareness and the use of more user data. Therefore, we conduct a survey-based study to find differences in user trust and perception between CARS and other RS. In the study, users viewed examples of CARS and RS. The results show that users have significantly lower trust in CARS compared to RS.

Trust evaluation and trust establishment play crucial roles in the management of trust within a multi-agent system. When it comes to collaboration systems, trust becomes directly linked to the specific roles performed by agents. The Role-Based Collaboration (RBC) methodology serves as a framework for assigning roles that facilitate agent collaboration. Within this context, the behavior of an agent with respect to a role is referred to as a process role. This research paper introduces a role engine that incorporates a trust establishment algorithm aimed at identifying optimal and reliable process roles. In our study, we define trust as a continuous value ranging from 0 to 1. To optimize trustworthy process roles, we have developed a consensus-based Gaussian Process Factor Graph (GPFG) tool. Our simulations and experiments validate the feasibility and efficiency of our proposed approach with autonomous robots in unsignalized intersections and narrow hallways.

The construction of traditional industrial networks poses challenges in cybersecurity, a sindus-tries are increasingly becoming more interconnected for management purposes. In this study, we analyzed events related to the insertion of the Zero Trust approach in industrial control systems. In a simulated test environment, we investigate how these systems respond to cyberattacks commonly observed in industrial scenarios. The results aim to identify potential benefits that Zero Trust policies can offer to industrial control systems vulnerable to cyber-attacks.

This paper describes a Zero Trust Architecture (ZTA) approach for the survivability development of mission critical embedded systems. Designers could use ZTA as a systems analysis tool to explore the design space. The ZTA concept of “never trust, always verify” is being leveraged in the design process to guide the selection of security and resilience features for the codesign of functionality, performance, and survivability. The design example of a small drone for survivability is described along with the explanation of the ZTA approach.

Cybersecurity is largely based on the use of frameworks (ISO27k, NIST, etc.) which main objective is compliance with the standard. They do not, however, address the quantification of the risk deriving from a threat scenario. This paper proposes a methodology that, having evaluated the overall capability of the controls of an ISO27001 framework, allows to select those that mitigate a threat scenario and evaluate the risk according to a Cybersecurity Risk Quantification model.

Simulation research on fish schooling behavior is of great significance. This paper proposes an improved fish schooling behavior simulation model, which introduces fish collision avoidance, escape, and pursuit rules based on the Boids model, so that the model can simulate the response of fish when facing threats. And the simulation of fish schooling behavior in complex environment was present based on Unity3D. The quantitative analysis of the simulation results shows that the model proposed in this paper can effectively reflect the behavior al characteristics of fish schools. These results are highly consistent with the actual fish schooling behavior, which clearly demonstrates the feasibility of the model in simulating fish schooling behavior.

Cyber-physical system such as automatic metering infrastructure (AMI) are overly complex infrastructures. With myriad stakeholders, real-time constraints, heterogeneous platforms and component dependencies, a plethora of attacks possibilities arise. Despite the best of available technology countermeasures and compliance standards, security practitioners struggle to protect their infrastructures. At the same time, it is important to note that not all attacks are same in terms of their likelihood of occurrence and impact. Hence, it is important to rank the various attacks and perform scenario analysis to have an objective decision on security countermeasures. In this paper, we make a comprehensive security risk analysis of AMI, both qualitatively and quantitatively. Qualitative analysis is performed by ranking the attacks in terms of sensitivity and criticality. Quantitative analysis is done by arranging the attacks as an attack tree and performing Bayesian analysis. Typically, state-of–the-art quantitative security risk analysis suffers from data scarcity. We acknowledge the aforementioned problem and circumvent it by using standard vulnerability database. Different from state-of-the-art surveys on the subject, which captures the big picture, our work is geared to is provide the prioritized baselines in addressing most common and damaging attacks.

Intrusion detection is important in the defense in depth network security framework and a hot topic in computer network security in recent years. In this paper, an effective method for anomaly intrusion detection with low overhead and high efficiency is presented and applied to monitor the abnormal behavior of processes. The method is based on rough set theory and capable of extracting a set of detection rules with the minimum size to form a normal behavior model from the record of system call sequences generated during the normal execution of a process. Based on the network security knowledge base system, this paper proposes an intrusion detection model based on the network security knowledge base system, including data filtering, attack attempt analysis and situation assessment engine. In this model, evolutionary self organizing mapping is used to discover multi - target attacks of the same origin; The association rules obtained by time series analysis method are used to correlate online alarm events to identify complex attacks scattered in time; Finally, the corresponding evaluation indexes and corresponding quantitative evaluation methods are given for host level and LAN system level threats respectively. Compared with the existing IDS, this model has a more complete structure, richer knowledge available, and can more easily find cooperative attacks and effectively reduce the false positive rate.

In recent times, the research looks into the measures taken by financial institutions to secure their systems and reduce the likelihood of attacks. The study results indicate that all cultures are undergoing a digital transformation at the present time. The dawn of the Internet ushered in an era of increased sophistication in many fields. There has been a gradual but steady shift in attitude toward digital and networked computers in the business world over the past few years. Financial organizations are increasingly vulnerable to external cyberattacks due to the ease of usage and positive effects. They are also susceptible to attacks from within their own organisation. In this paper, we develop a machine learning based quantitative risk assessment model that effectively assess and minimises this risk. Quantitative risk calculation is used since it is the best way for calculating network risk. According to the study, a network s vulnerability is proportional to the number of times its threats have been exploited and the amount of damage they have caused. The simulation is used to test the model s efficacy, and the results show that the model detects threats more effectively than the other methods.

The growth of Electric Vehicles (EVs), coupled with the deployment of large-scale extreme fast charging stations (XFCSs), has increased the attack surface for EV ecosystems. To secure such critical cyber-physical systems (CPSs), it is imperative for the system defenders to perform an in-depth analysis of potential attack vectors, evaluate possible countermeasures, and analyze attack-defense scenarios quantitatively to implement a defense strategy that will provide maximum utilization of their limited resources. Therefore, a systematic framework is essential, relying on modeling tools that security experts are familiar with. In this paper, we propose a comprehensive methodology for enabling the defender to perform a high-level attack surface analysis of an XFCS and determine the defense strategy with the highest utility value. We apply STRIDE threat modeling and attack defense tree (ADT) to enumerate realizable attack paths and identify possible defense measures. We then employ analytic hierarchy process (AHP) as a multi-criteria decisionmaking algorithm to obtain the highest utility strategy for the defender to adopt. The proposed methodology is validated by demonstrating its real-world feasibility through a case study, using sample attack paths for an XFCS.

Recently, Graphical Security Models (GrSMs) became widely used for security analysis. The basic formalism called Attack Tree (AT) has been augmented with new attributes covering defence, response, and countermeasure aspects to support security modelling and analysis in vulnerable systems. Although the models have strength in visualising and analysing small attack-defence scenarios, these suffer from lack of scalability when increasing nodes and adaptability with other refinement models to show the dynamic nature and state of systems in interest. In this work, Coloured Petri net (CPN) is used to fulfil the mentioned shortcomings in GrSMs (specifically Treebased models). It is applied for evaluating each component´s interactions, the impact of threats as well as defence systems to mitigate those threats. For that end and pointing out the CPN adaptability with GrSMs, a set of mapping rules are proposed allowing translation of ATs extension into CPN and their analysis. The quantitative analysis aspect is addressed in this work by introducing computing transition. We validate our proposed approach by applying it in an example of SCADA systems cybersecurity analysis.

Cybersecurity risk analysis is crucial for orga-nizations to assess, identify, and prioritize possible threats to their systems and assets. Organizations aim to estimate the loss cost in case cybersecurity risks occur to decide the control actions they should invest in. Quantitative risk analysis aids organizations in making well-informed decisions about risk mitigation strategies and resource allocation. Therefore, organizations must use quantitative risk analysis methods to identify and prioritize risks rather than relying on qualitative methods. This paper proposes a spreadsheet-based quantitative risk analysis method based on verbal likelihoods. Our approach relies on tables constructed by experts that map between linguistic likelihood and possible probability ranges. Using linguistic terms to estimate the probability of risk occurrence will help experts apply quantitative estimation easily by using common language as input, thus eliminating the need to assign precise probabilities. We experimented with real examples to validate our approach s accuracy and reliability and compared our results with those obtained from another method. Also, we conducted tests to measure our model s performance and robustness. Our study showcases the effectiveness of our approach and demonstrates its potential for risk analysts to use it in real-world applications.

Security still remains an afterthought in modern Electronic Design Automation (EDA) tools, which solely focus on enhancing performance and reducing the chip size. Typically, the security analysis is conducted by hand, leading to vulnerabilities in the design remaining unnoticed. Security-aware EDA tools assist the designer in the identification and removal of security threats while keeping performance and area in mind. Stateof-the-art approaches utilize information flow analysis to spot unintended information leakages in design structures. However, the classification of such threats is binary, resulting in negligible leakages being listed as well. A novel quantitative analysis allows the application of a metric to determine a numeric value for a leakage. Nonetheless, current approximations to quantify the leakage are still prone to overlooking leakages. The mathematical model 2D-QModel introduced in this work aims to overcome this shortcoming. Additionally, as previous work only includes a limited threat model, multiple threat models can be applied using the provided approach. Open-source benchmarks are used to show the capabilities of 2D-QModel to identify hardware Trojans in the design while ignoring insignificant leakages.

In this paper, an air Air target threat assessment method based on a variable weight cloud Bayesian network (VWCBN) is proposed, which addresses the qualitative issue of air target threat levels, as most of the existing threat assessment results in focus on quantitative analysis. The proposed method enables high, medium, and low qualitative decision-making for air target threat levels. Firstly, a Bayesian network model that incorporates the attribute of air threat is constructed, assessing the threat level of air targets. Secondly, the cloud model is introduced to the Bayesian network, using it to represent the probability of correlation between nodes in the network. Then, by combining the battlefield situation information, using an improved variable weight method with Gaussian expression, the weights of target attributes are determined. Finally, based on the correlation probability and target attribute weight, the cloud model operation rules are utilized to obtain the decision of the air target threat level. Simulation results demonstrate that the proposed VWCBN method can effectively assess target threats, obtain air target threat level decisions, and further improve the utilization of battlefield information.

This paper focuses on the challenges and issues of detecting malware in to-day s world where cyberattacks continue to grow in number and complexity. The paper reviews current trends and technologies in malware detection and the limitations of existing detection methods such as signaturebased detection and heuristic analysis. The emergence of new types of malware, such as file-less malware, is also discussed, along with the need for real-time detection and response. The research methodology used in this paper is presented, which includes a literature review of recent papers on the topic, keyword searches, and analysis and representation methods used in each study. In this paper, the authors aim to address the key issues and challenges in detecting malware today, the current trends and technologies in malware detection, and the limitations of existing methods. They also explore emerging threats and trends in malware attacks and highlight future directions for research and development in the field. To achieve this, the authors use a research methodology that involves a literature review of recent papers related to the topic. They focus on detecting and analyzing methods, as well as representation and ex-traction methods used in each study. Finally, they classify the literature re-view, and through reading and criticism, highlight future trends and problems in the field of malware detection.

With the continuous improvement of the current level of information technology, the malicious software produced by attackers is also becoming more complex. It s difficult for computer users to protect themselves against malicious software attacks. Malicious software can steal the user s privacy, damage the user s computer system, and often cause serious consequences and huge economic losses to the user or the organization. Hence, this research study presents a novel deep learning-based malware detection scheme considering packers and encryption. The proposed model has 2 aspects of innovations: (1) Generation steps of the packer malware is analyzed. Packing involves adding code to the program to be protected, and original program is compressed and encrypted during the packing process. By understanding this step, the analysis of the software will be efficient. (2) The deep learning based detection model is designed. Through the experiment compared with the latest methods, the performance is proven to be efficient.

Malware detection constitutes a fundamental step in safe and secure computational systems, including industrial systems and the Internet of Things (IoT). Modern malware detection is based on machine learning methods that classify software samples as malware or benign, based on features that are extracted from the samples through static and/or dynamic analysis. State-of-the-art malware detection systems employ Deep Neural Networks (DNNs) whose accuracy increases as more data are analyzed and exploited. However, organizations also have significant privacy constraints and concerns which limit the data that they share with centralized security providers or other organizations, despite the malware detection accuracy improvements that can be achieved with the aggregated data. In this paper we investigate the effectiveness of federated learning (FL) methods for developing and distributing aggregated DNNs among autonomous interconnected organizations. We analyze a solution where multiple organizations use independent malware analysis platforms as part of their Security Operations Centers (SOCs) and train their own local DNN model on their own private data. Exploiting cross-silo FL, we combine these DNNs into a global one which is then distributed to all organizations, achieving the distribution of combined malware detection models using data from multiple sources without sample or feature sharing. We evaluate the approach using the EMBER benchmark dataset and demonstrate that our approach effectively reaches the same accuracy as the non-federated centralized DNN model, which is above 93\%.

IBMD(Intelligent Behavior-Based Malware Detection) aims to detect and mitigate malicious activities in cloud computing environments by analyzing the behavior of cloud resources, such as virtual machines, containers, and applications.The system uses different machine learning methods like deep learning and artificial neural networks, to analyze the behavior of cloud resources and detect anomalies that may indicate malicious activity. The IBMD system can also monitor and accumulate the data from various resources, such as network traffic and system logs, to provide a comprehensive view of the behavior of cloud resources. IBMD is designed to operate in a cloud computing environment, taking advantage of the scalability and flexibility of the cloud to detect malware and respond to security incidents. The system can also be integrated with existing security tools and services, such as firewalls and intrusion detection systems, to provide a comprehensive security solution for cloud computing environments.

In today s digital landscape, the task of identifying various types of malicious files has become progressively challenging. Modern malware exhibits increasing sophistication, often evading conventional anti-malware solutions. The scarcity of data on distinct and novel malware strains further complicates effective detection. In response, this research presents an innovative approach to malware detection, specifically targeting multiple distinct categories of malicious software. In the initial stage, Principal Component Analysis (PCA) is performed and achieved a remarkable accuracy rate of 95.39\%. Our methodology revolves around leveraging features commonly accessible from user-uploaded files, aligning with the contextual behavior of typical users seeking to identify malignancy. This underscores the efficacy of the unique featurebased detection strategy and its potential to enhance contemporary malware identification methodologies. The outcomes achieved attest to the significance of addressing emerging malware threats through inventive analytical paradigms.