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Publications (10 of 51) Show all publications
Silonosov, A. & Henesey, L. (2024). Crypto-agility performance analysis for AIS data sharing confidentiality based on attribute-based encryption. In: D. C. Wyld & D. Nagamalai (Ed.), CS & IT Conference Proceedings: . Paper presented at 14th International Conference on Computer Science and Information Technology (CCSIT 2024), Copenhagen Sept 21-22, 2024 (pp. 193-212). AIRCC Publishing Corporation, 14
Open this publication in new window or tab >>Crypto-agility performance analysis for AIS data sharing confidentiality based on attribute-based encryption
2024 (English)In: CS & IT Conference Proceedings / [ed] D. C. Wyld & D. Nagamalai, AIRCC Publishing Corporation , 2024, Vol. 14, p. 193-212Conference paper, Published paper (Refereed)
Abstract [en]

The research presented in the paper evaluates practices of Attribute-Based Encryption as a key encapsulation mechanism and proposes end-to-end encryption architecture for a cloudbased ship tracking system confidentiality. Though extensively used for efficiently gathering and sharing maritime data, these systems draw information from Automated Identification Systems, ports, and vessels, which can lead to cyber-security vulnerabilities. This paper presents a study addressing the current state of knowledge, methodologies, and challenges associated with supporting cryptographic agility for End-to-End Encryption (E2EE) for AIS data. To study cryptographic agility performance, a new metric has been introduced for cryptographic library analysis that improves the methodology by comparing Attribute-Based Encryption (ABE) with state of the art CRYSTALS-Kyber key encapsulation mechanism (KEM) that belongs to Post-Quantum Cryptography (PQC). A comprehensive series of experiments are undertaken to simulate large-scale cryptographic migration within the proposed system, showcasing the practical applicability of the proposed approach in measuring cryptographic agility performance.

Place, publisher, year, edition, pages
AIRCC Publishing Corporation, 2024
Series
Computer Science & Information Technology (CS & IT), E-ISSN 2231-5403
Keywords
AIS ship tracking data, Key encapsulation mechanism, end-to-end encryption, cryptographic agility, CRYSTALS-Kyber, Post-Quantum Cryptography
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-26989 (URN)10.5121/csit.2024.141714 (DOI)
Conference
14th International Conference on Computer Science and Information Technology (CCSIT 2024), Copenhagen Sept 21-22, 2024
Available from: 2024-10-10 Created: 2024-10-10 Last updated: 2024-10-11Bibliographically approved
Henesey, L., Tkach, V., Parkhomenko, P. & Pingali, K. (2024). Evaluation of Machine Learning Algorithms and Methods for Improved Predictions in Cryptocurrency in Short-Time Horizons. In: Ireneusz Miciuła (Ed.), Cryptocurrencies - Financial Technologies of the Future: . IntechOpen
Open this publication in new window or tab >>Evaluation of Machine Learning Algorithms and Methods for Improved Predictions in Cryptocurrency in Short-Time Horizons
2024 (English)In: Cryptocurrencies - Financial Technologies of the Future / [ed] Ireneusz Miciuła, IntechOpen , 2024Chapter in book (Other academic)
Abstract [en]

Cryptocurrency has the potential to reshape financial systems and introduce financial investments that are inclusive in nature, which has led to significant research in the prediction of cryptocurrency prices by employing artificial neural networks and machine learning models. Accurate short-term predictions are essential for optimizing investment strategies, minimizing risks, and ensuring market stability. Prior studies in time-series forecasting have successfully employed statistical methods like Auto-Regressive Integrated Moving Average (ARIMA) and machine learning algorithms such as Long Short-Term Memory (LSTM). The research results presented in this paper evaluate various statistical and machine learning algorithms, assessing their accuracy and effectiveness in modeling volatile cryptocurrency data for short-term forecasting. Additionally, the study explores diverse hyperparameter settings to enhance the performance of machine learning models. The highest performance is achieved by a hybrid model combining LSTM and Deep Neural Network (DNN), showcasing its effectiveness in forecasting cryptocurrency prices with improved accuracy and capability.

Place, publisher, year, edition, pages
IntechOpen, 2024
Keywords
cryptocurrency, prediction, machine learning, long short-term memory (LSTM), deep neural networks
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-27426 (URN)10.5772/intechopen.1004320 (DOI)9780854662173 (ISBN)9780854662197 (ISBN)9780854662180 (ISBN)
Available from: 2025-02-03 Created: 2025-02-03 Last updated: 2025-02-03Bibliographically approved
Silonosov, A., Henesey, L. & Baranovskyi, O. (2024). Poster: Towards cryptographic agility in end-to-end encryption systems for computer generated telemetry data. In: HOTMOBILE 2024 - Proceedings of the 2024 25th International Workshop on Mobile Computing Systems and Applications: . Paper presented at 25th International Workshop on Mobile Computing Systems and Applications, HOTMOBILE 2024, San Diego, 28 February through 29 February 2024 (pp. 144). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Poster: Towards cryptographic agility in end-to-end encryption systems for computer generated telemetry data
2024 (English)In: HOTMOBILE 2024 - Proceedings of the 2024 25th International Workshop on Mobile Computing Systems and Applications, Association for Computing Machinery (ACM), 2024, p. 144-Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

The research presented focuses on cryptographic-agility for End-to-End Encryption systems (E2EE) that could be implemented for telemetry data encryption. The recent report 1 by Microsoft after a security incident, describes the consequences that lead to encryption key leakage from telemetry data of highly protected production system. Internet of Things (IoT) and mobile devices constantly produce telemetry data which contains sensitive information. The data partially belongs to vendor, but IoT consumer’s consent is needed to access such data for troubleshooting or forensic analysis. Goyal et al [2] proposed Attribute Based Encryption (ABE) as a solution for legitimately access the audit log contents by engineering team. © 2024 Copyright held by the owner/author(s).

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2024
Keywords
Cryptography, Mobile telecommunication systems, Sensitive data, Telemetering equipment, Computer generated, CryptoGraphics, Data encryption, Encryption key, Encryption system, End-to-end encryption, MicroSoft, Production system, Security incident, Telemetry data, Internet of things
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-26065 (URN)10.1145/3638550.3643624 (DOI)001278360200032 ()2-s2.0-85187203995 (Scopus ID)9798400704970 (ISBN)
Conference
25th International Workshop on Mobile Computing Systems and Applications, HOTMOBILE 2024, San Diego, 28 February through 29 February 2024
Available from: 2024-03-27 Created: 2024-03-27 Last updated: 2024-09-11Bibliographically approved
Gerlitz, L., Meyer, C. & Henesey, L. (2024). Sourcing Sustainability Transition in Small and Medium-Sized Ports of the Baltic Sea Region: A Case of Sustainable Futuring with Living Labs. Sustainability, 16(11), Article ID 4667.
Open this publication in new window or tab >>Sourcing Sustainability Transition in Small and Medium-Sized Ports of the Baltic Sea Region: A Case of Sustainable Futuring with Living Labs
2024 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 16, no 11, article id 4667Article in journal (Refereed) Published
Abstract [en]

The present research points to an alternative concern against the mainstream research of future ports’ development by taking a transdisciplinary approach of a Living Lab (LL) concept for a better sustainability and innovation record in Small and Medium-Sized Ports (SMSPs). Deploying qualitative research for the examination of this new phenomenon of aggregating LLs into SMSPs, this research builds upon stakeholder workshops, in-depth interviews, and designed port pilots as case studies dedicated to innovation and sustainability transition in the Baltic Sea Region (BSR) at the turn of 2030. Given its rich and significant empirical foundation, the present research substantially contributes to sustainability orientation and transitions in ports. The key original elements of this study are fourfold: (1) the research provides a theoretical and practical LL framework enabling innovation and sustainability to be grasped in ports in times of technological, social, and political disruption; (2) this research increases the minimal number of existing previous efforts studying SMSPs in the transitional discourse; (3) the paper addresses not only hard technological innovation concerns but also aspects of social acceptance and the role of social interactions; (4) the research goes beyond geographical boundaries of a single port, thus providing a joint and collaborative approach towards sustainability rather than an individual perception on sustainability transition, existing networks, and clusters. © 2024 by the authors.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
Baltic Sea Region, digitalization, Living Lab, pilot project, Small and Medium-Sized Ports, sustainability transition, sustainable innovation, Atlantic Ocean, Baltic Sea, digitization, empirical analysis, innovation, port development, stakeholder, sustainability
National Category
Social Sciences Interdisciplinary
Identifiers
urn:nbn:se:bth-26542 (URN)10.3390/su16114667 (DOI)001249259200001 ()2-s2.0-85195873532 (Scopus ID)
Funder
European Regional Development Fund (ERDF)
Available from: 2024-06-24 Created: 2024-06-24 Last updated: 2024-06-28Bibliographically approved
Silonosov, A. & Henesey, L. (2024). Telemetry data sharing based on Attribute-Based Encryption (ABE) schemes for cloud-based Drone Management system.. In: ACM International Conference Proceeding Series: . Paper presented at 19th International Conference on Availability, Reliability and Security, ARES 2024, Vienna, July 30- Aug 2 2024. Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Telemetry data sharing based on Attribute-Based Encryption (ABE) schemes for cloud-based Drone Management system.
2024 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), 2024Conference paper, Published paper (Refereed)
Abstract [en]

The research presented in the paper evaluates practices of Attribute-Based Encryption, leading to a proposed end-to-end encryption strategy for a cloud-based drone management system. Though extensively used for efficiently gathering and sharing video surveilance data, these systems also collect telemetry information with sensitive data. This paper presents a study addressing the current state of knowledge, methodologies, and challenges associated with supporting cryptographic agility for End-to-End Encryption (E2EE) for telemetry data confidentiality. To enhance cryptographic agility performance, a new metric has been introduced for cryptographic library analysis that improves the methodology by considering Attribute-Based Encryption (ABE) with a conventional key-encapsulation mechanism in OpenSSL. A comprehensive series of experiments are undertaken to simulate cryptographic agility within the proposed system, showcasing the practical applicability of the proposed approach in measuring cryptographic agility performance. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2024
Keywords
attribute based encryption., audit log data, cryptographic agility, end-to-end encryption, key-encapsulation mechanism, telemetry, Cryptography, Information management, Sensitive data, Telemetering equipment, Attribute-based encryptions, Audit logs, CryptoGraphics, Key encapsulation mechanisms, Log data, Telemetry data, Drones
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-26823 (URN)10.1145/3664476.3670909 (DOI)001283894700158 ()2-s2.0-85200415750 (Scopus ID)9798400717185 (ISBN)
Conference
19th International Conference on Availability, Reliability and Security, ARES 2024, Vienna, July 30- Aug 2 2024
Available from: 2024-08-16 Created: 2024-08-16 Last updated: 2025-01-03Bibliographically approved
Silonosov, A. & Henesey, L. (2024). Towards cryptographic agility manifesto in end-to-end encryption systems: a position paper from the perspective of crypto-consumers. In: Proceedings - 2024 IEEE Conference on Dependable, Autonomic and Secure Computing, DASC 2024: . Paper presented at 22nd IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2024, Boracay Island, Nov 5-8, 2024 (pp. 65-72). IEEE Computer Society
Open this publication in new window or tab >>Towards cryptographic agility manifesto in end-to-end encryption systems: a position paper from the perspective of crypto-consumers
2024 (English)In: Proceedings - 2024 IEEE Conference on Dependable, Autonomic and Secure Computing, DASC 2024, IEEE Computer Society, 2024, p. 65-72Conference paper, Published paper (Refereed)
Abstract [en]

This position paper presents the preliminary results from a research study focusing on cryptographic agility for consumers of open-source cryptographic libraries. We provide a concise overview of frontiers and recent advancements in cryptographic agility research frontier, examining the utilized approaches and emphasizing the perception of application layer encryption in end-to-end encryption systems. The paper delves into the state of practice of cryptographic libraries and programming interfaces, outlining recognized challenges and knowledge gaps that warrant exploration through new scientific research. Furthermore, we outline the values of cryptographic agility in a manifesto and propose a survey structure to validate our assumptions. 

Place, publisher, year, edition, pages
IEEE Computer Society, 2024
Keywords
application-level encryption, Cryptographic agility, end-to-end encryption, key-encapsulation mechanism, Application level, CryptoGraphics, Encryption system, Key encapsulation mechanisms, Open-source, Position papers, Research studies
National Category
Security, Privacy and Cryptography
Identifiers
urn:nbn:se:bth-27461 (URN)10.1109/DASC64200.2024.00015 (DOI)2-s2.0-85216551882 (Scopus ID)9798331522728 (ISBN)
Conference
22nd IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2024, Boracay Island, Nov 5-8, 2024
Available from: 2025-02-17 Created: 2025-02-17 Last updated: 2025-02-17Bibliographically approved
Alsolai, H., Qureshi, S., Iqbal, S. M., Vanichayobon, S., Henesey, L., Lindley, C. & Karrila, S. (2022). A Systematic Review of Literature on Automated Sleep Scoring. IEEE Access, 10, 79419-79443
Open this publication in new window or tab >>A Systematic Review of Literature on Automated Sleep Scoring
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2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 79419-79443Article, review/survey (Refereed) Published
Abstract [en]

Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Sleep, Feature extraction, Machine learning, Electroencephalography, StandardsSleep apnea, Deep learning, Artificial neural network, automatic sleep scoring system, big data, feature extraction, inter-rater variability, machine learning, sleep stages
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-23557 (URN)10.1109/access.2022.3194145 (DOI)000836601900001 ()
Note

open access

Available from: 2022-08-19 Created: 2022-08-19 Last updated: 2022-08-19Bibliographically approved
Alsolai, H., Qureshi, S., Iqbal, S. M., Ameer, A., Cheaha, D., Henesey, L. & Karrila, S. (2022). Employing a Long-Short-Term Memory Neural Network to Improve Automatic Sleep Stage Classification of Pharmaco-EEG Profiles. Applied Sciences, 12(10), Article ID 5248.
Open this publication in new window or tab >>Employing a Long-Short-Term Memory Neural Network to Improve Automatic Sleep Stage Classification of Pharmaco-EEG Profiles
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2022 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 10, article id 5248Article in journal (Refereed) Published
Abstract [en]

An increasing problem in today's society is the spiraling number of people suffering from various sleep disorders. The research results presented in this paper support the use of a novel method that employs techniques from the classification of sleep disorders for more accurate scoring. Applying this novel method will assist researchers with better analyzing subject profiles for recommending prescriptions or to alleviate sleep disorders. In biomedical research, the use of animal models is required to experimentally test the safety and efficacy of a drug in the pre-clinical stage. We have developed a novel LSTM Recurrent Neural Network to process Pharmaco-EEG Profiles of rats to automatically score their sleep-wake stages. The results indicate improvements over the current methods; for the case of combined channels, the model accuracy improved by 1% and 3% in binary or multiclass classifications, respectively, to accuracies of 93% and 82%. In the case of using a single channel, binary and multiclass LSTM models for identifying rodent sleep stages using single or multiple electrode positions for binary or multiclass problems have not been evaluated in prior literature. The results reveal that single or combined channels, and binary or multiclass classification tasks, can be applied in the automatic sleep scoring of rodents.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
recurrent neural network (RNN), electroencephalography (EEG), long short-term memory (LSTM), automatic sleep scoring, deep learning
National Category
Computer Sciences Neurosciences
Identifiers
urn:nbn:se:bth-23088 (URN)10.3390/app12105248 (DOI)000801687600001 ()
Note

open access

Available from: 2022-06-10 Created: 2022-06-10 Last updated: 2022-11-15Bibliographically approved
Paulauskas, V., Henesey, L., Paulauskas, D. & Simutis, M. (2022). Optimizing transportation between ports and the hinterland for decreasing impact to the environment. In: Bottani E., Bruzzone A.G., Longo F., Merkuryev Y., Piera M.A. (Ed.), International Conference on Harbour, Maritime and Multimodal Logistics Modelling and Simulation: . Paper presented at 24th International Conference on Harbor, Maritime and Multimodal Logistic Modeling and Simulation, HMS 2022, Rome, 19 September through 21 September 2022 (pp. 1-14). CAL-TEK
Open this publication in new window or tab >>Optimizing transportation between ports and the hinterland for decreasing impact to the environment
2022 (English)In: International Conference on Harbour, Maritime and Multimodal Logistics Modelling and Simulation / [ed] Bottani E., Bruzzone A.G., Longo F., Merkuryev Y., Piera M.A., CAL-TEK , 2022, p. 1-14Conference paper, Published paper (Refereed)
Abstract [en]

Today different transport modes use to deliver cargo between regions, from ports to final destination location or visa-versa. It is quite common to use road transport, which can deliver cargo “from door to door” but road transport causes big environmental impact. Considering alternative possibilities (road, railway and/or inland waterway transport) to decrease environmental impact from transport, it is very important. Based on theoretical and experimental tests, were find optimal solutions, which transport mode make minimum environmental impact and could be the most technically and economically effective solution. Traffic congestion on the roads, in some cases very high railway traffic in some regions, generates requirements by many stakeholders on ways to decrease the environmental impact from transport modes, which studded in Article to find and identify optimal transportation solutions with minimum environmental impact. A theoretical method evaluation conducted on the optimal transportation possibility that minimizes environmental impact. A transport modes environmental comparative index (ECI) is developed and used for evaluations. This paper presents possible alternative transportation conditions based on multi-criteria evaluation system, proposes theoretical basis for the optimal solutions from environmental and economic point of view, and provides for experimental testing during the specific case study, and finally provides recommendations and conclusions. © 2022 The Authors.

Place, publisher, year, edition, pages
CAL-TEK, 2022
Series
International Conference on Harbour, Maritime and Multimodal Logistics Modelling and Simulation, ISSN 2724-0339
Keywords
emissions, environmental comparative analysis, environmental comparative index, environmental impact, transport modes
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:bth-23750 (URN)10.46354/i3m.2022.hms.008 (DOI)2-s2.0-85139052216 (Scopus ID)9788885741744 (ISBN)
Conference
24th International Conference on Harbor, Maritime and Multimodal Logistic Modeling and Simulation, HMS 2022, Rome, 19 September through 21 September 2022
Note

open access

Available from: 2022-10-14 Created: 2022-10-14 Last updated: 2024-10-10Bibliographically approved
Paulauskas, V., Henesey, L., Plačiene, B., Jonkus, M., Paulauskas, D., Barzdžiukas, R., . . . Simutis, M. (2022). Optimizing Transportation between Sea Ports and Regions by Road Transport and Rail and Inland Waterway Transport Means Including “Last Mile” Solutions. Applied Sciences, 12(20), Article ID 10652.
Open this publication in new window or tab >>Optimizing Transportation between Sea Ports and Regions by Road Transport and Rail and Inland Waterway Transport Means Including “Last Mile” Solutions
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2022 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 20, article id 10652Article in journal (Refereed) Published
Abstract [en]

Optimization transportation cargo and passengers between ports and regions are very important, because industrial regions are located some distance from ports. The demand for energy request for the movement of transport is a necessity in the modern world. Transport and activity called transportation are used daily, everywhere, and a lot of energy is needed to power the various transport modes. Today different transport modes are being used to transport passengers and cargo. It is quite common to use road transport, which can transport passengers and cargo from door to door. Considering alternative possibilities (road, railway and/or inland waterway transport), it is important, based on theoretical and experimentation, to identify optimal solutions. In finding transport modes that are either most technically or economically effective, we could unearth possible solutions which would require minimal energy use. Unfortunately, with increased transportation, this often leads to traffic congestion on the roads, which requires additional energy (fuel). This situation generates requirements from many stakeholders in terms of finding ways to decrease the transportation time and energy (fuel) consumed by transport modes. A theoretical method evaluation is conducted on the optimal transportation possibility that minimizes transportation time and energy (fuel) use by employing graph theory, which is presented in this paper. The scientific contribution is the development of a transport modes comparative index, which is then used for evaluations. This paper presents possible alternative transportation conditions based on a multi-criteria evaluation system, proposes a theoretical basis for the optimal solutions from an eco-economic perspective that considers energy, and provides for experimental testing during a specific case study. The final results from the case study provide recommendations and conclusions. © 2022 by the authors.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
alternative fuels, connection to sea ports, energy consumption, optimal transportation solutions, transport mode comparative index, transport modes
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:bth-23826 (URN)10.3390/app122010652 (DOI)000872193800001 ()2-s2.0-85140450472 (Scopus ID)
Note

open access

Available from: 2022-11-04 Created: 2022-11-04 Last updated: 2022-12-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0518-6532

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