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TRANSLATING THE STPA-SEC SECURITY METHOD INTO A MODEL-BASED engineering APPROACH

INCOSE International Symposium 2024年第1期34卷 1948-1963页

作者： Silawi, Ehab Shaked, Avi Reich, Yoram Systems Engineering Research Initiative Tel Aviv University Israel Department of Computer Science University of Oxford United Kingdom

In today's interconnected digital ecosystem, protecting cyber-physical systems is critical. STPA-Sec is a systematic method that allows to analyze system designs and identify vulnerabilities in those designs from the onset and throughout the system lifecycle. In this article, we describe a carefully designed metamodel that accommodates the concepts and steps of the method. We translate key concepts from STPA-Sec into a metamodel, with the intention of facilitating a more structured and disciplined application of STPA-Sec. We demonstrate the advantage of using the metamodel in two case studies. This article offers a step forward in the rigorous application of STPA-Sec. Copyright © 2024 by Ehab Silawi, Avi Shaked & Yoram Reich. Permission granted to INCOSE to publish and use.

关键词： systems analysis

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Renewable hydrogen blending into urban natural gas grids with power-to-gas: A techno-economic assessment for Greece

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International Journal of Hydrogen Energy 2025年 139卷 380-395页

作者： Koutalidis, Hercules Pelekis, Sotiris Skepetari, Eugenia Ntanos, Christos Karakolis, Evangelos Michalitsi-Psarrou, Ariadni Blika, Afroditi Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Iroon Polytechniou 9 Attica Athens15773 Greece

Renewable hydrogen can be produced with Power-to-Gas (PtG) systems, using renewable energy sources (RES), with minimal Greenhouse Gas (GHG) emissions. With this paper, the authors aim to provide clear insights into optimal PtG systems’ techno-economic performance, in the case of renewable hydrogen blending into gas grids such as that of the city of Athens. Two different production methods are investigated, namely one in which the investor owns the required energy generation units, and one in which the required energy is procured from independent producers. Seven different underlying parametric scenarios are investigated, reflecting possible future costs developments according to scientific literature. The results show Levelized Costs of produced hydrogen ranging from 2.14 to 5 €/kg. Production through own energy units is currently overall more economical. Energy and electrolysis costs comprise most of the costs, indicating the necessity for considerable price reductions for green hydrogen to become competitive. © 2025 The Authors

关键词： Solar fuels

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Optical Hyperuricemia Sensor with Uricase Biofunctionalized Stacked Nanocomposite 10

Optical Hyperuricemia Sensor with Uricase Biofunctionalized ...

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10th IEEE International Conference on Photonics, ICP 2024

作者： Onifade, O.A. Bakar, M.H.B.A. Mahdi, M.A. Noor, A.S.B.M. Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Serdang Malaysia

ISBN: (纸本)9798350383164

—This paper presents an advanced optical sensor based on surface plasmon resonance (SPR) designed to detect hyperuricemia, characterized by elevated uric acid (UA) levels. The sensor features a stacked nanocomposite comprising gold-graphene quantum dots (GQD) biofunctionalized with uricase enzyme, ensuring high sensitivity and selectivity for UA detection over other potentially competing interferents like ascorbic acid, glucose, D-cystine, urea, and creatinine commonly found in biological samples. The developed sensor achieves an impressive sensitivity of 0.093◦/(mg/dL) for UA at 12 mg/dL, significantly higher than the recorded sensitivity of 0.015◦/(mg/dL) for all other potential interferents at the same concentration. This highlights the sensor’s exceptional precision in discerning UA. Moreover, the sensor demonstrates notable selectivity for UA even at low concentrations, registering a resonance angle shift of 0.371◦ at 0.5 mg/dL, compared to a lower angle shift of 0.185◦ for other interferents at higher concentrations of 12 mg/dL. These findings emphasize the sensor’s accuracy and rapid response in distinguishing UA, positioning it as a promising tool for early diagnosis and management of hyperuricemia in clinical settings, addressing a critical need for reliable and efficient UA detection. ©2024 IEEE.

关键词： Graphene quantum dots

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Forecasting Power Prices for Cloud Computing using an Enhanced Machine Learning 15

Forecasting Power Prices for Cloud Computing using an Enhanc...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nithisha, C. Tejaswini, Chillara Pavana Samyuktha, Busireddy Harika, Banda Chatrapathi, Gantala Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

The use of several machine learning regression models for the prediction of power costs is explored in this work. The following models are taken into consideration: Gradient Boosting, Adaboosting, Lgbmregressor, Cataboost regressor, Random Forest Regressor, XGBoost Regressor, Support Vector Regressor, and Stacking Regressor. Historical power pricing data with variables like time, demand, and weather make up the training and assessment dataset. A portion of the dataset is used to train each model, which is then adjusted to maximize prediction performance. To evaluate each model's precision, resilience, and computational effectiveness, a comparative study is done. Insights into the applicability of various regression models for the forecast of power prices are provided by the study's findings, which may help decision-makers optimize the planning and management of energy resources. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Hardening to Improve the Security of Financial Institutions 31

Hardening to Improve the Security of Financial Institutions

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31st IEEE International Conference on Electronics, Electrical engineering and Computing, INTERCON 2024

作者： Moscosos, Miguel A. Lazo Anccori, Pedro E. Sanchez Choquehuayta, Wilder Nina Universidad Tecnológica del Perú Computer and Systems Engineering Arequipa Peru

ISBN: (纸本)9798350378344

The great importance of having financial institutions certified in PCI DSS is the generation of a security environment in terms of data protection and it is an incentive that positions financial institutions as professional, responsible, serious and reliable. This research work proposes the implementation of hardening techniques and the application of security methodologies that allow having an advanced level of security in order to reduce vulnerabilities and risks that may exist in financial institutions. Previously, articles referring to security methodologies and standards applied in different case studies were reviewed in order to have a reference and be able to apply them in our case study. The proposed methodology is composed of 5 stages 1) Information gathering, in which information mapping was carried out. 2) Vulnerability Analysis, performing the scanning of the network in the productive area. 3) Application and comparison of information security methodologies, which include: PCI DSS, OSSTMM v3, OWASP and the following scanning tools were used: Nmap, Xprobe 2, Wireshark, Netstat, etc. 4) Analysis of the results. 5) Report generation. The results of the study indicate a 2 improvement in the effectiveness of mitigated risks and controls. On the other hand, when comparing security methodologies, the PCI DSS standard scores better on the comparison scale for our case study, as it is closely aligned with internal banking processes (SMEs). © 2024 IEEE.

关键词： Hardening

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Integrating Tesseract OCR with Large Language Models for Enhancing Textual Understanding 15

Integrating Tesseract OCR with Large Language Models for Enh...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sriharsha, A.V. Bhavana, Mekala Tejaswee, Syamala Ahmed, Mynasaheb Bushra Reddy, Peddi Reddi Jaipal Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Optical Character Recognition (OCR) is a technology that automatically extracts textual information from document images. An LLM, a large language model, is a neural network designed to understand, generate, and respond to human-like text. These models are deep neural networks trained on massive amounts of text data, sometimes encompassing large portions of the entire publicly available text on the internet. LLMs require textual data to be converted into numerical vectors, known as embeddings since they can't process raw text. Embeddings transform discrete data (like words or images) into continuous vector spaces, making them compatible with neural network operations. The combined strengths of Tesseract OCR's image-based text extraction and the deep language understanding of LLMs enhance the accuracy and depth of textual comprehension. The proposed integrated model establishes a novel paradigm in enhancing textual understanding, progress in document analysis, information retrieval, and various NLP applications. © Grenze Scientific Society, 2024.

关键词： Deep neural networks

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Using Media Player Software as Stealth Attack Vectors for Manipulating Human Mind and Behavior

Using Media Player Software as Stealth Attack Vectors for Ma...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Siriwardana, S.M.D.N. Computer Systems Engineering Department Malabe Sri Lanka

ISBN: (纸本)9798350331790

Advanced malware detection mechanism and technologies being used in anti-virus software solutions, firewalls and many other malware detection technologies had been able to restrict the spread, infection and exploitation of the vulnerabilities within information systems to mitigate the risks to the information assets that these particular systems possess. As human factor being the weakest link in any security chain, exploiting the vulnerabilities and weaknesses in humans tend to be more effective than resource and time-consuming system breaches and network penetrations. Manipulating the mind of people is found to be more effective by proven outcomes of famous social engineering and phishing attacks. Social engineering and phishing exploit the vulnerabilities of the conscious mind which is about 5% of the human mind. But a deeper "penetration"into the hidden 95% of human subconscious mind will open the doors to new opportunities which will be devastating. This research paper will introduce a new malware type known as "mindware"which focus on how a malware can be infected from an electronic media device to the subconscious mind of a human and manipulate that human being as a "bot"in an advanced cybercrime chain. Furthermore, the elaboration of the capabilities of mindware, potential threat areas, stealth mechanisms, good uses, misuses, proofs about the reliability and the future development areas of mindware are also discussed concisely in this research paper. © 2023 IEEE.

关键词： Cybersecurity

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Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading

IEEE Networking Letters

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IEEE Networking Letters 2024年第4期6卷 232-236页

作者： Zhou, Changshi Liu, Weiqi Han, Tao Ansari, Nirwan New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States

From AI-assisted art creation to large language model (LLM)-powered ChatGPT, AI-generated contents and services are becoming a transforming force. It calls for the telecom industry to embrace the prospects of AIGC services and face the unique challenges posed by incorporating generative model services into the AI-native 6G wireless network paradigm. We propose enabling AIGC inference services on mobile devices by optimizing MEC-device computing offloading, through which AIGC task latency is minimized by reinforcement learning based policy agent in a computing resource constrained and bandwidth limited wireless environment. Simulation results are presented to demonstrate the performance advantage. © 2019 IEEE.

关键词： Reinforcement learning

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COVID-19 Detection Using Convolutional Neural Networks and Ensemble Approaches – A Review 15th

COVID-19 Detection Using Convolutional Neural Networks and E...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Agilandeeswari, L. Singh, Abhimanyu School of Computer Science Engineering and Information Systems VIT Vellore India

ISBN: (纸本)9783031810794

More than 115 million individuals were affected by the coronavirus (COVID-19), which the World Health Organization classified as a pandemic. The virus fatally affected more than 2 million people. This has underscored the critical need for accurate and efficient diagnostic tools. In this paper, we provide a deep review of Convolutional Neural Network (CNN) based architectures and Ensembles for the detection of COVID-19 infection from medical imaging data. The study encompasses an in-depth analysis of the current state of the art, challenges, and advancements in the field, highlighting the potential of combining multiple models through ensemble methods for improved diagnostic accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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Credit card default prediction using ML and DL techniques

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 293-306页

作者： Wahab, Fazal Khan, Imran Sabada, Sneha Corvit Systems Peshawar Pakistan Department of Informatics University of Sussex United Kingdom Department of Engineering and Computer Science University of Hertfordshire United Kingdom

The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %. © 2024 The Authors

关键词： Prediction models

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