Biblio

Filters: Keyword is Decision support systems  [Clear All Filters]
2021-02-01
Ng, M., Coopamootoo, K. P. L., Toreini, E., Aitken, M., Elliot, K., Moorsel, A. van.  2020.  Simulating the Effects of Social Presence on Trust, Privacy Concerns Usage Intentions in Automated Bots for Finance. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :190–199.
FinBots are chatbots built on automated decision technology, aimed to facilitate accessible banking and to support customers in making financial decisions. Chatbots are increasing in prevalence, sometimes even equipped to mimic human social rules, expectations and norms, decreasing the necessity for human-to-human interaction. As banks and financial advisory platforms move towards creating bots that enhance the current state of consumer trust and adoption rates, we investigated the effects of chatbot vignettes with and without socio-emotional features on intention to use the chatbot for financial support purposes. We conducted a between-subject online experiment with N = 410 participants. Participants in the control group were provided with a vignette describing a secure and reliable chatbot called XRO23, whereas participants in the experimental group were presented with a vignette describing a secure and reliable chatbot that is more human-like and named Emma. We found that Vignette Emma did not increase participants' trust levels nor lowered their privacy concerns even though it increased perception of social presence. However, we found that intention to use the presented chatbot for financial support was positively influenced by perceived humanness and trust in the bot. Participants were also more willing to share financially-sensitive information such as account number, sort code and payments information to XRO23 compared to Emma - revealing a preference for a technical and mechanical FinBot in information sharing. Overall, this research contributes to our understanding of the intention to use chatbots with different features as financial technology, in particular that socio-emotional support may not be favoured when designed independently of financial function.
2021-04-09
Song, M., Lind, M..  2020.  Towards Automated Generation of Function Models from P IDs. 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 1:1081—1084.
Although function model has been widely applied to develop various operator decision support systems, the modeling process is essentially a manual work, which takes significant efforts on knowledge acquisition. It would greatly improve the efficiency of modeling if relevant information can be automatically retrieved from engineering documents. This paper investigates the possibility of automated transformation from P&IDs to a function model called MFM via AutomationML. Semantics and modeling patterns of MFM are established in AutomationML, which can be utilized to convert plant topology models into MFM models. The proposed approach is demonstrated with a small use case. Further topics for extending the study are also discussed.
2020-11-02
Fedosova, Tatyana V., Masych, Marina A., Afanasvev, Anton A., Liabakh, Nikolay N..  2019.  Development of a Decision Support System for Intellectual Property Utilization. 2019 International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT QM IS). :482—485.
This paper outlines the concept of intellectual property utilization and develops a framework for the targeted generation of intellectual property for the benefit of various economic entities. The study proposes two types of the decision support system: (i) based on deterministic logic, and (ii) based on multi-agent systems. The results of the study offer the development of a mathematical approach to the interaction process of agents in multi-agent systems, inter alia related to the targeted generation of intellectual property.
2020-03-23
Bothe, Alexander, Bauer, Jan, Aschenbruck, Nils.  2019.  RFID-assisted Continuous User Authentication for IoT-based Smart Farming. 2019 IEEE International Conference on RFID Technology and Applications (RFID-TA). :505–510.
Smart Farming is driven by the emergence of precise positioning systems and Internet of Things technologies which have already enabled site-specific applications, sustainable resource management, and interconnected machinery. Nowadays, so-called Farm Management Information Systems (FMISs) enable farm-internal interconnection of agricultural machines and implements and, thereby, allow in-field data exchange and the orchestration of collaborative agricultural processes. Machine data is often directly logged during task execution. Moreover, interconnection of farms, agricultural contractors, and marketplaces ease the collaboration. However, current FMISs lack in security and particularly in user authentication. In this paper, we present a security architecture for a decentralized, manufacturer-independent, and open-source FMIS. Special attention is turned on the Radio Frequency Identification (RFID)-based continuous user authentication which greatly improves security and credibility of automated documentation, while at the same time preserves usability in practice.
2020-10-12
Jharko, Elena, Promyslov, Vitaly, Iskhakov, Andrey.  2019.  Extending Functionality of Early Fault Diagnostic System for Online Security Assessment of Nuclear Power Plant. 2019 International Russian Automation Conference (RusAutoCon). :1–6.

The new instrumentation and control (I&C) systems of the nuclear power plants (NPPs) improve the ability to operate the plant enhance the safety and performance of the NPP. However, they bring a new type of threat to the NPP's industry-cyber threat. The early fault diagnostic system (EDS) is one of the decision support systems that might be used online during the operation stage. The EDS aim is to prevent the incident/accident evolution by a timely troubleshooting process during any plant operational modes. It means that any significative deviation of plant parameters from normal values is pointed-out to plant operators well before reaching any undesired threshold potentially leading to a prohibited plant state, together with the cause that has generated the deviation. The paper lists the key benefits using the EDS to counter the cyber threat and proposes the framework for cybersecurity assessment using EDS during the operational stage.

2020-03-02
Sahu, Abhijeet, Huang, Hao, Davis, Katherine, Zonouz, Saman.  2019.  SCORE: A Security-Oriented Cyber-Physical Optimal Response Engine. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1–6.

Automatic optimal response systems are essential for preserving power system resilience and ensuring faster recovery from emergency under cyber compromise. Numerous research works have developed such response engine for cyber and physical system recovery separately. In this paper, we propose a novel cyber-physical decision support system, SCORE, that computes optimal actions considering pure and hybrid cyber-physical states, using Markov Decision Process (MDP). Such an automatic decision making engine can assist power system operators and network administrators to make a faster response to prevent cascading failures and attack escalation respectively. The hybrid nature of the engine makes the reward and state transition model of the MDP unique. Value iteration and policy iteration techniques are used to compute the optimal actions. Tests are performed on three and five substation power systems to recover from attacks that compromise relays to cause transmission line overflow. The paper also analyses the impact of reward and state transition model on computation. Corresponding results verify the efficacy of the proposed engine.

2020-04-13
Horne, Benjamin D., Gruppi, Mauricio, Adali, Sibel.  2019.  Trustworthy Misinformation Mitigation with Soft Information Nudging. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :245–254.

Research in combating misinformation reports many negative results: facts may not change minds, especially if they come from sources that are not trusted. Individuals can disregard and justify lies told by trusted sources. This problem is made even worse by social recommendation algorithms which help amplify conspiracy theories and information confirming one's own biases due to companies' efforts to optimize for clicks and watch time over individuals' own values and public good. As a result, more nuanced voices and facts are drowned out by a continuous erosion of trust in better information sources. Most misinformation mitigation techniques assume that discrediting, filtering, or demoting low veracity information will help news consumers make better information decisions. However, these negative results indicate that some news consumers, particularly extreme or conspiracy news consumers will not be helped. We argue that, given this background, technology solutions to combating misinformation should not simply seek facts or discredit bad news sources, but instead use more subtle nudges towards better information consumption. Repeated exposure to such nudges can help promote trust in better information sources and also improve societal outcomes in the long run. In this article, we will talk about technological solutions that can help us in developing such an approach, and introduce one such model called Trust Nudging.

2020-07-16
Balduccini, Marcello, Griffor, Edward, Huth, Michael, Vishik, Claire, Wollman, David, Kamongi, Patrick.  2019.  Decision Support for Smart Grid: Using Reasoning to Contextualize Complex Decision Making. 2019 7th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES). :1—6.

The smart grid is a complex cyber-physical system (CPS) that poses challenges related to scale, integration, interoperability, processes, governance, and human elements. The US National Institute of Standards and Technology (NIST) and its government, university and industry collaborators, developed an approach, called CPS Framework, to reasoning about CPS across multiple levels of concern and competency, including trustworthiness, privacy, reliability, and regulatory. The approach uses ontology and reasoning techniques to achieve a greater understanding of the interdependencies among the elements of the CPS Framework model applied to use cases. This paper demonstrates that the approach extends naturally to automated and manual decision-making for smart grids: we apply it to smart grid use cases, and illustrate how it can be used to analyze grid topologies and address concerns about the smart grid. Smart grid stakeholders, whose decision making may be assisted by this approach, include planners, designers and operators.

2020-02-17
Prajanti, Anisa Dewi, Ramli, Kalamullah.  2019.  A Proposed Framework for Ranking Critical Information Assets in Information Security Risk Assessment Using the OCTAVE Allegro Method with Decision Support System Methods. 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). :1–4.
The security of an organization lies not only in physical buildings, but also in its information assets. Safeguarding information assets requires further study to establish optimal security mitigation steps. In determining the appropriate mitigation of information assets, both an information security risk assessment and a clear and measurable rating are required. Most risk management methods do not provide the right focus on ranking the critical information assets of an organization. This paper proposes a framework approach for ranking critical information assets. The proposed framework uses the OCTAVE Allegro method, which focuses on profiling information assets by combining ranking priority measurements using decision support system methods, such as Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP). The combined OCTAVE Allegro-SAW and OCTAVE Allegro-AHP methods are expected to better address risk priority as an input to making mitigation decisions for critical information assets. These combinations will help management to avoid missteps in adjusting budget needs allocation or time duration by selecting asset information mitigation using the ranking results of the framework.
2020-01-27
Salamai, Abdullah, Hussain, Omar, Saberi, Morteza.  2019.  Decision Support System for Risk Assessment Using Fuzzy Inference in Supply Chain Big Data. 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD IS). :248–253.

Currently, organisations find it difficult to design a Decision Support System (DSS) that can predict various operational risks, such as financial and quality issues, with operational risks responsible for significant economic losses and damage to an organisation's reputation in the market. This paper proposes a new DSS for risk assessment, called the Fuzzy Inference DSS (FIDSS) mechanism, which uses fuzzy inference methods based on an organisation's big data collection. It includes the Emerging Association Patterns (EAP) technique that identifies the important features of each risk event. Then, the Mamdani fuzzy inference technique and several membership functions are evaluated using the firm's data sources. The FIDSS mechanism can enhance an organisation's decision-making processes by quantifying the severity of a risk as low, medium or high. When it automatically predicts a medium or high level, it assists organisations in taking further actions that reduce this severity level.

2019-02-08
Isaacson, D. M..  2018.  The ODNI-OUSD(I) Xpress Challenge: An Experimental Application of Artificial Intelligence Techniques to National Security Decision Support. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). :104-109.
Current methods for producing and disseminating analytic products contribute to the latency of relaying actionable information and analysis to the U.S. Intelligence Community's (IC's) principal customers, U.S. policymakers and warfighters. To circumvent these methods, which can often serve as a bottleneck, we report on the results of a public prize challenge that explored the potential for artificial intelligence techniques to generate useful analytic products. The challenge tasked solvers to develop algorithms capable of searching and processing nearly 15,000 unstructured text files into a 1-2 page analytic product without human intervention; these analytic products were subsequently evaluated and scored using established IC methodologies and criteria. Experimental results from this challenge demonstrate the promise for the ma-chine generation of analytic products to ensure that the IC warns and informs in a more timely fashion.
2018-12-03
Matta, R. de, Miller, T..  2018.  A Strategic Manufacturing Capacity and Supply Chain Network Design Contingency Planning Approach. 2018 IEEE Technology and Engineering Management Conference (TEMSCON). :1–6.

We develop a contingency planning methodology for how a firm would build a global supply chain network with reserve manufacturing capacity which can be strategically deployed by the firm in the event actual demand exceeds forecast. The contingency planning approach is comprised of: (1) a strategic network design model for finding the profit maximizing plant locations, manufacturing capacity and inventory investments, and production level and product distribution; and (2) a scenario planning and risk assessment scheme to analyze the costs and benefits of alternative levels of manufacturing capacity and inventory investments. We develop an efficient heuristic procedure to solve the model. We show numerically how a firm would use our approach to explore and weigh the potential upside benefits and downside risks of alternative strategies.

2018-10-26
Toliupa, S., Babenko, T., Trush, A..  2017.  The building of a security strategy based on the model of game management. 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S T). :57–60.

Cyber security management of systems in the cyberspace has been a challenging problem for both practitioners and the research community. Their proprietary nature along with the complexity renders traditional approaches rather insufficient and creating the need for the adoption of a holistic point of view. This paper draws upon the principles theory game in order to present a novel systemic approach towards cyber security management, taking into account the complex inter-dependencies and providing cost-efficient defense solutions.

2017-12-12
Reinerman-Jones, L., Matthews, G., Wohleber, R., Ortiz, E..  2017.  Scenarios using situation awareness in a simulation environment for eliciting insider threat behavior. 2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). :1–3.

An important topic in cybersecurity is validating Active Indicators (AI), which are stimuli that can be implemented in systems to trigger responses from individuals who might or might not be Insider Threats (ITs). The way in which a person responds to the AI is being validated for identifying a potential threat and a non-threat. In order to execute this validation process, it is important to create a paradigm that allows manipulation of AIs for measuring response. The scenarios are posed in a manner that require participants to be situationally aware that they are being monitored and have to act deceptively. In particular, manipulations in the environment should no differences between conditions relative to immersion and ease of use, but the narrative should be the driving force behind non-deceptive and IT responses. The success of the narrative and the simulation environment to induce such behaviors is determined by immersion, usability, and stress response questionnaires, and performance. Initial results of the feasibility to use a narrative reliant upon situation awareness of monitoring and evasion are discussed.

2018-01-23
Nakhla, N., Perrett, K., McKenzie, C..  2017.  Automated computer network defence using ARMOUR: Mission-oriented decision support and vulnerability mitigation. 2017 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). :1–8.

Mission assurance requires effective, near-real time defensive cyber operations to appropriately respond to cyber attacks, without having a significant impact on operations. The ability to rapidly compute, prioritize and execute network-based courses of action (CoAs) relies on accurate situational awareness and mission-context information. Although diverse solutions exist for automatically collecting and analysing infrastructure data, few deliver automated analysis and implementation of network-based CoAs in the context of the ongoing mission. In addition, such processes can be operatorintensive and available tools tend to be specific to a set of common data sources and network responses. To address these issues, Defence Research and Development Canada (DRDC) is leading the development of the Automated Computer Network Defence (ARMOUR) technology demonstrator and cyber defence science and technology (S&T) platform. ARMOUR integrates new and existing off-the-shelf capabilities to provide enhanced decision support and to automate many of the tasks currently executed manually by network operators. This paper describes the cyber defence integration framework, situational awareness, and automated mission-oriented decision support that ARMOUR provides.

2017-11-01
De Sutter, Bjorn, Basile, Cataldo, Ceccato, Mariano, Falcarin, Paolo, Zunke, Michael, Wyseur, Brecht, d'Annoville, Jerome.  2016.  The ASPIRE Framework for Software Protection. Proceedings of the 2016 ACM Workshop on Software PROtection. :91–92.
In the ASPIRE research project, a software protection tool flow was designed and prototyped that targets native ARM Android code. This tool flow supports the deployment of a number of protections against man-at-the-end attacks. In this tutorial, an overview of the tool flow will be presented and attendants will participate to a hands-on demonstration. In addition, we will present an overview of the decision support systems developed in the project to facilitate the use of the protection tool flow.
2017-09-19
Plachkov, Alex, Abielmona, Rami, Harb, Moufid, Falcon, Rafael, Inkpen, Diana, Groza, Voicu, Petriu, Emil.  2016.  Automatic Course of Action Generation Using Soft Data for Maritime Domain Awareness. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. :1071–1078.

Information Fusion (IF) systems have long exploited data provided by hard (physics-based) sensors with the aspiration of making sense of the environment they are monitoring. In recent times, the IF community has recognized the potential of utilizing data generated by people, also known as soft data. In this study, we demonstrate how course of action (CoA) generation, one of the key elements of Level 3 High-Level Information Fusion and a vital component for security and defense decision support systems, can be augmented using soft (human-derived) data for improved mission effectiveness. This conceptualization is validated through an elaborate experiment situated in the maritime world. To the best of the authors' knowledge, this is the first study to apply soft data to automatic CoA generation in the maritime domain.

2018-05-15
2017-12-04
Idayanti, N., Dedi, Nanang, T. K., Sudrajat, Septiani, A., Mulyadi, D., Irasari, P..  2016.  The implementation of hybrid bonded permanent magnet on permanent magnet generator for renewable energy power plants. 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA). :557–560.

{This paper describes application of permanent magnet on permanent magnet generator (PMG) for renewable energy power plants. Permanent magnet used are bonded hybrid magnet that was a mixture of barium ferrite magnetic powders 50 wt % and NdFeB magnetic powders 50 wt % with 15 wt % of adhesive polymer as a binder. Preparation of bonded hybrid magnets by hot press method at a pressure of 2 tons and temperature of 200°C for 15 minutes. The magnetic properties obtained were remanence induction (Br) =1.54 kG, coercivity (Hc) = 1.290 kOe, product energy maximum (BHmax) = 0.28 MGOe, surface remanence induction (Br) = 1200 gauss

2017-11-27
Chopade, P., Zhan, J., Bikdash, M..  2016.  Micro-Community detection and vulnerability identification for large critical networks. 2016 IEEE Symposium on Technologies for Homeland Security (HST). :1–7.

In this work we put forward our novel approach using graph partitioning and Micro-Community detection techniques. We firstly use algebraic connectivity or Fiedler Eigenvector and spectral partitioning for community detection. We then used modularity maximization and micro level clustering for detecting micro-communities with concept of community energy. We run micro-community clustering algorithm recursively with modularity maximization which helps us identify dense, deeper and hidden community structures. We experimented our MicroCommunity Clustering (MCC) algorithm for various types of complex technological and social community networks such as directed weighted, directed unweighted, undirected weighted, undirected unweighted. A novel fact about this algorithm is that it is scalable in nature.

2017-04-20
Chaudhary, P., Gupta, B. B., Yamaguchi, S..  2016.  XSS detection with automatic view isolation on online social network. 2016 IEEE 5th Global Conference on Consumer Electronics. :1–5.

Online Social Networks (OSNs) are continuously suffering from the negative impact of Cross-Site Scripting (XSS) vulnerabilities. This paper describes a novel framework for mitigating XSS attack on OSN-based platforms. It is completely based on the request authentication and view isolation approach. It detects XSS attack through validating string value extracted from the vulnerable checkpoint present in the web page by implementing string examination algorithm with the help of XSS attack vector repository. Any similarity (i.e. string is not validated) indicates the presence of malicious code injected by the attacker and finally it removes the script code to mitigate XSS attack. To assess the defending ability of our designed model, we have tested it on OSN-based web application i.e. Humhub. The experimental results revealed that our model discovers the XSS attack vectors with low false negatives and false positive rate tolerable performance overhead.

2017-03-08
Guo, Q., Fan, J., Li, N..  2015.  The achieve of power manager application honey-pot based on sandbox. 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT). :2523–2527.

Honeypot is a common method of attack capture, can maximize the reduction of cyber-attacks. However, its limited application layer simulation makes it impossible to use effectively in power system. Through research on sandboxing technology, this article implements the simulated power manager applications by packaging real power manager applications, in order to expand the honeypot applied range.

Jalili, A., Ahmadi, V., Keshtgari, M., Kazemi, M..  2015.  Controller placement in software-defined WAN using multi objective genetic algorithm. 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI). :656–662.

SDN is a promising architecture that can overcome the challenges facing traditional networks. SDN enables administrator/operator to build a simpler, customizable, programmable, and manageable network. In software-defined WAN deployments, multiple controllers are often needed, and the location of these controllers affect various metrics. Since these metrics conflict each other, this problem can be regarded as a multi-objective combinatorial optimization problem (MOCO). A particular efficient method to solve a typical MOCO, which is used in the relevant literature, is to find the actual Pareto frontier first and give it to the decision maker to select the most appropriate solution(s). In small and medium sized combinatorial problems, evaluating the whole search space and find the exact Pareto frontier may be possible in a reasonable time. However, for large scale problems whose search spaces involves thousands of millions of solutions, the exhaustive evaluation needs a considerable amount of computational efforts and memory used. An effective alternative mechanism is to estimate the original Pareto frontier within a relatively small algorithm's runtime and memory consumption. Heuristic methods, which have been studied well in the literature, proved to be very effective methods in this regards. The second version of the Non-dominated Sorting Genetic Algorithm, called NSGA-II has been carried out quite well on different discrete and continuous optimization problems. In this paper, we adapt this efficient mechanism for a new presented multi-objective model of the control placement problem [7]. The results of implementing the adapted algorithm carried out on the Internet2 OS3E network run on MATLAB 2013b confirmed its effectiveness.

Cao, B., Wang, Z., Shi, H., Yin, Y..  2015.  Research and practice on Aluminum Industry 4.0. 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP). :517–521.

This paper presents a six-layer Aluminum Industry 4.0 architecture for the aluminum production and full lifecycle supply chain management. It integrates a series of innovative technologies, including the IoT sensing physical system, industrial cloud platform for data management, model-driven and big data driven analysis & decision making, standardization & securitization intelligent control and management, as well as visual monitoring and backtracking process etc. The main relevant control models are studied. The applications of real-time accurate perception & intelligent decision technology in the aluminum electrolytic industry are introduced.

2017-02-27
Li, X., He, Z., Zhang, S..  2015.  Robust optimization of risk for power system based on information gap decision theory. 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT). :200–204.

Risk-control optimization has great significance for security of power system. Usually the probabilistic uncertainties of parameters are considered in the research of risk optimization of power system. However, the method of probabilistic uncertainty description will be insufficient in the case of lack of sample data. Thus non-probabilistic uncertainties of parameters should be considered, and will impose a significant influence on the results of optimization. To solve this problem, a robust optimization operation method of power system risk-control is presented in this paper, considering the non-probabilistic uncertainty of parameters based on information gap decision theory (IGDT). In the method, loads are modeled as the non-probabilistic uncertainty parameters, and the model of robust optimization operation of risk-control is presented. By solving the model, the maximum fluctuation of the pre-specified target can be obtained, and the strategy of this situation can be obtained at the same time. The proposed model is applied to the IEEE-30 system of risk-control by simulation. The results can provide the valuable information for operating department to risk management.