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AbuKhousa, E., El-Tahawy, M. S. & Atif, Y. (2023). Envisioning an Architecture of Metaverse Intensive Learning Experience (MiLEx): Career Readiness in the 21st Century and Collective Intelligence Development Scenario. Future Internet, 15(2), Article ID 53.
Open this publication in new window or tab >>Envisioning an Architecture of Metaverse Intensive Learning Experience (MiLEx): Career Readiness in the 21st Century and Collective Intelligence Development Scenario
2023 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 15, no 2, article id 53Article in journal (Refereed) Published
Abstract [en]

The metaverse presents a new opportunity to construct personalized learning paths and to promote practices that scale the development of future skills and collective intelligence. The attitudes, knowledge and skills that are necessary to face the challenges of the 21st century should be developed through iterative cycles of continuous learning, where learners are enabled to experience, reflect, and produce new ideas while participating in a collective creativity process. In this paper, we propose an architecture to develop a metaverse-intensive learning experience (MiLEx) platform with an illustrative scenario that reinforces the development of 21st century career practices and collective intelligence. The learning ecosystem of MiLEx integrates four key elements: (1) key players that define the main actors and their roles in the learning process; (2) a learning context that defines the learning space and the networks of expected interactions among human and non-human objects; (3) experiential learning instances that deliver education via a real-life–virtual merge; and (4) technology support for building practice communities online, developing experiential cycles and transforming knowledge between human and non-human objects within the community. The proposed MiLEx architecture incorporates sets of technological and data components to (1) discover/profile learners and design learner-centric, theoretically grounded and immersive learning experiences; (2) create elements and experiential learning scenarios; (3) analyze learner’s interactive and behavioral patterns; (4) support the emergence of collective intelligence; (5) assess learning outcomes and monitor the learner’s maturity process; and (6) evaluate experienced learning and recommend future experiences. We also present the MiLEx continuum as a cyclic flow of information to promote immersive learning. Finally, we discuss some open issues to increase the learning value and propose some future work suggestions to further shape the transformative potential of metaverse-based learning environments.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
metaverse, metaverse for higher education, ecosystem, metaverse platform architecture, experiential learning cycle, career readiness, 21st century skills, immersive learning experience, community of practice, collective intelligence, instructional design, learning technology
National Category
Computer and Information Sciences
Research subject
Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-22237 (URN)10.3390/fi15020053 (DOI)000938716800001 ()2-s2.0-85148913411 (Scopus ID)
Note

CC BY 4.0

(This article belongs to the Special Issue Software Engineering and Data Science II)

Published: 30 January 2023

Available from: 2023-02-03 Created: 2023-02-03 Last updated: 2023-08-03Bibliographically approved
Jiang, Y. & Atif, Y. (2022). Towards automatic discovery and assessment of vulnerability severity in cyber-physical systems. Array, 15, Article ID 100209.
Open this publication in new window or tab >>Towards automatic discovery and assessment of vulnerability severity in cyber-physical systems
2022 (English)In: Array, ISSN 2590-0056, Vol. 15, article id 100209Article in journal (Refereed) Published
Abstract [en]

Despite their wide proliferation, complex cyber–physical systems (CPSs) are subject to cybersecurity vulnerabilities and potential attacks. Vulnerability assessment for such complex systems are challenging, partly due to the discrepancy among mechanisms used to evaluate their cyber-security weakness levels. Several sources do report these weaknesses like the National Vulnerability Database (NVD), as well as manufacturer websites besides other security scanning advisories such as Cyber Emergency Response Team (CERT) and Shodan databases. However, these multiple sources are found to face inconsistency issues, especially in terms of vulnerability severity scores. We advocate an artificial intelligence based approach to streamline the computation of vulnerability severity magnitudes. This approach decreases the error rate induced by manual calculation processes, that are traditionally used in cybersecurity analysis. Popular repositories such as NVD and SecurityFocus are employed to validate the proposed approach, assisted with a query method to retrieve vulnerability instances. In doing so, we report discovered correlations among reported vulnerability scores to infer consistent magnitude values of vulnerability instances. The method is applied to a case study featuring a CPS application to illustrate the automation of the proposed vulnerability scoring mechanism, used to mitigate cybersecurity weaknesses.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Cybersecurity, Text-mining, Cyber-physical system, Vulnerability analysis, CVSS
National Category
Computer Engineering
Research subject
Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-21409 (URN)10.1016/j.array.2022.100209 (DOI)2-s2.0-85133584882 (Scopus ID)
Note

CC BY 4.0

This research has been supported in part by EU ISF (Internal Security Fund) in the context of Project Grant #A431.678/2016.

Available from: 2022-06-27 Created: 2022-06-27 Last updated: 2022-07-21Bibliographically approved
Jiang, Y. & Atif, Y. (2021). A selective ensemble model for cognitive cybersecurity analysis. Journal of Network and Computer Applications, 193, Article ID 103210.
Open this publication in new window or tab >>A selective ensemble model for cognitive cybersecurity analysis
2021 (English)In: Journal of Network and Computer Applications, ISSN 1084-8045, E-ISSN 1095-8592, Vol. 193, article id 103210Article in journal (Refereed) Published
Abstract [en]

Dynamic data-driven vulnerability assessments face massive heterogeneous data contained in, and produced by SOCs (Security Operations Centres). Manual vulnerability assessment practices result in inaccurate data and induce complex analytical reasoning. Contemporary security repositories’ diversity, incompleteness and redundancy contribute to such security concerns. These issues are typical characteristics of public and manufacturer vulnerability reports, which exacerbate direct analysis to root out security deficiencies. Recent advances in machine learning techniques promise novel approaches to overcome these notorious diversity and incompleteness issues across massively increasing vulnerability reports corpora. Yet, these techniques themselves exhibit varying degrees of performance as a result of their diverse methods. We propose a cognitive cybersecurity approach that empowers human cognitive capital along two dimensions. We first resolve conflicting vulnerability reports and preprocess embedded security indicators into reliable data sets. Then, we use these data sets as a base for our proposed ensemble meta-classifier methods that fuse machine learning techniques to improve the predictive accuracy over individual machine learning algorithms. The application and implication of this methodology in the context of vulnerability analysis of computer systems are yet to unfold the full extent of its potential. The proposed cognitive security methodology in this paper is shown to improve performances when addressing the above-mentioned incompleteness and diversity issues across cybersecurity alert repositories. The experimental analysis conducted on actual cybersecurity data sources reveals interesting tradeoffs of our proposed selective ensemble methodology, to infer patterns of computer system vulnerabilities.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Information security, Vulnerability analysis, Data correlation, Machine learning, Ensemble, Data mining, Database management
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-20524 (URN)10.1016/j.jnca.2021.103210 (DOI)000709557700008 ()2-s2.0-85114497022 (Scopus ID)
Note

CC BY 4.0

Available online 4 September 2021, 103210

This research has been supported in part by EU ISF (Internal Security Fund) in the context of Project Grant # A431.678/2016.

Available from: 2021-09-06 Created: 2021-09-06 Last updated: 2021-11-11Bibliographically approved
Berri, J., Benlamri, R., Atif, Y. & Khallouki, H. (2021). Web Hypermedia Resources Reuse and Integration for On-Demand M-Learning. International Journal of Computer Science and Network Security, 21(1), 125-136
Open this publication in new window or tab >>Web Hypermedia Resources Reuse and Integration for On-Demand M-Learning
2021 (English)In: International Journal of Computer Science and Network Security, ISSN 1738-7906, Vol. 21, no 1, p. 125-136Article in journal (Refereed) Published
Abstract [en]

The development of systems that can generate automatically instructional material is a challenging goal for the e-learning community. These systems pave the way towards large scale e-learning deployment as they produce instruction on-demand for users requesting to learn about any topic, anywhere and anytime. However, realizing such systems is possible with the availability of vast repositories of web information in different formats that can be searched, reused and integrated into information-rich environments for interactive learning. This paradigm of learning relieves instructors from the tedious authoring task, making them focusing more on the design and quality of instruction. This paper presents a mobile learning system (Mole) that supports the generation of instructional material in M-Learning (Mobile Learning) contexts, by reusing and integrating heterogeneous hypermedia web resources. Mole uses open hypermedia repositories to build a Learning Web and to generate learning objects including various hypermedia resources that are adapted to the user context. Learning is delivered through a nice graphical user interface allowing the user to navigate conveniently while building their own learning path. A test case scenario illustrating Mole is presented along with a system evaluation which shows that in 90% of the cases Mole was able to generate learning objects that are related to the user query.

Place, publisher, year, edition, pages
International Journal of Computer Science and Network Security, 2021
Keywords
On-demand learning, learning object, web hypermedia resource, resource integration, mobile learning system
National Category
Computer Sciences
Research subject
Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-19554 (URN)10.22937/IJCSNS.2021.21.1.17 (DOI)000621103000017 ()
Available from: 2021-03-25 Created: 2021-03-25 Last updated: 2021-04-26Bibliographically approved
Atif, Y., Al-Falahi, K., Wangchuk, T. & Lindström, B. (2020). A fuzzy logic approach to influence maximization in social networks. Journal of Ambient Intelligence and Humanized Computing, 11(6), 2435-2451
Open this publication in new window or tab >>A fuzzy logic approach to influence maximization in social networks
2020 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 11, no 6, p. 2435-2451Article in journal (Refereed) Published
Abstract [en]

Within a community, social relationships are paramount to profile individuals’ conduct. For instance, an individual within a social network might be compelled to embrace a behaviour that his/her companion has recently adopted. Such social attitude is labelled social influence, which assesses the extent by which an individual’s social neighbourhood adopt that individual’s behaviour. We suggest an original approach to influence maximization using a fuzzy-logic based model, which combines influence-weights associated with historical logs of the social network users, and their favourable location in the network. Our approach uses a two-phases process to maximise influence diffusion. First, we harness the complexity of the problem by partitioning the network into significantly-enriched community-structures, which we then use as modules to locate the most influential nodes across the entire network. These key users are determined relatively to a fuzzy-logic based technique that identifies the most influential users, out of which the seed-set candidates to diffuse a behaviour or an innovation are extracted following the allocated budget for the influence campaign. This way to deal with influence propagation in social networks, is different from previous models, which do not compare structural and behavioural attributes among members of the network. The performance results show the validity of the proposed partitioning-approach of a social network into communities, and its contribution to “activate” a higher number of nodes overall. Our experimental study involves both empirical and real contemporary social-networks, whereby a smaller seed set of key users, is shown to scale influence to the high-end compared to some renowned techniques, which employ a larger seed set of key users and yet they influence less nodes in the social network.

Place, publisher, year, edition, pages
Springer, 2020
Keywords
Social networks, Community detection, Influence propagation, Fuzzy logic
National Category
Computer and Information Sciences
Research subject
Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-16779 (URN)10.1007/s12652-019-01286-2 (DOI)000536462400019 ()2-s2.0-85064252809 (Scopus ID)
Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2020-06-29Bibliographically approved
Jiang, Y., Atif, Y., Ding, J. & Wang, W. (2020). A Semantic Framework With Humans in the Loop for Vulnerability-Assessment in Cyber-Physical Production Systems. In: Slim Kallel, Frédéric Cuppens, Nora Cuppens-Boulahia, Ahmed Hadj Kacem (Ed.), Risks and Security of Internet and Systems: 14th International Conference, CRiSIS 2019, Hammamet, Tunisia, October 29–31, 2019, Proceedings. Paper presented at The 14th International Conference on Risks and Security of Internet and Systems, Hammamet, Tunisia, October 29-31, 2019 (pp. 128-143). Springer, 12026
Open this publication in new window or tab >>A Semantic Framework With Humans in the Loop for Vulnerability-Assessment in Cyber-Physical Production Systems
2020 (English)In: Risks and Security of Internet and Systems: 14th International Conference, CRiSIS 2019, Hammamet, Tunisia, October 29–31, 2019, Proceedings / [ed] Slim Kallel, Frédéric Cuppens, Nora Cuppens-Boulahia, Ahmed Hadj Kacem, Springer, 2020, Vol. 12026, p. 128-143Conference paper, Published paper (Refereed)
Abstract [en]

Criticalmanufacturingprocessesinsmartnetworkedsystems such as Cyber-Physical Production Systems (CPPSs) typically require guaranteed quality-of-service performances, which is supported by cyber- security management. Currently, most existing vulnerability-assessment techniques mostly rely on only the security department due to limited communication between di↵erent working groups. This poses a limitation to the security management of CPPSs, as malicious operations may use new exploits that occur between successive analysis milestones or across departmental managerial boundaries. Thus, it is important to study and analyse CPPS networks’ security, in terms of vulnerability analysis that accounts for humans in the production process loop, to prevent potential threats to infiltrate through cross-layer gaps and to reduce the magnitude of their impact. We propose a semantic framework that supports the col- laboration between di↵erent actors in the production process, to improve situation awareness for cyberthreats prevention. Stakeholders with dif- ferent expertise are contributing to vulnerability assessment, which can be further combined with attack-scenario analysis to provide more prac- tical analysis. In doing so, we show through a case study evaluation how our proposed framework leverages crucial relationships between vulner- abilities, threats and attacks, in order to narrow further the risk-window induced by discoverable vulnerabilities.

Place, publisher, year, edition, pages
Springer, 2020
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12026
Keywords
Cyber-Physical Production System Security, Human-in-the-Loop, Vulnerability Assessment, Semantic Model, Reference Model
National Category
Embedded Systems Other Electrical Engineering, Electronic Engineering, Information Engineering Information Systems Human Computer Interaction
Research subject
Distributed Real-Time Systems; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17754 (URN)10.1007/978-3-030-41568-6_9 (DOI)2-s2.0-85082136847 (Scopus ID)978-3-030-41567-9 (ISBN)978-3-030-41568-6 (ISBN)
Conference
The 14th International Conference on Risks and Security of Internet and Systems, Hammamet, Tunisia, October 29-31, 2019
Projects
ELVIRA
Note

Also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 12026)

EU ISF Project A431.678/2016 ELVIRA

Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2021-06-24Bibliographically approved
Jiang, Y. & Atif, Y. (2020). An Approach to Discover and Assess Vulnerability Severity Automatically in Cyber-Physical Systems. In: Berna Örs, Atilla Elçi (Ed.), Proceedings of the 13th International Conference on Security of Information and Networks: November 4-6, 2020, virtual, Istanbul, Turkey. Paper presented at 13th International Conference on Security of Information and Networks, SIN 2020, November 4-6, 2020, virtual, Istanbul, Turkey. New York, NY, USA: Association for Computing Machinery (ACM), Article ID 9.
Open this publication in new window or tab >>An Approach to Discover and Assess Vulnerability Severity Automatically in Cyber-Physical Systems
2020 (English)In: Proceedings of the 13th International Conference on Security of Information and Networks: November 4-6, 2020, virtual, Istanbul, Turkey / [ed] Berna Örs, Atilla Elçi, New York, NY, USA: Association for Computing Machinery (ACM), 2020, article id 9Conference paper, Published paper (Refereed)
Abstract [en]

Current vulnerability scoring mechanisms in complex cyber-physical systems (CPSs) face challenges induced by the proliferation of both component versions and recurring scoring-mechanism versions. Different data-repository sources like National Vulnerability Database (NVD), vendor websites as well as third party security tool analysers (e.g. ICS CERT and VulDB) may provide conflicting severity scores. We propose a machine-learning pipeline mechanism to compute vulnerability severity scores automatically. This method also discovers score correlations from established sources to infer and enhance the severity consistency of reported vulnerabilities. To evaluate our approach, we show through a CPS-based case study how our proposed scoring system automatically synthesises accurate scores for some vulnerability instances, to support remediation decision-making processes. In this case study, we also analyse the characteristics of CPS vulnerability instances. 

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2020
Series
ACM International Conference Proceedings Series (ICPS)
Keywords
Cybersecurity, Text-Mining, Cyber-Physical System, Vulnerability Analysis, CVSS, Decision making, Embedded systems, Turing machines, Current vulnerabilities, Cyber physical systems (CPSs), Data repositories, National vulnerability database, Remediation decision, Scoring systems, Security tools, Third parties, Network security
National Category
Embedded Systems Computer Systems
Research subject
Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-19500 (URN)10.1145/3433174.3433612 (DOI)2-s2.0-85100625302 (Scopus ID)978-1-4503-8751-4 (ISBN)
Conference
13th International Conference on Security of Information and Networks, SIN 2020, November 4-6, 2020, virtual, Istanbul, Turkey
Note

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, or republish,to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.SIN 2020, November 4–7, 2020, Merkez, Turkey© 2020 Association for Computing Machinery.

Available from: 2021-02-25 Created: 2021-02-25 Last updated: 2021-08-20Bibliographically approved
Atif, Y., Kharrazi, S., Ding, J. & Andler, S. F. (2020). Internet of Things data analytics for parking availability prediction and guidance. European transactions on telecommunications, 31, Article ID e3862.
Open this publication in new window or tab >>Internet of Things data analytics for parking availability prediction and guidance
2020 (English)In: European transactions on telecommunications, ISSN 1124-318X, E-ISSN 2161-3915, Vol. 31, article id e3862Article in journal (Refereed) Published
Abstract [en]

Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of transmission control protocol/internet protocol con- nectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data streams, which could be exploited to deliver novel cloud-based services. Connected vehicles and road-infrastructure data are leveraged in this article to build applications that alleviate notorious parking and induced traffic-congestion issues. To optimize the utility of parking lots, our proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First, a search pro- cess is triggered to identify the expected arrival-time periods to all parking lots in the targeted central business district (CBD) area. This process utilizes smart-pole data streams reporting congestion rates across parking area junc- tions. Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state-transition matrix, showing the transfor- mation of available spots in a parking lot over short periods of time. Finally, this matrix is projected against similar future seasonal periods to figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capa- bilities, when further parking resources are made available, compared to a baseline case algorithm. Using standard urban-mobility simulation packages, the traffic-congestion-aware SmartPark is also shown to minimize the journey duration to the selected parking lot while maximizing the chances to find an available spot at the selected lot.

Place, publisher, year, edition, pages
Wiley-Blackwell Publishing Inc., 2020
Keywords
smart parking, stochastic model, markov chain, internet of things, sumo, data analytics, autonomous cars
National Category
Transport Systems and Logistics Computer and Information Sciences
Research subject
Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-18081 (URN)10.1002/ett.3862 (DOI)000506093200001 ()2-s2.0-85078033422 (Scopus ID)
Projects
SmartPark
Funder
Vinnova, 2017-03028
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-05-20Bibliographically approved
Kharrazi, S. & Atif, Y. (2020). Sustainable smart-parking management for connected and autonomous vehicles. Linköping: Statens väg- och transportforskningsinstitut
Open this publication in new window or tab >>Sustainable smart-parking management for connected and autonomous vehicles
2020 (English)Report (Other academic)
Alternative title[sv]
Hållbar, smart parkeringskoordinering för uppkopplade och autonoma fordon
Abstract [en]

Traffic induced by parking-spot seekers is a growing challenge and constitutes a considerable portion of the traffic in city centers. New opportunities to solve this problem are emerging by connected vehicles and infrastructure. For instance, ultrasonic and magnetic sensors are already mounted on the ceiling of many parking lots to detect the availability of a parking spot. These sensors can provide parking spot availability information in real-time. Further, traffic-aware smart sensors which can detect the movement of individual vehicles are also available in many city and highway areas. This report suggests an algorithm for a cloud-based parking service that exploits these streams of data to choose the best parking lot in a given parking area.

The parking seeking problem is subject to a range of criteria that may include user, municipality and parking operator preferences. Users may have some preferences with respect to walking distance to destination. Municipalities prefer to spread the traffic to reduce congestion in the urban core. Parking operators seek to maximize parking lot utilization in order to increase the revenue on real-estate investments. To solve this problem, an optimization algorithm based on multicriteria decision making process is used.

The proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot. The algorithm features three modular sections:

• First, a search process is triggered to identify the expected arrival time periods to all parking lots in the targeted parking area. This process utilizes smart pole data streams reporting congestion rates across the targeted parking area.

• Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state transition matrix, showing the transformation of available spots in a parking lot over short periods of time.

• Finally, this matrix is projected against similar future seasonal periods to predict the actual vacancy of a parking lot at the arrival time.

Abstract [sv]

Trafik som består av sökande efter parkeringsplats är ett växande problem och utgör en avsevärd del av trafiken i stadskärnor. Nya möjligheter att lösa detta problem kommer dock finnas när fordonen är uppkopplade till infrastrukturen. Till exempel, ultraljudsensorer och magnetiska sensorer är redan monterade i taket på många parkeringshus för att detektera tillgängligheten av en parkeringsplats, och dessa sensorer kan ge information om lediga parkeringsplatser i realtid. Vidare, smarta sensorer för trafikmätning, som också kan se förflyttning av enskilda fordon, finns redan i många städer och på motorvägar. Denna rapport föreslår en algoritm till en molnbaserad parkeringstjänst som använder ovanstående typ av data för att välja den bästa parkeringen i ett visst parkeringsområde.

Lösningen på problemet att välja bästa parkering omfattar en rad kriterier som kan inkludera preferenser från användare, kommuner och parkeringsbolag. Användare kan till exempel ha vissa preferenser med avseende på gångavstånd till destinationen, kommuner kan föredra att sprida trafiken för att minska trafikstockningarna i stadskärnan och parkeringsbolag försöker maximera parkeringars utnyttjandegrad för att öka avkastningen på fastighetsinvesteringar. För att lösa detta parkeringsproblem användes en optimeringsalgoritm baserad på en beslutsprocess med flera kriterier.

Den föreslagna SmartPark algoritmen använder en diskret Markov-kedjemodell för att prognosticera det framtida tillståndet för en parkeringsplats. Algoritmen innehåller tre modulära delar:

• Först används en sökprocess för att identifiera de förväntade ankomsttiderna på alla parkeringsplatser i det önskade parkeringsområdet. Denna process använder data från smarta stolpar som mäter trafik och trängsel inom parkeringsområdet.

• Sedan görs en prediktiv analys med hjälp av sammanställda historiska data över tidigare parkeringsanvändning för att skapa en matris som visar förändringen av tillgängliga platser på en parkeringsplats över kortare tidsperioder.

• Till sist används matrisen tillsammans med data om säsongsvariationer för att prediktera ledigheten av en parkeringsplats vid tiden för den beräknade ankomsten.

Place, publisher, year, edition, pages
Linköping: Statens väg- och transportforskningsinstitut, 2020. p. 29
Series
VTI rapport,, ISSN 0347-6030 ; 1033A
National Category
Transport Systems and Logistics Other Computer and Information Science Computer Engineering Computer Systems Signal Processing
Research subject
Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-23303 (URN)
Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2023-10-09Bibliographically approved
Jiang, Y., Atif, Y. & Ding, J. (2019). Cyber-Physical Systems Security Based on A Cross-Linked and Correlated Vulnerability Database. In: Simin Nadjm-Tehrani (Ed.), Simin Nadjm-Tehrani (Ed.), Critical Information Infrastructures Security: 14th International Conference, CRITIS 2019, Linköping, Sweden, September 23–25, 2019, Revised Selected Papers. Paper presented at the 14th International Conference on Critical Information Infrastructures Security, Linköping, Sweden, 23-25 September 2019 (pp. 71-82). Paper presented at the 14th International Conference on Critical Information Infrastructures Security, Linköping, Sweden, 23-25 September 2019. Springer, 11777
Open this publication in new window or tab >>Cyber-Physical Systems Security Based on A Cross-Linked and Correlated Vulnerability Database
2019 (English)In: Critical Information Infrastructures Security: 14th International Conference, CRITIS 2019, Linköping, Sweden, September 23–25, 2019, Revised Selected Papers / [ed] Simin Nadjm-Tehrani, Springer, 2019, Vol. 11777, p. 71-82Chapter in book (Refereed)
Abstract [en]

Recent advances in data analytics prompt dynamic datadriven vulnerability assessments whereby data contained from vulnerabilityalert repositories as well as from Cyber-physical System (CPS) layer networks and standardised enumerations. Yet, current vulnerability assessment processes are mostly conducted manually. However, the huge volume of scanned data requires substantial information processing and analytical reasoning, which could not be satisfied considering the imprecision of manual vulnerability analysis. In this paper, we propose to employ a cross-linked and correlated database to collect, extract, filter and visualise vulnerability data across multiple existing repositories, whereby CPS vulnerability information is inferred. Based on our locally-updated database, we provide an in-depth case study on gathered CPS vulnerability data, to explore the trends of CPS vulnerability. In doing so, we aim to support a higher level of automation in vulnerability awareness and back risk-analysis exercises in critical infrastructures (CIs) protection.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11777
Keywords
Cyber-Physical System Security, Vulnerability Analysis, Correlated Database Management, SCADA
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Sciences
Research subject
Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-17753 (URN)10.1007/978-3-030-37670-3_6 (DOI)000612959400006 ()2-s2.0-85077502760 (Scopus ID)978-3-030-37669-7 (ISBN)978-3-030-37670-3 (ISBN)
Conference
the 14th International Conference on Critical Information Infrastructures Security, Linköping, Sweden, 23-25 September 2019
Projects
EU ISF Project A431.678/2016 ELVIRA
Note

Also part of the Security and Cryptology book sub series (LNSC, volume 11777)

Funded by EU Internal Security Funds

Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2022-04-12Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7312-9089

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