Visible to the public Biblio

Found 172 results

Filters: Keyword is Games  [Clear All Filters]
Zhou, Zan, Xu, Changqiao, Ma, Tengchao, Kuang, Xiaohui.  2020.  Multi-vNIC Intelligent Mutation: A Moving Target Defense to thwart Client-side DNS Cache Attack. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—6.
As massive research efforts are poured into server-side DNS security enhancement in online cloud service platforms, sophisticated APTs tend to develop client-side DNS attacks, where defenders only have limited resources and abilities. The collaborative DNS attack is a representative newest client-side paradigm to stealthily undermine user cache by falsifying DNS responses. Different from existing static methods, in this paper, we propose a moving target defense solution named multi-vNIC intelligent mutation to free defenders from arduous work and thwart elusive client-side DNS attack in the meantime. Multiple virtual network interface cards are created and switched in a mutating manner. Thus attackers have to blindly guess the actual NIC with a high risk of exposure. Firstly, we construct a dynamic game-theoretic model to capture the main characteristics of both attacker and defender. Secondly, a reinforcement learning mechanism is developed to generate adaptive optimal defense strategy. Experiment results also highlight the security performance of our defense method compared to several state-of-the-art technologies.
Grimsman, David, Hespanha, João P., Marden, Jason R..  2020.  Stackelberg Equilibria for Two-Player Network Routing Games on Parallel Networks. 2020 American Control Conference (ACC). :5364—5369.
We consider a two-player zero-sum network routing game in which a router wants to maximize the amount of legitimate traffic that flows from a given source node to a destination node and an attacker wants to block as much legitimate traffic as possible by flooding the network with malicious traffic. We address scenarios with asymmetric information, in which the router must reveal its policy before the attacker decides how to distribute the malicious traffic among the network links, which is naturally modeled by the notion of Stackelberg equilibria. The paper focuses on parallel networks, and includes three main contributions: we show that computing the optimal attack policy against a given routing policy is an NP-hard problem; we establish conditions under which the Stackelberg equilibria lead to no regret; and we provide a metric that can be used to quantify how uncertainty about the attacker's capabilities limits the router's performance.
Chiariotti, Federico, Signori, Alberto, Campagnaro, Filippo, Zorzi, Michele.  2020.  Underwater Jamming Attacks as Incomplete Information Games. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :1033—1038.
Autonomous Underwater Vehicles (AUVs) have several fundamental civilian and military applications, and Denial of Service (DoS) attacks against their communications are a serious threat. In this work, we analyze such an attack using game theory in an asymmetric scenario, in which the node under attack does not know the position of the jammer that blocks its signals. The jammer has a dual objective, namely, disrupting communications and forcing the legitimate transmitter to spend more energy protecting its own transmissions. Our model shows that, if both nodes act rationally, the transmitter is able to quickly reduce its disadvantage, estimating the location of the jammer and responding optimally to the attack.
Suciu, George, Hussain, Ijaz, Petrescu, Gabriel.  2020.  Role of Ubiquitous Computing and Mobile WSN Technologies and Implementation. 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). :1–6.
Computing capabilities such as real time data, unlimited connection, data from sensors, environmental analysis, automated decisions (machine learning) are demanded by many areas like industry for example decision making, machine learning, by research and military, for example GPS, sensor data collection. The possibility to make these features compatible with each domain that demands them is known as ubiquitous computing. Ubiquitous computing includes network topologies such as wireless sensor networks (WSN) which can help further improving the existing communication, for example the Internet. Also, ubiquitous computing is included in the Internet of Things (IoT) applications. In this article, it is discussed the mobility of WSN and its advantages and innovations, which make possible implementations for smart home and office. Knowing the growing number of mobile users, we place the mobile phone as the key factor of the future ubiquitous wireless networks. With secure computing, communicating, and storage capacities of mobile devices, they can be taken advantage of in terms of architecture in the sense of scalability, energy efficiency, packet delay, etc. Our work targets to present a structure from a ubiquitous computing point of view for researchers who have an interest in ubiquitous computing and want to research on the analysis, to implement a novel method structure for the ubiquitous computing system in military sectors. Also, this paper presents security and privacy issues in ubiquitous sensor networks (USN).
Gamagedara Arachchilage, Nalin Asanka, Hameed, Mumtaz Abdul.  2020.  Designing a Serious Game: Teaching Developers to Embed Privacy into Software Systems. 2020 35th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW). :7—12.
Software applications continue to challenge user privacy when users interact with them. Privacy practices (e.g. Data Minimisation (DM), Privacy by Design (PbD) or General Data Protection Regulation (GDPR)) and related “privacy engineering” methodologies exist and provide clear instructions for developers to implement privacy into software systems they develop that preserve user privacy. However, those practices and methodologies are not yet a common practice in the software development community. There has been no previous research focused on developing “educational” interventions such as serious games to enhance software developers' coding behaviour. Therefore, this research proposes a game design framework as an educational tool for software developers to improve (secure) coding behaviour, so they can develop privacy-preserving software applications that people can use. The elements of the proposed framework were incorporated into a gaming application scenario that enhances the software developers' coding behaviour through their motivation. The proposed work not only enables the development of privacy-preserving software systems but also helping the software development community to put privacy guidelines and engineering methodologies into practice.
Scarabaggio, Paolo, Carli, Raffaele, Dotoli, Mariagrazia.  2020.  A game-theoretic control approach for the optimal energy storage under power flow constraints in distribution networks. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). :1281—1286.
Traditionally, the management of power distribution networks relies on the centralized implementation of the optimal power flow and, in particular, the minimization of the generation cost and transmission losses. Nevertheless, the increasing penetration of both renewable energy sources and independent players such as ancillary service providers in modern networks have made this centralized framework inadequate. Against this background, we propose a noncooperative game-theoretic framework for optimally controlling energy storage systems (ESSs) in power distribution networks. Specifically, in this paper we address a power grid model that comprehends traditional loads, distributed generation sources and several independent energy storage providers, each owning an individual ESS. Through a rolling-horizon approach, the latter participate in the grid optimization process, aiming both at increasing the penetration of distributed generation and leveling the power injection from the transmission grid. Our framework incorporates not only economic factors but also grid stability aspects, including the power flow constraints. The paper fully describes the distribution grid model as well as the underlying market hypotheses and policies needed to force the energy storage providers to find a feasible equilibrium for the network. Numerical experiments based on the IEEE 33-bus system confirm the effectiveness and resiliency of the proposed framework.
Plager, Trenton, Zhu, Ying, Blackmon, Douglas A..  2020.  Creating a VR Experience of Solitary Confinement. 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). :692—693.
The goal of this project is to create a realistic VR experience of solitary confinement and study its impact on users. Although there have been active debates and studies on this subject, very few people have personal experience of solitary confinement. Our first aim is to create such an experience in VR to raise the awareness of solitary confinement. We also want to conduct user studies to compare the VR solitary confinement experience with other types of media experiences, such as films or personal narrations. Finally, we want to study people’s sense of time in such a VR environment.
Xing, Hang, Zhou, Chunjie, Ye, Xinhao, Zhu, Meipan.  2020.  An Edge-Cloud Synergy Integrated Security Decision-Making Method for Industrial Cyber-Physical Systems. 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS). :989–995.
With the introduction of new technologies such as cloud computing and big data, the security issues of industrial cyber-physical systems (ICPSs) have become more complicated. Meanwhile, a lot of current security research lacks adaptation to industrial system upgrades. In this paper, an edge-cloud synergy framework for security decision-making is proposed, which takes advantage of the huge convenience and advantages brought by cloud computing and edge computing, and can make security decisions on a global perspective. Under this framework, a combination of Bayesian network-based risk assessment and stochastic game model-based security decision-making is proposed to generate an optimal defense strategy to minimize system losses. This method trains models in the clouds and infers at the edge computing nodes to achieve rapid defense strategy generation. Finally, a case study on the hardware-in-the-loop simulation platform proves the feasibility of the approach.
Yang, Wenti, Wang, Ruimiao, Guan, Zhitao, Wu, Longfei, Du, Xiaojiang, Guizani, Mohsen.  2020.  A Lightweight Attribute Based Encryption Scheme with Constant Size Ciphertext for Internet of Things. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—6.

The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large number of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.

Ramasubramanian, Bhaskar, Niu, Luyao, Clark, Andrew, Bushnell, Linda, Poovendran, Radha.  2020.  Privacy-Preserving Resilience of Cyber-Physical Systems to Adversaries. 2020 59th IEEE Conference on Decision and Control (CDC). :3785–3792.

A cyber-physical system (CPS) is expected to be resilient to more than one type of adversary. In this paper, we consider a CPS that has to satisfy a linear temporal logic (LTL) objective in the presence of two kinds of adversaries. The first adversary has the ability to tamper with inputs to the CPS to influence satisfaction of the LTL objective. The interaction of the CPS with this adversary is modeled as a stochastic game. We synthesize a controller for the CPS to maximize the probability of satisfying the LTL objective under any policy of this adversary. The second adversary is an eavesdropper who can observe labeled trajectories of the CPS generated from the previous step. It could then use this information to launch other kinds of attacks. A labeled trajectory is a sequence of labels, where a label is associated to a state and is linked to the satisfaction of the LTL objective at that state. We use differential privacy to quantify the indistinguishability between states that are related to each other when the eavesdropper sees a labeled trajectory. Two trajectories of equal length will be differentially private if they are differentially private at each state along the respective trajectories. We use a skewed Kantorovich metric to compute distances between probability distributions over states resulting from actions chosen according to policies from related states in order to quantify differential privacy. Moreover, we do this in a manner that does not affect the satisfaction probability of the LTL objective. We validate our approach on a simulation of a UAV that has to satisfy an LTL objective in an adversarial environment.

Tahsini, A., Dunstatter, N., Guirguis, M., Ahmed, C. M..  2020.  DeepBLOC: A Framework for Securing CPS through Deep Reinforcement Learning on Stochastic Games. 2020 IEEE Conference on Communications and Network Security (CNS). :1–9.

One important aspect in protecting Cyber Physical System (CPS) is ensuring that the proper control and measurement signals are propagated within the control loop. The CPS research community has been developing a large set of check blocks that can be integrated within the control loop to check signals against various types of attacks (e.g., false data injection attacks). Unfortunately, it is not possible to integrate all these “checks” within the control loop as the overhead introduced when checking signals may violate the delay constraints of the control loop. Moreover, these blocks do not completely operate in isolation of each other as dependencies exist among them in terms of their effectiveness against detecting a subset of attacks. Thus, it becomes a challenging and complex problem to assign the proper checks, especially with the presence of a rational adversary who can observe the check blocks assigned and optimizes her own attack strategies accordingly. This paper tackles the inherent state-action space explosion that arises in securing CPS through developing DeepBLOC (DB)-a framework in which Deep Reinforcement Learning algorithms are utilized to provide optimal/sub-optimal assignments of check blocks to signals. The framework models stochastic games between the adversary and the CPS defender and derives mixed strategies for assigning check blocks to ensure the integrity of the propagated signals while abiding to the real-time constraints dictated by the control loop. Through extensive simulation experiments and a real implementation on a water purification system, we show that DB achieves assignment strategies that outperform other strategies and heuristics.

Jin, R., He, X., Dai, H..  2019.  On the Security-Privacy Tradeoff in Collaborative Security: A Quantitative Information Flow Game Perspective. IEEE Transactions on Information Forensics and Security. 14:3273–3286.
To contest the rapidly developing cyber-attacks, numerous collaborative security schemes, in which multiple security entities can exchange their observations and other relevant data to achieve more effective security decisions, are proposed and developed in the literature. However, the security-related information shared among the security entities may contain some sensitive information and such information exchange can raise privacy concerns, especially when these entities belong to different organizations. With such consideration, the interplay between the attacker and the collaborative entities is formulated as Quantitative Information Flow (QIF) games, in which the QIF theory is adapted to measure the collaboration gain and the privacy loss of the entities in the information sharing process. In particular, three games are considered, each corresponding to one possible scenario of interest in practice. Based on the game-theoretic analysis, the expected behaviors of both the attacker and the security entities are obtained. In addition, the simulation results are presented to validate the analysis.
Kotra, A., Eldosouky, A., Sengupta, S..  2020.  Every Anonymization Begins with k: A Game-Theoretic Approach for Optimized k Selection in k-Anonymization. 2020 International Conference on Advances in Computing and Communication Engineering (ICACCE). :1–6.
Privacy preservation is one of the greatest concerns when data is shared between different organizations. On the one hand, releasing data for research purposes is inevitable. On the other hand, sharing this data can jeopardize users' privacy. An effective solution, for the sharing organizations, is to use anonymization techniques to hide the users' sensitive information. One of the most popular anonymization techniques is k-Anonymization in which any data record is indistinguishable from at least k-1 other records. However, one of the fundamental challenges in choosing the value of k is the trade-off between achieving a higher privacy and the information loss associated with the anonymization. In this paper, the problem of choosing the optimal anonymization level for k-anonymization, under possible attacks, is studied when multiple organizations share their data to a common platform. In particular, two common types of attacks are considered that can target the k-anonymization technique. To this end, a novel game-theoretic framework is proposed to model the interactions between the sharing organizations and the attacker. The problem is formulated as a static game and its different Nash equilibria solutions are analytically derived. Simulation results show that the proposed framework can significantly improve the utility of the sharing organizations through optimizing the choice of k value.
Xu, Z., Easwaran, A..  2020.  A Game-Theoretic Approach to Secure Estimation and Control for Cyber-Physical Systems with a Digital Twin. 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS). :20–29.
Cyber-Physical Systems (CPSs) play an increasingly significant role in many critical applications. These valuable applications attract various sophisticated attacks. This paper considers a stealthy estimation attack, which aims to modify the state estimation of the CPSs. The intelligent attackers can learn defense strategies and use clandestine attack strategies to avoid detection. To address the issue, we design a Chi-square detector in a Digital Twin (DT), which is an online digital model of the physical system. We use a Signaling Game with Evidence (SGE) to find the optimal attack and defense strategies. Our analytical results show that the proposed defense strategies can mitigate the impact of the attack on the physical estimation and guarantee the stability of the CPSs. Finally, we use an illustrative application to evaluate the performance of the proposed framework.
Liao, S., Wu, J., Li, J., Bashir, A. K..  2020.  Proof-of-Balance: Game-Theoretic Consensus for Controller Load Balancing of SDN. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :231–236.
Software Defined Networking (SDN) focus on the isolation of control plane and data plane, greatly enhancing the network's support for heterogeneity and flexibility. However, although the programmable network greatly improves the performance of all aspects of the network, flexible load balancing across controllers still challenges the current SDN architecture. Complex application scenarios lead to flexible and changeable communication requirements, making it difficult to guarantee the Quality of Service (QoS) for SDN users. To address this issue, this paper proposes a paradigm that uses blockchain to incentive safe load balancing for multiple controllers. We proposed a controller consortium blockchain for secure and efficient load balancing of multi-controllers, which includes a new cryptographic currency balance coin and a novel consensus mechanism Proof-of-Balance (PoB). In addition, we have designed a novel game theory-based incentive mechanism to incentive controllers with tight communication resources to offload tasks to idle controllers. The security analysis and performance simulation results indicate the superiority and effectiveness of the proposed scheme.
Solovey, R., Lavrova, D..  2020.  Game-Theoretic Approach to Self-Regulation of Dynamic Network Infrastructure to Protect Against Cyber Attacks. 2020 International Scientific and Technical Conference Modern Computer Network Technologies (MoNeTeC). :1–7.
The paper presents the concept of applying a game theory approach in infrastructure of wireless dynamic networks to counter computer attacks. The applying of this approach will allow to create mechanism for adaptive reconfiguration of network structure in the context of implementation various types of computer attacks and to provide continuous operation of network even in conditions of destructive information impacts.
Dai, Q., Shi, L..  2020.  A Game-Theoretic Analysis of Cyber Attack-Mitigation in Centralized Feeder Automation System. 2020 IEEE Power Energy Society General Meeting (PESGM). :1–5.
The intelligent electronic devices widely deployed across the distribution network are inevitably making the feeder automation (FA) system more vulnerable to cyber-attacks, which would lead to disastrous socio-economic impacts. This paper proposes a three-stage game-theoretic framework that the defender allocates limited security resources to minimize the economic impacts on FA system while the attacker deploys limited attack resources to maximize the corresponding impacts. Meanwhile, the probability of successful attack is calculated based on the Bayesian attack graph, and a fault-tolerant location technique for centralized FA system is elaborately considered during analysis. The proposed game-theoretic framework is converted into a two-level zero-sum game model and solved by the particle swarm optimization (PSO) combined with a generalized reduced gradient algorithm. Finally, the proposed model is validated on distribution network for RBTS bus 2.
Fan, X., Zhang, F., Turamat, E., Tong, C., Wu, J. H., Wang, K..  2020.  Provenance-based Classification Policy based on Encrypted Search. 2020 2nd International Conference on Industrial Artificial Intelligence (IAI). :1–6.
As an important type of cloud data, digital provenance is arousing increasing attention on improving system performance. Currently, provenance has been employed to provide cues regarding access control and to estimate data quality. However, provenance itself might also be sensitive information. Therefore, provenance might be encrypted and stored in the Cloud. In this paper, we provide a mechanism to classify cloud documents by searching specific keywords from their encrypted provenance, and we prove our scheme achieves semantic security. In term of application of the proposed techniques, considering that files are classified to store separately in the cloud, in order to facilitate the regulation and security protection for the files, the classification policies can use provenance as conditions to determine the category of a document. Such as the easiest sample policy goes like: the documents have been reviewed twice can be classified as “public accessible”, which can be accessed by the public.
Kirkbride, P., Dewan, M. A. Akber, Lin, F..  2020.  Game-Like Captchas for Intrusion Detection. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :312—315.

In this paper, we consider a novel method of mining biometric data for user authentication by replacing traditional captchas with game-like captchas. The game-like captchas present the user with a short game in which they attempt to get a high score. The data produced from a user's game play will be used to produce a behavior biometric based on user interactions, such as mouse movement, click patterns and game choices. The baseline expectation of interactive behavior will be used as a single factor in an intrusion detection system providing continuous authentication, considering the factors such as IP address, location, time of use, website interactions, and behavior anomalies. In addition to acting as a source of data, game-like captchas are expected to deter bots and automated systems from accessing web-based services and improving the user experience for the end-users who have become accustomed to monotonous alternatives, such as Google's re-captcha.

Tao, J., Xiong, Y., Zhao, S., Xu, Y., Lin, J., Wu, R., Fan, C..  2020.  XAI-Driven Explainable Multi-view Game Cheating Detection. 2020 IEEE Conference on Games (CoG). :144–151.
Online gaming is one of the most successful applications having a large number of players interacting in an online persistent virtual world through the Internet. However, some cheating players gain improper advantages over normal players by using illegal automated plugins which has brought huge harm to game health and player enjoyment. Game industries have been devoting much efforts on cheating detection with multiview data sources and achieved great accuracy improvements by applying artificial intelligence (AI) techniques. However, generating explanations for cheating detection from multiple views still remains a challenging task. To respond to the different purposes of explainability in AI models from different audience profiles, we propose the EMGCD, the first explainable multi-view game cheating detection framework driven by explainable AI (XAI). It combines cheating explainers to cheating classifiers from different views to generate individual, local and global explanations which contributes to the evidence generation, reason generation, model debugging and model compression. The EMGCD has been implemented and deployed in multiple game productions in NetEase Games, achieving remarkable and trustworthy performance. Our framework can also easily generalize to other types of related tasks in online games, such as explainable recommender systems, explainable churn prediction, etc.
Kerim, A., Genc, B..  2020.  Mobile Games Success and Failure: Mining the Hidden Factors. 2020 7th International Conference on Soft Computing Machine Intelligence (ISCMI). :167–171.
Predicting the success of a mobile game is a prime issue in game industry. Thousands of games are being released each day. However, a few of them succeed while the majority fail. Towards the goal of investigating the potential correlation between the success of a mobile game and its specific attributes, this work was conducted. More than 17 thousands games were considered for that reason. We show that specific game attributes, such as number of IAPs (In-App Purchases), belonging to the puzzle genre, supporting different languages and being produced by a mature developer highly and positively affect the success of the game in the future. Moreover, we show that releasing the game in July and not including any IAPs seems to be highly associated with the game’s failure. Our second main contribution, is the proposal of a novel success score metric that reflects multiple objectives, in contrast to evaluating only revenue, average rating or rating count. We also employ different machine learning models, namely, SVM (Support Vector Machine), RF (Random Forest) and Deep Learning (DL) to predict this success score metric of a mobile game given its attributes. The trained models were able to predict this score, as well as the rating average and rating count of a mobile game with more than 70% accuracy. This prediction can help developers before releasing their game to the market to avoid any potential disappointments.
Mukhametov, D. R..  2020.  Self-organization of Network Communities via Blockchain Technology: Reputation Systems and Limits of Digital Democracy. 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO). :1—7.

The article is devoted to the analysis of the use of blockchain technology for self-organization of network communities. Network communities are characterized by the key role of trust in personal interactions, the need for repeated interactions, strong and weak ties within the network, social learning as the mechanism of self-organization. Therefore, in network communities reputation is the central component of social action, assessment of the situation, and formation of the expectations. The current proliferation of virtual network communities requires the development of appropriate technical infrastructure in the form of reputation systems - programs that provide calculation of network members reputation and organization of their cooperation and interaction. Traditional reputation systems have vulnerabilities in the field of information security and prevention of abusive behavior of agents. Overcoming these restrictions is possible through integration of reputation systems and blockchain technology that allows to increase transparency of reputation assessment system and prevent attempts of manipulation the system and social engineering. At the same time, the most promising is the use of blockchain-oracles to ensure communication between the algorithms of blockchain-based reputation system and the external information environment. The popularization of blockchain technology and its implementation in various spheres of social management, production control, economic exchange actualizes the problems of using digital technologies in political processes and their impact on the formation of digital authoritarianism, digital democracy and digital anarchism. The paper emphasizes that blockchain technology and reputation systems can equally benefit both the resources of government control and tools of democratization and public accountability to civil society or even practices of avoiding government. Therefore, it is important to take into account the problems of political institutionalization, path dependence and the creation of differentiated incentives as well as the technological aspects.

Gao, L., Sun, J., Li, J..  2020.  Security of Networked Control Systems with Incomplete Information Based on Game Theory. 2020 39th Chinese Control Conference (CCC). :6701—6706.

The security problem of networked control systems (NCSs) suffering denial of service(DoS) attacks with incomplete information is investigated in this paper. Data transmission among different components in NCSs may be blocked due to DoS attacks. We use the concept of security level to describe the degree of security of different components in an NCS. Intrusion detection system (IDS) is used to monitor the invalid data generated by DoS attacks. At each time slot, the defender considers which component to monitor while the attacker considers which place for invasion. A one-shot game between attacker and defender is built and both the complete information case and the incomplete information case are considered. Furthermore, a repeated game model with updating beliefs is also established based on the Bayes' rule. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

Ajenaghughrure, I. B., Sousa, S. C. da Costa, Lamas, D..  2020.  Risk and Trust in artificial intelligence technologies: A case study of Autonomous Vehicles. 2020 13th International Conference on Human System Interaction (HSI). :118–123.
This study investigates how risk influences users' trust before and after interactions with technologies such as autonomous vehicles (AVs'). Also, the psychophysiological correlates of users' trust from users” eletrodermal activity responses. Eighteen (18) carefully selected participants embark on a hypothetical trip playing an autonomous vehicle driving game. In order to stay safe, throughout the drive experience under four risk conditions (very high risk, high risk, low risk and no risk) that are based on automotive safety and integrity levels (ASIL D, C, B, A), participants exhibit either high or low trust by evaluating the AVs' to be highly or less trustworthy and consequently relying on the Artificial intelligence or the joystick to control the vehicle. The result of the experiment shows that there is significant increase in users' trust and user's delegation of controls to AVs' as risk decreases and vice-versa. In addition, there was a significant difference between user's initial trust before and after interacting with AVs' under varying risk conditions. Finally, there was a significant correlation in users' psychophysiological responses (electrodermal activity) when exhibiting higher and lower trust levels towards AVs'. The implications of these results and future research opportunities are discussed.
Gupta, K., Hajika, R., Pai, Y. S., Duenser, A., Lochner, M., Billinghurst, M..  2020.  Measuring Human Trust in a Virtual Assistant using Physiological Sensing in Virtual Reality. 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). :756–765.
With the advancement of Artificial Intelligence technology to make smart devices, understanding how humans develop trust in virtual agents is emerging as a critical research field. Through our research, we report on a novel methodology to investigate user's trust in auditory assistance in a Virtual Reality (VR) based search task, under both high and low cognitive load and under varying levels of agent accuracy. We collected physiological sensor data such as electroencephalography (EEG), galvanic skin response (GSR), and heart-rate variability (HRV), subjective data through questionnaire such as System Trust Scale (STS), Subjective Mental Effort Questionnaire (SMEQ) and NASA-TLX. We also collected a behavioral measure of trust (congruency of users' head motion in response to valid/ invalid verbal advice from the agent). Our results indicate that our custom VR environment enables researchers to measure and understand human trust in virtual agents using the matrices, and both cognitive load and agent accuracy play an important role in trust formation. We discuss the implications of the research and directions for future work.