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2021-10-12
Muller, Tim, Wang, Dongxia, Sun, Jun.  2020.  Provably Robust Decisions based on Potentially Malicious Sources of Information. 2020 IEEE 33rd Computer Security Foundations Symposium (CSF). :411–424.
Sometimes a security-critical decision must be made using information provided by peers. Think of routing messages, user reports, sensor data, navigational information, blockchain updates. Attackers manifest as peers that strategically report fake information. Trust models use the provided information, and attempt to suggest the correct decision. A model that appears accurate by empirical evaluation of attacks may still be susceptible to manipulation. For a security-critical decision, it is important to take the entire attack space into account. Therefore, we define the property of robustness: the probability of deciding correctly, regardless of what information attackers provide. We introduce the notion of realisations of honesty, which allow us to bypass reasoning about specific feedback. We present two schemes that are optimally robust under the right assumptions. The “majority-rule” principle is a special case of the other scheme which is more general, named “most plausible realisations”.
2021-09-17
Christie V, Samuel H., Smirnova, Daria, Chopra, Amit K., Singh, Munindar P..  2020.  Protocols Over Things: A Decentralized Programming Model for the Internet of Things. 53:60–68.
Current programming models for developing Internet of Things (IoT) applications are logically centralized and ill-suited for most IoT applications. We contribute Protocols over Things, a decentralized programming model that represents an IoT application via a protocol between the parties involved and provides improved performance over network-level delivery guarantees.
2021-09-16
Venkataramanan, Venkatesh, Hahn, Adam, Srivastava, Anurag.  2020.  CP-SAM: Cyber-Physical Security Assessment Metric for Monitoring Microgrid Resiliency. IEEE Transactions on Smart Grid. 11:1055–1065.
Trustworthy and secure operation of the cyber-power system calls for resilience against malicious and accidental failures. The objective of a resilient system is to withstand and recover operation of the system to supply critical loads despite multiple contingencies in the system. To take timely actions, we need to continuously measure the cyberphysical security of the system. We propose a cyber-physical security assessment metric (CP-SAM) based on quantitative factors affecting resiliency and utilizing concepts from graph theoretic analysis, probabilistic model of availability, attack graph metrics, and vulnerabilities across different layers of the microgrid system. These factors are integrated into a single metric using a multi-criteria decision making (MCDM) technique, Choquet Integral to compute CP-SAM. The developed metric will be valuable for i) monitoring the microgrid resiliency considering a holistic cyber-physical model; and ii) enable better decision-making to select best possible mitigation strategies towards resilient microgrid system. Developed CP-SAM can be extended for active distribution system and has been validated in a real-world power-grid test-bed to monitor the microgrid resiliency.
2021-07-27
MacDermott, Áine, Carr, John, Shi, Qi, Baharon, Mohd Rizuan, Lee, Gyu Myoung.  2020.  Privacy Preserving Issues in the Dynamic Internet of Things (IoT). 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1–6.
Convergence of critical infrastructure and data, including government and enterprise, to the dynamic Internet of Things (IoT) environment and future digital ecosystems exhibit significant challenges for privacy and identity in these interconnected domains. There are an increasing variety of devices and technologies being introduced, rendering existing security tools inadequate to deal with the dynamic scale and varying actors. The IoT is increasingly data driven with user sovereignty being essential - and actors in varying scenarios including user/customer, device, manufacturer, third party processor, etc. Therefore, flexible frameworks and diverse security requirements for such sensitive environments are needed to secure identities and authenticate IoT devices and their data, protecting privacy and integrity. In this paper we present a review of the principles, techniques and algorithms that can be adapted from other distributed computing paradigms. Said review will be used in application to the development of a collaborative decision-making framework for heterogeneous entities in a distributed domain, whilst simultaneously highlighting privacy preserving issues in the IoT. In addition, we present our trust-based privacy preserving schema using Dempster-Shafer theory of evidence. While still in its infancy, this application could help maintain a level of privacy and nonrepudiation in collaborative environments such as the IoT.
2021-06-02
Bychkov, Igor, Feoktistov, Alexander, Gorsky, Sergey, Edelev, Alexei, Sidorov, Ivan, Kostromin, Roman, Fereferov, Evgeniy, Fedorov, Roman.  2020.  Supercomputer Engineering for Supporting Decision-making on Energy Systems Resilience. 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT). :1—6.
We propose a new approach to creating a subject-oriented distributed computing environment. Such an environment is used to support decision-making in solving relevant problems of ensuring energy systems resilience. The proposed approach is based on the idea of advancing and integrating the following important capabilities in supercomputer engineering: continuous integration, delivery, and deployment of the system and applied software, high-performance computing in heterogeneous environments, multi-agent intelligent computation planning and resource allocation, big data processing and geo-information servicing for subject information, including weakly structured data, and decision-making support. This combination of capabilities and their advancing are unique to the subject domain under consideration, which is related to combinatorial studying critical objects of energy systems. Evaluation of decision-making alternatives is carrying out through applying combinatorial modeling and multi-criteria selection rules. The Orlando Tools framework is used as the basis for an integrated software environment. It implements a flexible modular approach to the development of scientific applications (distributed applied software packages).
2021-06-01
Wang, Qi, Zhao, Weiliang, Yang, Jian, Wu, Jia, Zhou, Chuan, Xing, Qianli.  2020.  AtNE-Trust: Attributed Trust Network Embedding for Trust Prediction in Online Social Networks. 2020 IEEE International Conference on Data Mining (ICDM). :601–610.
Trust relationship prediction among people provides valuable supports for decision making, information dissemination, and product promotion in online social networks. Network embedding has achieved promising performance for link prediction by learning node representations that encode intrinsic network structures. However, most of the existing network embedding solutions cannot effectively capture the properties of a trust network that has directed edges and nodes with in/out links. Furthermore, there usually exist rich user attributes in trust networks, such as ratings, reviews, and the rated/reviewed items, which may exert significant impacts on the formation of trust relationships. It is still lacking a network embedding-based method that can adequately integrate these properties for trust prediction. In this work, we develop an AtNE-Trust model to address these issues. We firstly capture user embedding from both the trust network structures and user attributes. Then we design a deep multi-view representation learning module to further mine and fuse the obtained user embedding. Finally, a trust evaluation module is developed to predict the trust relationships between users. Representation learning and trust evaluation are optimized together to capture high-quality user embedding and make accurate predictions simultaneously. A set of experiments against the real-world datasets demonstrates the effectiveness of the proposed approach.
2021-05-26
Boursinos, Dimitrios, Koutsoukos, Xenofon.  2020.  Trusted Confidence Bounds for Learning Enabled Cyber-Physical Systems. 2020 IEEE Security and Privacy Workshops (SPW). :228—233.

Cyber-physical systems (CPS) can benefit by the use of learning enabled components (LECs) such as deep neural networks (DNNs) for perception and decision making tasks. However, DNNs are typically non-transparent making reasoning about their predictions very difficult, and hence their application to safety-critical systems is very challenging. LECs could be integrated easier into CPS if their predictions could be complemented with a confidence measure that quantifies how much we trust their output. The paper presents an approach for computing confidence bounds based on Inductive Conformal Prediction (ICP). We train a Triplet Network architecture to learn representations of the input data that can be used to estimate the similarity between test examples and examples in the training data set. Then, these representations are used to estimate the confidence of set predictions from a classifier that is based on the neural network architecture used in the triplet. The approach is evaluated using a robotic navigation benchmark and the results show that we can computed trusted confidence bounds efficiently in real-time.

2021-05-25
Nazemi, Mostafa, Dehghanian, Payman, Alhazmi, Mohannad, Wang, Fei.  2020.  Multivariate Uncertainty Characterization for Resilience Planning in Electric Power Systems. 2020 IEEE/IAS 56th Industrial and Commercial Power Systems Technical Conference (I CPS). :1—8.
Following substantial advancements in stochastic classes of decision-making optimization problems, scenario-based stochastic optimization, robust\textbackslashtextbackslash distributionally robust optimization, and chance-constrained optimization have recently gained an increasing attention. Despite the remarkable developments in probabilistic forecast of uncertainties (e.g., in renewable energies), most approaches are still being employed in a univariate framework which fails to unlock a full understanding on the underlying interdependence among uncertain variables of interest. In order to yield cost-optimal solutions with predefined probabilistic guarantees, conditional and dynamic interdependence in uncertainty forecasts should be accommodated in power systems decision-making. This becomes even more important during the emergencies where high-impact low-probability (HILP) disasters result in remarkable fluctuations in the uncertain variables. In order to model the interdependence correlation structure between different sources of uncertainty in power systems during both normal and emergency operating conditions, this paper aims to bridge the gap between the probabilistic forecasting methods and advanced optimization paradigms; in particular, perdition regions are generated in the form of ellipsoids with probabilistic guarantees. We employ a modified Khachiyan's algorithm to compute the minimum volume enclosing ellipsoids (MVEE). Application results based on two datasets on wind and photovoltaic power are used to verify the efficiency of the proposed framework.
2021-04-27
Samuel, J., Aalab, K., Jaskolka, J..  2020.  Evaluating the Soundness of Security Metrics from Vulnerability Scoring Frameworks. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :442—449.

Over the years, a number of vulnerability scoring frameworks have been proposed to characterize the severity of known vulnerabilities in software-dependent systems. These frameworks provide security metrics to support decision-making in system development and security evaluation and assurance activities. When used in this context, it is imperative that these security metrics be sound, meaning that they can be consistently measured in a reproducible, objective, and unbiased fashion while providing contextually relevant, actionable information for decision makers. In this paper, we evaluate the soundness of the security metrics obtained via several vulnerability scoring frameworks. The evaluation is based on the Method for DesigningSound Security Metrics (MDSSM). We also present several recommendations to improve vulnerability scoring frameworks to yield more sound security metrics to support the development of secure software-dependent systems.

2021-04-09
Fourastier, Y., Baron, C., Thomas, C., Esteban, P..  2020.  Assurance levels for decision making in autonomous intelligent systems and their safety. 2020 IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT). :475—483.
The autonomy of intelligent systems and their safety rely on their ability for local decision making based on collected environmental information. This is even more for cyber-physical systems running safety critical activities. While this intelligence is partial and fragmented, and cognitive techniques are of limited maturity, the decision function must produce results whose validity and scope must be weighted in light of the underlying assumptions, unavoidable uncertainty and hypothetical safety limitation. Besides the cognitive techniques dependability, it is about the assurance level of the decision self-making. Beyond the pure decision-making capabilities of the autonomous intelligent system, we need techniques that guarantee the system assurance required for the intended use. Security mechanisms for cognitive systems may be consequently tightly intricated. We propose a trustworthiness module which is part of the system and its resulting safety. In this paper, we briefly review the state of the art regarding the dependability of cognitive techniques, the assurance level definition in this context, and related engineering practices. We elaborate regarding the design of autonomous intelligent systems safety, then we discuss its security design and approaches for the mitigation of safety violations by the cognitive functions.
2021-03-30
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.

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.

2021-03-17
Straub, J..  2020.  Modeling Attack, Defense and Threat Trees and the Cyber Kill Chain, ATT CK and STRIDE Frameworks as Blackboard Architecture Networks. 2020 IEEE International Conference on Smart Cloud (SmartCloud). :148—153.

Multiple techniques for modeling cybersecurity attacks and defense have been developed. The use of tree- structures as well as techniques proposed by several firms (such as Lockheed Martin's Cyber Kill Chain, Microsoft's STRIDE and the MITRE ATT&CK frameworks) have all been demonstrated. These approaches model actions that can be taken to attack or stopped to secure infrastructure and other resources, at different levels of detail.This paper builds on prior work on using the Blackboard Architecture for cyberwarfare and proposes a generalized solution for modeling framework/paradigm-based attacks that go beyond the deployment of a single exploit against a single identified target. The Blackboard Architecture Cyber Command Entity attack Route (BACCER) identification system combines rules and facts that implement attack type determination and attack decision making logic with actions that implement reconnaissance techniques and attack and defense actions. BACCER's efficacy to model examples of tree-structures and other models is demonstrated herein.

2021-03-01
Said, S., Bouloiz, H., Gallab, M..  2020.  Identification and Assessment of Risks Affecting Sociotechnical Systems Resilience. 2020 IEEE 6th International Conference on Optimization and Applications (ICOA). :1–10.
Resilience is regarded nowadays as the ideal solution that can be envisaged by sociotechnical systems for coping with potential threats and crises. This being said, gaining and maintaining this ability is not always easy, given the multitude of risks driving the adverse and challenging events. This paper aims to propose a method consecrated to the assessment of risks directly affecting resilience. This work is conducted within the framework of risk assessment and resilience engineering approaches. A 5×5 matrix, dedicated to the identification and assessment of risk factors that constitute threats to the system resilience, has been elaborated. This matrix consists of two axes, namely, the impact on resilience metrics and the availability and effectiveness of resilience planning. Checklists serving to collect information about these two attributes are established and a case study is undertaken. In this paper, a new method for identifying and assessing risk factors menacing directly the resilience of a given system is presented. The analysis of these risks must be given priority to make the system more resilient to shocks.
2021-02-22
Alzahrani, A., Feki, J..  2020.  Toward a Natural Language-Based Approach for the Specification of Decisional-Users Requirements. 2020 3rd International Conference on Computer Applications Information Security (ICCAIS). :1–6.
The number of organizations adopting the Data Warehouse (DW) technology along with data analytics in order to improve the effectiveness of their decision-making processes is permanently increasing. Despite the efforts invested, the DW design remains a great challenge research domain. More accurately, the design quality of the DW depends on several aspects; among them, the requirement-gathering phase is a critical and complex task. In this context, we propose a Natural language (NL) NL-template based design approach, which is twofold; firstly, it facilitates the involvement of decision-makers in the early step of the DW design; indeed, using NL is a good and natural means to encourage the decision-makers to express their requirements as query-like English sentences. Secondly, our approach aims to generate a DW multidimensional schema from a set of gathered requirements (as OLAP: On-Line-Analytical-Processing queries, written according to the NL suggested templates). This approach articulates around: (i) two NL-templates for specifying multidimensional components, and (ii) a set of five heuristic rules for extracting the multidimensional concepts from requirements. Really, we are developing a software prototype that accepts the decision-makers' requirements then automatically identifies the multidimensional components of the DW model.
2021-02-08
Nikouei, S. Y., Chen, Y., Faughnan, T. R..  2018.  Smart Surveillance as an Edge Service for Real-Time Human Detection and Tracking. 2018 IEEE/ACM Symposium on Edge Computing (SEC). :336—337.

Monitoring for security and well-being in highly populated areas is a critical issue for city administrators, policy makers and urban planners. As an essential part of many dynamic and critical data-driven tasks, situational awareness (SAW) provides decision-makers a deeper insight of the meaning of urban surveillance. Thus, surveillance measures are increasingly needed. However, traditional surveillance platforms are not scalable when more cameras are added to the network. In this work, a smart surveillance as an edge service has been proposed. To accomplish the object detection, identification, and tracking tasks at the edge-fog layers, two novel lightweight algorithms are proposed for detection and tracking respectively. A prototype has been built to validate the feasibility of the idea, and the test results are very encouraging.

Liu, S., Kosuru, R., Mugombozi, C. F..  2020.  A Moving Target Approach for Securing Secondary Frequency Control in Microgrids. 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). :1–6.
Microgrids' dependency on communication links exposes the control systems to cyber attack threats. In this work, instead of designing reactive defense approaches, a proacitve moving target defense mechanism is proposed for securing microgrid secondary frequency control from denial of service (DoS) attack. The sensor data is transmitted by following a Markov process, not in a deterministic way. This uncertainty will increase the difficulty for attacker's decision making and thus significantly reduce the attack space. As the system parameters are constantly changing, a gain scheduling based secondary frequency controller is designed to sustain the system performance. Case studies of a microgrid with four inverter-based DGs show the proposed moving target mechanism can enhance the resiliency of the microgrid control systems against DoS attacks.
2021-02-03
Xu, J., Howard, A..  2020.  How much do you Trust your Self-Driving Car? Exploring Human-Robot Trust in High-Risk Scenarios 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :4273—4280.

Trust is an important characteristic of successful interactions between humans and agents in many scenarios. Self-driving scenarios are of particular relevance when discussing the issue of trust due to the high-risk nature of erroneous decisions being made. The present study aims to investigate decision-making and aspects of trust in a realistic driving scenario in which an autonomous agent provides guidance to humans. To this end, a simulated driving environment based on a college campus was developed and presented. An online and an in-person experiment were conducted to examine the impacts of mistakes made by the self-driving AI agent on participants’ decisions and trust. During the experiments, participants were asked to complete a series of driving tasks and make a sequence of decisions in a time-limited situation. Behavior analysis indicated a similar relative trend in the decisions across these two experiments. Survey results revealed that a mistake made by the self-driving AI agent at the beginning had a significant impact on participants’ trust. In addition, similar overall experience and feelings across the two experimental conditions were reported. The findings in this study add to our understanding of trust in human-robot interaction scenarios and provide valuable insights for future research work in the field of human-robot trust.

Xu, J., Howard, A..  2020.  Would you Take Advice from a Robot? Developing a Framework for Inferring Human-Robot Trust in Time-Sensitive Scenarios 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :814—820.

Trust is a key element for successful human-robot interaction. One challenging problem in this domain is the issue of how to construct a formulation that optimally models this trust phenomenon. This paper presents a framework for modeling human-robot trust based on representing the human decision-making process as a formulation based on trust states. Using this formulation, we then discuss a generalized model of human-robot trust based on Hidden Markov Models and Logistic Regression. The proposed approach is validated on datasets collected from two different human subject studies in which the human is provided the ability to take advice from a robot. Both experimental scenarios were time-sensitive, in that a decision had to be made by the human in a limited time period, but each scenario featured different levels of cognitive load. The experimental results demonstrate that the proposed formulation can be utilized to model trust, in which the system can predict whether the human will decide to take advice (or not) from the robot. It was found that our prediction performance degrades after the robot made a mistake. The validation of this approach on two scenarios implies that this model can be applied to other interactive scenarios as long as the interaction dynamics fits into the proposed formulation. Directions for future improvements are discussed.

He, S., Lei, D., Shuang, W., Liu, C., Gu, Z..  2020.  Network Security Analysis of Industrial Control System Based on Attack-Defense Tree. 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS). :651—655.
In order to cope with the network attack of industrial control system, this paper proposes a quantifiable attack-defense tree model. In order to reduce the influence of subjective factors on weight calculation and the probability of attack events, the Fuzzy Analytic Hierarchy Process and the Attack-Defense Tree model are combined. First, the model provides a variety of security attributes for attack and defense leaf nodes. Secondly, combining the characteristics of leaf nodes, a fuzzy consistency matrix is constructed to calculate the security attribute weight of leaf nodes, and the probability of attack and defense leaf nodes. Then, the influence of defense node on attack behavior is analyzed. Finally, the network risk assessment of typical airport oil supply automatic control system has been undertaken as a case study using this attack-defense tree model. The result shows that this model can truly reflect the impact of defense measures on the attack behavior, and provide a reference for the network security scheme.
2021-02-01
Hou, M..  2020.  IMPACT: A Trust Model for Human-Agent Teaming. 2020 IEEE International Conference on Human-Machine Systems (ICHMS). :1–4.
A trust model IMPACT: Intention, Measurability, Predictability, Agility, Communication, and Transparency has been conceptualized to build human trust in autonomous agents. The six critical characteristics must be exhibited by the agents in order to gain and maintain the trust from their human partners towards an effective and collaborative team in achieving common goals. The IMPACT model guided a design of an intelligent adaptive decision aid for dynamic target engagement processes in a military context. Positive feedback from subject matter experts participated in a large scale joint exercise controlling multiple unmanned vehicles indicated the effectiveness of the decision aid. It also demonstrated the utility of the IMPACT model as design principles for building up a trusted human-agent teaming.
2021-01-25
Malzahn, D., Birnbaum, Z., Wright-Hamor, C..  2020.  Automated Vulnerability Testing via Executable Attack Graphs. 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–10.
Cyber risk assessments are an essential process for analyzing and prioritizing security issues. Unfortunately, many risk assessment methodologies are marred by human subjectivity, resulting in non-repeatable, inconsistent findings. The absence of repeatable and consistent results can lead to suboptimal decision making with respect to cyber risk reduction. There is a pressing need to reduce cyber risk assessment uncertainty by using tools that use well defined inputs, producing well defined results. This paper presents Automated Vulnerability and Risk Analysis (AVRA), an end-to-end process and tool for identifying and exploiting vulnerabilities, designed for use in cyber risk assessments. The approach presented is more comprehensive than traditional vulnerability scans due to its analysis of an entire network, integrating both host and network information. AVRA automatically generates a detailed model of the network and its individual components, which is used to create an attack graph. Then, AVRA follows individual attack paths, automatically launching exploits to reach a particular objective. AVRA was successfully tested within a virtual environment to demonstrate practicality and usability. The presented approach and resulting system enhances the cyber risk assessment process through rigor, repeatability, and objectivity.
2021-01-22
Akbari, I., Tahoun, E., Salahuddin, M. A., Limam, N., Boutaba, R..  2020.  ATMoS: Autonomous Threat Mitigation in SDN using Reinforcement Learning. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1—9.
Machine Learning has revolutionized many fields of computer science. Reinforcement Learning (RL), in particular, stands out as a solution to sequential decision making problems. With the growing complexity of computer networks in the face of new emerging technologies, such as the Internet of Things and the growing complexity of threat vectors, there is a dire need for autonomous network systems. RL is a viable solution for achieving this autonomy. Software-defined Networking (SDN) provides a global network view and programmability of network behaviour, which can be employed for security management. Previous works in RL-based threat mitigation have mostly focused on very specific problems, mostly non-sequential, with ad-hoc solutions. In this paper, we propose ATMoS, a general framework designed to facilitate the rapid design of RL applications for network security management using SDN. We evaluate our framework for implementing RL applications for threat mitigation, by showcasing the use of ATMoS with a Neural Fitted Q-learning agent to mitigate an Advanced Persistent Threat. We present the RL model's convergence results showing the feasibility of our solution for active threat mitigation.
2020-12-17
Amrouche, F., Lagraa, S., Frank, R., State, R..  2020.  Intrusion detection on robot cameras using spatio-temporal autoencoders: A self-driving car application. 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). :1—5.

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing security breaches do not have robust solutions. In this paper we focus on the camera vulnerabilities, as it is often the most important source for the environment discovery and the decision-making process. We propose an unsupervised anomaly detection tool for detecting suspicious frames incoming from camera flows. Our solution is based on spatio-temporal autoencoders used to truthfully reconstruct the camera frames and detect abnormal ones by measuring the difference with the input. We test our approach on a real-word dataset, i.e. flows coming from embedded cameras of self-driving cars. Our solution outperforms the existing works on different scenarios.

2020-12-14
Willcox, G., Rosenberg, L., Domnauer, C..  2020.  Analysis of Human Behaviors in Real-Time Swarms. 2020 10th Annual Computing and Communication Workshop and Conference (CCWC). :0104–0109.
Many species reach group decisions by deliberating in real-time systems. This natural process, known as Swarm Intelligence (SI), has been studied extensively in a range of social organisms, from schools of fish to swarms of bees. A new technique called Artificial Swarm Intelligence (ASI) has enabled networked human groups to reach decisions in systems modeled after natural swarms. The present research seeks to understand the behavioral dynamics of such “human swarms.” Data was collected from ten human groups, each having between 21 and 25 members. The groups were tasked with answering a set of 25 ordered ranking questions on a 1-5 scale, first independently by survey and then collaboratively as a real-time swarm. We found that groups reached significantly different answers, on average, by swarm versus survey ( p=0.02). Initially, the distribution of individual responses in each swarm was little different than the distribution of survey responses, but through the process of real-time deliberation, the swarm's average answer changed significantly ( ). We discuss possible interpretations of this dynamic behavior. Importantly, the we find that swarm's answer is not simply the arithmetic mean of initial individual “votes” ( ) as in a survey, suggesting a more complex mechanism is at play-one that relies on the time-varying behaviors of the participants in swarms. Finally, we publish a set of data that enables other researchers to analyze human behaviors in real-time swarms.