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2021-03-30
Abbas, H., Suguri, H., Yan, Z., Allen, W., Hei, X. S..  2020.  IEEE Access Special Section: Security Analytics and Intelligence for Cyber Physical Systems. IEEE Access. 8:208195—208198.

A Cyber Physical System (CPS) is a smart network system with actuators, embedded sensors, and processors to interact with the physical world by guaranteeing the performance and supporting real-time operations of safety critical applications. These systems drive innovation and are a source of competitive advantage in today’s challenging world. By observing the behavior of physical processes and activating actions, CPS can alter its behavior to make the physical environment perform better and more accurately. By definition, CPS basically has two major components including cyber systems and physical processes. Examples of CPS include autonomous transportation systems, robotics systems, medical monitoring, automatic pilot avionics, and smart grids. Advances in CPS will empower scalability, capability, usability, and adaptability, which will go beyond the simple systems of today. At the same time, CPS has also increased cybersecurity risks and attack surfaces. Cyber attackers can harm such systems from multiple sources while hiding their identities. As a result of sophisticated threat matrices, insufficient knowledge about threat patterns, and industrial network automation, CPS has become extremely insecure. Since such infrastructure is networked, attacks can be prompted easily without much human participation from remote locations, thereby making CPS more vulnerable to sophisticated cyber-attacks. In turn, large-scale data centers managing a huge volume of CPS data become vulnerable to cyber-attacks. To secure CPS, the role of security analytics and intelligence is significant. It brings together huge amounts of data to create threat patterns, which can be used to prevent cyber-attacks in a timely fashion. The primary objective of this Special Section in IEEE A CCESS is to collect a complementary and diverse set of articles, which demonstrate up-to-date information and innovative developments in the domain of security analytics and intelligence for CPS.

Baybulatov, A. A., Promyslov, V. G..  2020.  On a Deterministic Approach to Solving Industrial Control System Problems. 2020 International Russian Automation Conference (RusAutoCon). :115—120.

Since remote ages, queues and delays have been a rather exasperating reality of human daily life. Today, they pursue us everywhere: in technical, social, socio-technical, and even control systems, dramatically deteriorating their performance. In this variety, it is the computer systems that are sure to cause the growing anxiety in our digital era. Although for our everyday Internet surfing, experiencing long-lasting and annoying delays is an unpleasant but not dangerous situation, for industrial control systems, especially those dealing with critical infrastructures, such behavior is unacceptable. The article presents a deterministic approach to solving some digital control system problems associated with delays and backlogs. Being based on Network calculus, in contrast to statistical methods of Queuing theory, it provides worst-case results, which are eminently desirable for critical infrastructures. The article covers the basics of a theory of deterministic queuing systems Network calculus, its evolution regarding the relationship between backlog bound and delay, and a technique for handling empirical data. The problems being solved by the deterministic approach: standard calculation of network performance measures, estimation of database maximum updating time, and cybersecurity assessment including such issues as the CIA triad representation, operational technology influence, and availability understanding focusing on its correlation with a delay are thoroughly discussed as well.

Khan, W. Z., Arshad, Q.-u-A., Hakak, S., Khan, M. K., Saeed-Ur-Rehman.  2020.  Trust Management in Social Internet of Things: Architectures, Recent Advancements and Future Challenges. IEEE Internet of Things Journal. :1—1.

Social Internet of Things (SIoT) is an extension of Internet of Things (IoT) that converges with Social networking concepts to create Social networks of interconnected smart objects. This convergence allows the enrichment of the two paradigms, resulting into new ecosystems. While IoT follows two interaction paradigms, human-to-human (H2H) and thing-to-thing (T2T), SIoT adds on human-to-thing (H2T) interactions. SIoT enables smart “Social objects” that intelligently mimic the social behavior of human in the daily life. These social objects are equipped with social functionalities capable of discovering other social objects in the surroundings and establishing social relationships. They crawl through the social network of objects for the sake of searching for services and information of interest. The notion of trust and trustworthiness in social communities formed in SIoT is still new and in an early stage of investigation. In this paper, our contributions are threefold. First, we present the fundamentals of SIoT and trust concepts in SIoT, clarifying the similarities and differences between IoT and SIoT. Second, we categorize the trust management solutions proposed so far in the literature for SIoT over the last six years and provide a comprehensive review. We then perform a comparison of the state of the art trust management schemes devised for SIoT by performing comparative analysis in terms of trust management process. Third, we identify and discuss the challenges and requirements in the emerging new wave of SIoT, and also highlight the challenges in developing trust and evaluating trustworthiness among the interacting social objects.

Foroughi, F., Hadipour, H., Shafiee, A. M..  2020.  High-Performance Monitoring Sensors for Home Computer Users Security Profiling. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1—7.

Recognising user's risky behaviours in real-time is an important element of providing appropriate solutions and recommending suitable actions for responding to cybersecurity threats. Employing user modelling and machine learning can make this process automated by requires high-performance intelligent agent to create the user security profile. User profiling is the process of producing a profile of the user from historical information and past details. This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user's privacy. This observer agent helps to create a decision-making model that influences the user's decision following real-time threats or risky behaviours.

Ashiku, L., Dagli, C..  2020.  Agent Based Cybersecurity Model for Business Entity Risk Assessment. 2020 IEEE International Symposium on Systems Engineering (ISSE). :1—6.

Computer networks and surging advancements of innovative information technology construct a critical infrastructure for network transactions of business entities. Information exchange and data access though such infrastructure is scrutinized by adversaries for vulnerabilities that lead to cyber-attacks. This paper presents an agent-based system modelling to conceptualize and extract explicit and latent structure of the complex enterprise systems as well as human interactions within the system to determine common vulnerabilities of the entity. The model captures emergent behavior resulting from interactions of multiple network agents including the number of workstations, regular, administrator and third-party users, external and internal attacks, defense mechanisms for the network setting, and many other parameters. A risk-based approach to modelling cybersecurity of a business entity is utilized to derive the rate of attacks. A neural network model will generalize the type of attack based on network traffic features allowing dynamic state changes. Rules of engagement to generate self-organizing behavior will be leveraged to appoint a defense mechanism suitable for the attack-state of the model. The effectiveness of the model will be depicted by time-state chart that shows the number of affected assets for the different types of attacks triggered by the entity risk and the time it takes to revert into normal state. The model will also associate a relevant cost per incident occurrence that derives the need for enhancement of security solutions.

Ben-Yaakov, Y., Meyer, J., Wang, X., An, B..  2020.  User detection of threats with different security measures. 2020 IEEE International Conference on Human-Machine Systems (ICHMS). :1—6.

Cyber attacks and the associated costs made cybersecurity a vital part of any system. User behavior and decisions are still a major part in the coping with these risks. We developed a model of optimal investment and human decisions with security measures, given that the effectiveness of each measure depends partly on the performance of the others. In an online experiment, participants classified events as malicious or non-malicious, based on the value of an observed variable. Prior to making the decisions, they had invested in three security measures - a firewall, an IDS or insurance. In three experimental conditions, maximal investment in only one of the measures was optimal, while in a fourth condition, participants should not have invested in any of the measures. A previous paper presents the analysis of the investment decisions. This paper reports users' classifications of events when interacting with these systems. The use of security mechanisms helped participants gain higher scores. Participants benefited in particular from purchasing IDS and/or Cyber Insurance. Participants also showed higher sensitivity and compliance with the alerting system when they could benefit from investing in the IDS. Participants, however, did not adjust their behavior optimally to the security settings they had chosen. The results demonstrate the complex nature of risk-related behaviors and the need to consider human abilities and biases when designing cyber security systems.

Ganfure, G. O., Wu, C.-F., Chang, Y.-H., Shih, W.-K..  2020.  DeepGuard: Deep Generative User-behavior Analytics for Ransomware Detection. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). :1—6.

In the last couple of years, the move to cyberspace provides a fertile environment for ransomware criminals like ever before. Notably, since the introduction of WannaCry, numerous ransomware detection solution has been proposed. However, the ransomware incidence report shows that most organizations impacted by ransomware are running state of the art ransomware detection tools. Hence, an alternative solution is an urgent requirement as the existing detection models are not sufficient to spot emerging ransomware treat. With this motivation, our work proposes "DeepGuard," a novel concept of modeling user behavior for ransomware detection. The main idea is to log the file-interaction pattern of typical user activity and pass it through deep generative autoencoder architecture to recreate the input. With sufficient training data, the model can learn how to reconstruct typical user activity (or input) with minimal reconstruction error. Hence, by applying the three-sigma limit rule on the model's output, DeepGuard can distinguish the ransomware activity from the user activity. The experiment result shows that DeepGuard effectively detects a variant class of ransomware with minimal false-positive rates. Overall, modeling the attack detection with user-behavior permits the proposed strategy to have deep visibility of various ransomware families.

Meshkat, L., Miller, R. L., Hillsgrove, C., King, J..  2020.  Behavior Modeling for Cybersecurity. 2020 Annual Reliability and Maintainability Symposium (RAMS). :1—7.

A significant percentage of cyber security incidents can be prevented by changing human behaviors. The humans in the loop include the system administrators, software developers, end users and the personnel responsible for securing the system. Each of these group of people work in a given context and are affected by both soft factors such as management influences and workload and more tangible factors in the real world such as errors in procedures and scanning devices, faulty code or the usability of the systems they work with.

Faith, B. Fatokun, Hamid, S., Norman, A., Johnson, O. Fatokun, Eke, C. I..  2020.  Relating Factors of Tertiary Institution Students’ Cybersecurity Behavior. 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS). :1—6.

Humans are majorly identified as the weakest link in cybersecurity. Tertiary institution students undergo lot of cybersecurity issues due to their constant Internet exposure, however there is a lack in literature with regards to tertiary institution students' cybersecurity behaviors. This research aimed at linking the factors responsible for tertiary institutions students' cybersecurity behavior, via validated cybersecurity factors, Perceived Vulnerability (PV); Perceived Barriers (PBr); Perceived Severity (PS); Security Self-Efficacy (SSE); Response Efficacy (RE); Cues to Action (CA); Peer Behavior (PBhv); Computer Skills (CS); Internet Skills (IS); Prior Experience with Computer Security Practices (PE); Perceived Benefits (PBnf); Familiarity with Cyber-Threats (FCT), thus exploring the relationship between the factors and the students' Cybersecurity Behaviors (CSB). A cross-sectional online survey was used to gather data from 450 undergraduate and postgraduate students from tertiary institutions within Klang Valley, Malaysia. Correlation Analysis was used to find the relationships existing among the cybersecurity behavioral factors via SPSS version 25. Results indicate that all factors were significantly related to the cybersecurity behaviors of the students apart from Perceived Severity. Practically, the study instigates the need for more cybersecurity training and practices in the tertiary institutions.

Shah, P. R., Agarwal, A..  2020.  Cybersecurity Behaviour of Smartphone Users Through the Lens of Fogg Behaviour Model. 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA). :79—82.

It is now a fact that human is the weakest link in the cybersecurity chain. Many theories from behavioural science like the theory of planned behaviour and protection motivation theory have been used to investigate the factors that affect the cybersecurity behaviour and practices of the end-user. In this paper, the researchers have used Fogg behaviour model (FBM) to study factors affecting the cybersecurity behaviour and practices of smartphone users. This study found that the odds of secure behaviour and practices by respondents with high motivation and high ability were 4.64 times more than the respondents with low motivation and low ability. This study describes how FBM may be used in the design and development of cybersecurity awareness program leading to a behaviour change.

2021-03-29
Grundy, J..  2020.  Human-centric Software Engineering for Next Generation Cloud- and Edge-based Smart Living Applications. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). :1—10.

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.

Bogdan-Iulian, C., Vasilică-Gabriel, S., Alexandru, M. D., Nicolae, G., Andrei, V..  2020.  Improved Secure Internet of Things System using Web Services and Low Power Single-board Computers. 2020 International Conference on e-Health and Bioengineering (EHB). :1—5.

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.

Guo, Y., Wang, B., Hughes, D., Lewis, M., Sycara, K..  2020.  Designing Context-Sensitive Norm Inverse Reinforcement Learning Framework for Norm-Compliant Autonomous Agents. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :618—625.

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.

Liu, F., Wen, Y., Wu, Y., Liang, S., Jiang, X., Meng, D..  2020.  MLTracer: Malicious Logins Detection System via Graph Neural Network. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :715—726.

Malicious login, especially lateral movement, has been a primary and costly threat for enterprises. However, there exist two critical challenges in the existing methods. Specifically, they heavily rely on a limited number of predefined rules and features. When the attack patterns change, security experts must manually design new ones. Besides, they cannot explore the attributes' mutual effect specific to login operations. We propose MLTracer, a graph neural network (GNN) based system for detecting such attacks. It has two core components to tackle the previous challenges. First, MLTracer adopts a novel method to differentiate crucial attributes of login operations from the rest without experts' designated features. Second, MLTracer leverages a GNN model to detect malicious logins. The model involves a convolutional neural network (CNN) to explore attributes of login operations, and a co-attention mechanism to mutually improve the representations (vectors) of login attributes through learning their login-specific relation. We implement an evaluation of such an approach. The results demonstrate that MLTracer significantly outperforms state-of-the-art methods. Moreover, MLTracer effectively detects various attack scenarios with a remarkably low false positive rate (FPR).

Distler, V., Lallemand, C., Koenig, V..  2020.  Making Encryption Feel Secure: Investigating how Descriptions of Encryption Impact Perceived Security. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :220—229.

When communication about security to end users is ineffective, people frequently misinterpret the protection offered by a system. The discrepancy between the security users perceive a system to have and the actual system state can lead to potentially risky behaviors. It is thus crucial to understand how security perceptions are shaped by interface elements such as text-based descriptions of encryption. This article addresses the question of how encryption should be described to non-experts in a way that enhances perceived security. We tested the following within-subject variables in an online experiment (N=309): a) how to best word encryption, b) whether encryption should be described with a focus on the process or outcome, or both c) whether the objective of encryption should be mentioned d) when mentioning the objective of encryption, how to best describe it e) whether a hash should be displayed to the user. We also investigated the role of context (between subjects). The verbs "encrypt" and "secure" performed comparatively well at enhancing perceived security. Overall, participants stated that they felt more secure not knowing about the objective of encryption. When it is necessary to state the objective, positive wording of the objective of encryption worked best. We discuss implications and why using these results to design for perceived lack of security might be of interest as well. This leads us to discuss ethical concerns, and we give guidelines for the design of user interfaces where encryption should be communicated to end users.

Maklachkova, V. V., Dokuchaev, V. A., Statev, V. Y..  2020.  Risks Identification in the Exploitation of a Geographically Distributed Cloud Infrastructure for Storing Personal Data. 2020 International Conference on Engineering Management of Communication and Technology (EMCTECH). :1—6.

Throughout the life cycle of any technical project, the enterprise needs to assess the risks associated with its development, commissioning, operation and decommissioning. This article defines the task of researching risks in relation to the operation of a data storage subsystem in the cloud infrastructure of a geographically distributed company and the tools that are required for this. Analysts point out that, compared to 2018, in 2019 there were 3.5 times more cases of confidential information leaks from storages on unprotected (freely accessible due to incorrect configuration) servers in cloud services. The total number of compromised personal data and payment information records increased 5.4 times compared to 2018 and amounted to more than 8.35 billion records. Moreover, the share of leaks of payment information has decreased, but the percentage of leaks of personal data has grown and accounts for almost 90% of all leaks from cloud storage. On average, each unsecured service identified resulted in 33.7 million personal data records being leaked. Leaks are mainly related to misconfiguration of services and stored resources, as well as human factors. These impacts can be minimized by improving the skills of cloud storage administrators and regularly auditing storage. Despite its seeming insecurity, the cloud is a reliable way of storing data. At the same time, leaks are still occurring. According to Kaspersky Lab, every tenth (11%) data leak from the cloud became possible due to the actions of the provider, while a third of all cyber incidents in the cloud (31% in Russia and 33% in the world) were due to gullibility company employees caught up in social engineering techniques. Minimizing the risks associated with the storage of personal data is one of the main tasks when operating a company's cloud infrastructure.

Juyal, S., Sharma, S., Harbola, A., Shukla, A. S..  2020.  Privacy and Security of IoT based Skin Monitoring System using Blockchain Approach. 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). :1—5.

Remote patient monitoring is a system that focuses on patients care and attention with the advent of the Internet of Things (IoT). The technology makes it easier to track distance, but also to diagnose and provide critical attention and service on demand so that billions of people are safer and more safe. Skincare monitoring is one of the growing fields of medical care which requires IoT monitoring, because there is an increasing number of patients, but cures are restricted to the number of available dermatologists. The IoT-based skin monitoring system produces and store volumes of private medical data at the cloud from which the skin experts can access it at remote locations. Such large-scale data are highly vulnerable and otherwise have catastrophic results for privacy and security mechanisms. Medical organizations currently do not concentrate much on maintaining safety and privacy, which are of major importance in the field. This paper provides an IoT based skin surveillance system based on a blockchain data protection and safety mechanism. A secure data transmission mechanism for IoT devices used in a distributed architecture is proposed. Privacy is assured through a unique key to identify each user when he registers. The principle of blockchain also addresses security issues through the generation of hash functions on every transaction variable. We use blockchain consortiums that meet our criteria in a decentralized environment for controlled access. The solutions proposed allow IoT based skin surveillance systems to privately and securely store and share medical data over the network without disturbance.

Schiliro, F., Moustafa, N., Beheshti, A..  2020.  Cognitive Privacy: AI-enabled Privacy using EEG Signals in the Internet of Things. 2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys). :73—79.

With the advent of Industry 4.0, the Internet of Things (IoT) and Artificial Intelligence (AI), smart entities are now able to read the minds of users via extracting cognitive patterns from electroencephalogram (EEG) signals. Such brain data may include users' experiences, emotions, motivations, and other previously private mental and psychological processes. Accordingly, users' cognitive privacy may be violated and the right to cognitive privacy should protect individuals against the unconsented intrusion by third parties into the brain data as well as against the unauthorized collection of those data. This has caused a growing concern among users and industry experts that laws to protect the right to cognitive liberty, right to mental privacy, right to mental integrity, and the right to psychological continuity. In this paper, we propose an AI-enabled EEG model, namely Cognitive Privacy, that aims to protect data and classifies users and their tasks from EEG data. We present a model that protects data from disclosure using normalized correlation analysis and classifies subjects (i.e., a multi-classification problem) and their tasks (i.e., eye open and eye close as a binary classification problem) using a long-short term memory (LSTM) deep learning approach. The model has been evaluated using the EEG data set of PhysioNet BCI, and the results have revealed its high performance of classifying users and their tasks with achieving high data privacy.

Anell, S., Gröber, L., Krombholz, K..  2020.  End User and Expert Perceptions of Threats and Potential Countermeasures. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :230—239.

Experts often design security and privacy technology with specific use cases and threat models in mind. In practice however, end users are not aware of these threats and potential countermeasures. Furthermore, mis-conceptions about the benefits and limitations of security and privacy technology inhibit large-scale adoption by end users. In this paper, we address this challenge and contribute a qualitative study on end users' and security experts' perceptions of threat models and potential countermeasures. We follow an inductive research approach to explore perceptions and mental models of both security experts and end users. We conducted semi-structured interviews with 8 security experts and 13 end users. Our results suggest that in contrast to security experts, end users neglect acquaintances and friends as attackers in their threat models. Our findings highlight that experts value technical countermeasures whereas end users try to implement trust-based defensive methods.

Das, T., Eldosouky, A. R., Sengupta, S..  2020.  Think Smart, Play Dumb: Analyzing Deception in Hardware Trojan Detection Using Game Theory. 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–8.
In recent years, integrated circuits (ICs) have become significant for various industries and their security has been given greater priority, specifically in the supply chain. Budgetary constraints have compelled IC designers to offshore manufacturing to third-party companies. When the designer gets the manufactured ICs back, it is imperative to test for potential threats like hardware trojans (HT). In this paper, a novel multi-level game-theoretic framework is introduced to analyze the interactions between a malicious IC manufacturer and the tester. In particular, the game is formulated as a non-cooperative, zero-sum, repeated game using prospect theory (PT) that captures different players' rationalities under uncertainty. The repeated game is separated into a learning stage, in which the defender learns about the attacker's tendencies, and an actual game stage, where this learning is used. Experiments show great incentive for the attacker to deceive the defender about their actual rationality by "playing dumb" in the learning stage (deception). This scenario is captured using hypergame theory to model the attacker's view of the game. The optimal deception rationality of the attacker is analytically derived to maximize utility gain. For the defender, a first-step deception mitigation process is proposed to thwart the effects of deception. Simulation results show that the attacker can profit from the deception as it can successfully insert HTs in the manufactured ICs without being detected.
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.
Lakhdhar, Y., Rekhis, S., Sabir, E..  2020.  A Game Theoretic Approach For Deploying Forensic Ready Systems. 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). :1–6.
Cyber incidents are occurring every day using various attack strategies. Deploying security solutions with strong configurations will reduce the attack surface and improve the forensic readiness, but will increase the security overhead and cost. In contrast, using moderate or low security configurations will reduce that overhead, but will inevitably decrease the investigation readiness. To avoid the use of cost-prohibitive approaches in developing forensic-ready systems, we present in this paper a game theoretic approach for deploying an investigation-ready infrastructure. The proposed game is a non-cooperative two-player game between an adaptive cyber defender that uses a cognitive security solution to increase the investigation readiness and reduce the attackers' untraceability, and a cyber attacker that wants to execute non-provable attacks with a low cost. The cognitive security solution takes its strategic decision, mainly based on its ability to make forensic experts able to differentiate between provable identifiable, provable non-identifiable, and non-provable attack scenarios, starting from the expected evidences to be generated. We study the behavior of the two strategic players, looking for a mixed Nash equilibrium during competition and computing the probabilities of attacking and defending. A simulation is conducted to prove the efficiency of the proposed model in terms of the mean percentage of gained security cost, the number of stepping stones that an attacker creates and the rate of defender false decisions compared to two different approaches.