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International Scientific Conference on Power and Electrical engineering of Riga Technical University (RTUCON)

作者： Jamshaid Iqbal Janjua Taher M. Ghazal Walid Abushiba Sagheer Abbas Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia College of Engineering Applied Science University Bahrain Department of Computer Science PMU Al Khobar Saudi Arabia

ISBN: (数字)9798350365771

ISBN: (纸本)9798350365788

Healthcare's integration with the emerging artificial intelligence, predictive analytics and health information exchange (HIE) system is currently undergoing a revolution. These emerging technologies are offering better diagnostic accuracies, more personalized treatments, and better patients' outcomes. Artificial Intelligence supported by data from either Electronic Health Records or other big data sources are increasing the accuracy of forecasting critical patient conditions such as disease developments, hospital readmission rates, and mortality risks in patients. These AI-empowered models ensure seamless data sharing between the care providers and promote operational interoperability. Challenges to overcome include issues of privacy of the data, the biases of the prediction algorithms, and the proper integration of the model into the currently established operational environments of healthcare providers. With these technologies combined, it will become possible to optimize patient's outcomes with the provision of data to empower real-time medical interventions.

关键词： Data privacy Accuracy Hospitals Mortality Predictive models Prediction algorithms Data models Artificial intelligence Predictive analytics information exchange

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Knowledge System,Ontology,and Knowledge Graph of the Deep-Time Digital Earth(DDE): Progress and Perspective

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Journal of Earth science 2023年第5期34卷 1323-1327页

作者： Xiumian Hu Yiwei Xu Xiaogang Ma Yunqiang Zhu Chao Ma Chao Li Hairong Lü Xinbing Wang Chenghu Zhou Chengshan Wang School of Earth Sciences and Engineering Nanjing UniversityNanjing 210023China State Key Laboratory of Palaeobiology and Stratigraphy Nanjing Institute of Geology and Palaeontology and Center for Excellence in Life and PaleoenvironmentChinese Academy of SciencesNanjing 210008China Department of Computer Science University of IdahoMoscow ID 83844USA Institute of Geographical Science and Natural Resources Chinese Academy of SciencesBeijing 100101China State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation&Institute of Sedimentary Geology&College of Computer Science and Cyber Security Chengdu University of TechnologyChengdu 610059China IUGS Deep-Time Digital Earth Research Center of Excellence Suzhou 215101China Department of Automation Tsinghua UniversityBeijing 100084China School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesBeijing 100083China

THE USE OF KNOWLEDGE GRAPH IN NATURAL science Knowledge graph is a field of Artificial Intelligence(AI)that aims to represent knowledge in the form of graphs,consisting of nodes and edges which represent entities and relationships between nodes respectively(Aidan et al.,2022).Although the knowledge graph was popularized recently due to use of this idea in Google’s search engine in 2012(Amit,2012),its root can be traced back to the emergence of the Semantic Web as well as earlier works in ontology(Aggarwal,2021).

关键词： DDE Google Web Semantic

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Survey on Blockchain-Based Internet of Things security Technologies

Survey on Blockchain-Based Internet of Things Security Techn...

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Smart World Congress (SWC), IEEE

作者： Qingrui Meng Zhihong Sun Zheng Lv Qiang Cheng Wei Liu Yun Chen department of information security Naval University of Engineering Wuhan Hubei China Computer Science and Engineering Wuhan Institute of Technologe Wuhan Hubei China Simulation Training Center Air Force Early Warning Academy Wuhan Hubei China Simulation Training Center Naval University of Engineering Wuhan Hubei China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Internet of Things(IoT) terminals are characterized by large number, wide distribution, complex hierarchical structure, etc., and are often in complex environments, which are more susceptible to attacks than traditional Internet terminal devices, and their security management faces greater challenges. Because blockchain technology has the advantages of being distributed, decentralized, and not easily tampered with, it is very suitable for solving the current security problems faced by the Internet of Things. In this paper, the research on the security aspects of IoT based on blockchain technology in recent years is sorted out, discussing the application of blockchain technology in the field of IoT security from three aspects of privacy protection, identity authentication, and access control, analyzing the technical characteristics, advantages and disadvantages of different technical solutions, and looking forward to the future development of these fields.

关键词： Access control Surveys Privacy security management Authentication Blockchains Internet of Things Protection Faces

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Privacy-Preserving Location information Fusion for Secure Localization in Cyber-Physical Social Systems: A Case of Consumer's Trustworthy Personalized Recommendation

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IEEE Transactions on Consumer Electronics 2024年

作者： Medhane, Darshan Vishwasrao Sangaiah, Arun Kumar Fan, Chun-I National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan SPPU K. K. Wagh Institute of Engineering Education and Research Department of Information Technology Maharashtra Nashik India National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Sunway University School of Engineering and Technology Selangor Petaling Jaya47500 Malaysia Chandigarh University Punjab 140413 India National Sun Yat-sen University Department of Computer Science and Engineering Information Security Research Center Intelligent Electronic Commerce Research Center Kaohsiung804 Taiwan

Cyber-physical social systems (CPSS) have developed significantly in the last decade as a result of their effective computing and communication capabilities. security and safety concerns, yet continue to be the biggest obstacle to the wider acceptance of CPSS in spite of all technological developments. In this work, we investigate the problem of secure localization of roaming users along with preserving their privacy in CPSS through location information fusion. We present a novel idea of privacy preserving location information fusion based on linear combination aggregation and extend it for its use in CPSS ensuring the privacy of anchor's locations and dimensions, and target's location while roaming in CPSS environment. Furthermore, a privacy-preserving secure localization scheme is offered, together with the necessary cryptographic support to ensure the ultimate aim of privacy. In CPSS contexts where real-time location information is crucial for participant's safety, the proposed privacy-preserving secure localization scheme can be implemented. The proposed scheme is assessed through the implementation of a trustworthy personalized consumer recommendation system. The experimental results demonstrate that the proposed method can guarantee high recommendation accuracy and achieve the accuracy and diversity of personalized recommendations under varying recommendation list lengths when compared to other reliable methods for personalized consumer recommendations. © 1975-2011 IEEE.

关键词： Differential privacy

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Multi-Layer Perceptrons and Convolutional Neural Networks Based Side-Channel Attacks on AES Encryption

Multi-Layer Perceptrons and Convolutional Neural Networks Ba...

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International Conference on engineering Technology and Technopreneuship (ICE2T)

作者： Amjed Abbas Ahmed Mohammad Kamrul Hasan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Computer Techniques Engineering Imam Al-Kadhum College (IKC) Baghdad Iraq

ISBN: (数字)9798350304107

ISBN: (纸本)9798350304114

Side - channel attacks (SCA) exploit vulnerabilities introduced by insecure implementations in order to get information about the data that is being processed or the data itself via device leakages. The majority of modern attack techniques use AES systems, and several studies have exploited side-channel leakages. Exploiting leakages for the first and final rounds, often known as outer rounds, is frequently the foundation of the strategy. There is a possibility that outer round leakages cannot be attacked due to inadequate countermeasures or device design. The attacker has to focus their attention on the inner rounds. Deep learning side-channel attacks, whether they include profiling or not, have the potential to recover secret keys stored in cryptographic devices that use masked AES. Mask value profiling is being used in more recent techniques for the extraction of secret keys. The objective of our research is to defeat masked AES by deep learning methods like as multi-layer perceptrons and convolutional neural networks are used. In an environment when profiling is not taking place, we extract sensitive information using DDLA. The HW (Hamming Weight) model is used by our approach in order to carry out DDLA by means of an innovative binary labeling strategy. Using the ChipWhisperer power traces and the ASCAD dataset, we show profiling and non-profiling attacks. The accuracy of key recovery is at its maximum when the AES implementation is either masked or unmasked.

关键词： Deep learning Focusing Side-channel attacks Encryption Convolutional neural networks Labeling Data mining

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An IoT-Based Air Pollution Monitoring System for Smart City

An IoT-Based Air Pollution Monitoring System for Smart City

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computer Systems (ICCS), IEEE International Conference on

作者： Alibek Aubakirov Kanat Makhanov Aliya Issainova Natalya Glazyrina Bibigul Razkhova Dina Satybaldina School of Computer Science Hangzhou Dianzi University Hangzhou China Department of Radio Engineering Electronics and Telecommunications L.N Gumilyov Eurasian National University Astana Kazakhstan Information Security Department L.N. Gumilyov Eurasian National University Astana Kazakhstan Computer and Software Engineering Department L.N Gumilyov Eurasian National University Astana Kazakhstan Department of Artificial Intelligence Technologies) L.N. Gumilyov Eurasian National University Astana Kazakhstan Information Security and Cryptology Research Institute L.N. Gumilyov Eurasian National University Astana Kazakhstan

ISBN: (数字)9798350366341

ISBN: (纸本)9798350366358

In this paper, we present the results of research on the design and development of a gas analytical unit, an electronic nose, with an autonomous power supply, as well as the function of wireless data transmission via the Long-Range Wide Area Networks protocol. We built the device on a circuit board featuring eight gas sensors (MQ series), an STM32F103C8T6 microcontroller with 12-bit analog-to-digital conversion, a RAK3172 wireless data transmission module, voltage dividers, and a USB serial adapter. It also has an HDC2080DMBR temperature and humidity sensor. We evaluated the electronic nose performance towards standard mixtures of carbon dioxide, carbon monoxide, nitrogen dioxide, ammonia, and hydrogen sulfide, which are important analytes in outdoor and indoor air quality monitoring. Multidimensional linear regression methods were employed to build calibration curves for electronic nose sensors, which allowed for converting the measured sensor responses into quantitative values of the concentrations of the analyzed substances. The estimated coefficients of determination, R2, approach 1 indicating a strong correlation between the approximating model and the original data. The multivariate chemiresistive responses were successfully clustered using the k-means algorithm, revealing clear distinctions between sensor data associated with different gas concentrations. The k-NN classifier, applied to the clustered data, achieved 100% accuracy in identifying gas concentrations, underscoring the potential of the proposed methodology for gas sensing applications. We proposed and implemented the architecture of a network of mobile gas analyzers as the Internet of Things devices for environmental monitoring using the VK Cloud Platform.

关键词： Wireless communication Wireless sensor networks Atmospheric measurements Smart cities Clustering algorithms Electronic noses Pollution measurement Classification algorithms Calibration Data communication

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A Secure Deepfake Mitigation Framework: Architecture, Issues, Challenges, and Societal Impact

Cyber Security and Applications

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Cyber security and Applications 2024年 2卷

作者： Wazid, Mohammad Mishra, Amit Kumar Mohd, Noor Das, Ashok Kumar Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun 248 002 India Department of Computer Science and Engineering Graphic Era Hill University Dehradun 248 002 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India

Deepfake refers to synthetic media generated through artificial intelligence (AI) techniques. It involves creating or altering video, audio, or images to make them appear as though they depict something or someone else. Deepfake technology advances just like the mechanisms that are used to detect them. There's an ongoing cat-and-mouse game between creators of deepfakes and those developing detection methods. As the technology that underpins deepfakes continues to improve, we are obligated to confront the repercussions that it will have on society. The introduction of educational initiatives, regulatory frameworks, technical solutions, and ethical concerns are all potential avenues via which this matter can be addressed. Multiple approaches need to be combined to identify deepfakes effectively. Detecting deepfakes can be challenging due to their increasingly sophisticated nature, but several methods and techniques are being developed to identify them. Mitigating the negative impact of deepfakes involves a combination of technological advancements, awareness, and policy measures. In this paper, we propose a secure deepfake mitigation framework. We have also provided a security analysis of the proposed framework via the Scyhter tool-based formal security verification. It proves that the proposed framework is secure against various cyber attacks. We also discuss the societal impact of deepfake events along with its detection process. Then some AI models, which are used for creating and detecting the deepfake events, are highlighted. Ultimately, we provide the practical implementation of the proposed framework to observe its functioning in a real-world scenario. © 2024 The Authors

关键词： Artificial Intelligence (AI) Authentication Cyber security Deepfake Machine learning

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Live Demonstration: Low Cost AES-256 Circuit Design and Application

Live Demonstration: Low Cost AES-256 Circuit Design and Appl...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Chia-Chou Chuang Yu-Lun Jheng Chien Huang Lu-Ying Huang Pei-Yin Chen Narn-Yih Lee Program on Integrated Circuit Design Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Network & Information Security Division Computer and Network Center National Cheng Kung University Tainan Taiwan

ISBN: (数字)9798350378771

ISBN: (纸本)9798350378788

With the advent of the post-quantum era, the most worrying issue is information security. Quantum computers have been able to crack common asymmetric cryptography, such as RSA, DSA, ECC, etc, by Shor’s Algorithm. In terms of common symmetric-key algorithm, Grover’s algorithm can reduce the difficulty of brute-force cracking from N attempt to $\sqrt{N}$ [1], which decreases the security. In other words, the symmetric-key algorithm doubling the key size can effectively resist quantum attacks. Hence, AES-256 will be a worthwhile research topic in the future.

关键词： computers Quantum computing Costs Circuits and systems Asia information security Resists Elliptic curve cryptography Circuit synthesis Cryptography

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Success Probability and Spectral Efficiency Estimation Using Successive Interference Cancellation for B5G Network

Success Probability and Spectral Efficiency Estimation Using...

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Computing (ICOCO), IEEE International Conference on

作者： Faizan Qamar Anand Gachhadar Syed Hussain Ali Kazmi Rosilah Hassan Center for Cyber Security Faculty of Information Science and Technology (FTSM) Universiti Kebangsaan Malaysia (UKM) UKM Bangi Selangor Malaysia Department of Electronics and Computer Engineering Institute of Engineering Tribhuvan University Kathmandu Nepal Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia

This research paper investigates the examination of Successive Interference Cancellation (SIC) implementation within ultra-dense 5G networks. The study focuses on the theoretical foundations of SIC, encompassing its system and channel models modified for Ultra-Dense Networks (UDNs). A mathematical model is formulated to assess critical network performance metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), Success Probability and Spectral Efficiency after the application of SIC in the network. The effectiveness of SIC is evaluated through simulations and compared against existing interference mitigation techniques like Zero Forcing. The simulations results show that the proposed model has potential to subdue the interference-related issues, such as improved success and probability. By knowing the capabilities and limitations of SIC, network operators can make informed decisions to optimize network performance and provide enhanced user experiences. Through an exploration of SIC's principles and advantages, this study aims to shed light on its potential for effectively mitigating interference and improving the Success Probability and Spectral Efficiency of B5G networks in real-world scenarios.

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Cybersecurity Revolution via Large Language Models and Explainable AI

Cybersecurity Revolution via Large Language Models and Expla...

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security of information and Networks (SIN), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Anuam Ihsan Nidal A. Al-Dmour College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Engineering College of Engineering Mutah University Jordan

ISBN: (数字)9798331509736

ISBN: (纸本)9798331509743

Integrating Groundbreaking advancements in AI, like language models, interpretable AI, and machine learning, opens up a world of exciting new possibilities. The Evolving face of cybersecurity and Modern cyber threats are complex and well crafted; hence, conventional cybersecurity mechanisms show difficulty in staying relevant. LLMs, especially based on Transformer architecture will noticeably increase the accuracy and speed of detecting threats. Transparency and trust are increased by XAI approaches like SHAP and LIME, which offer facts about ML model predictions. This paper explores the literature that demonstrates the integration between XAI and LLMs in cybersecurity, exemplifying how this trinity of models has the potential to help attenuate errors producing reduced false positives and improve how we detect threats. Thinking about the possibilities the challenges including performance Explainability trade-offs, the need for common evaluation metrics, and the black-box nature of AI Models, remain in place. Solving these will help to enhance AI-driven solutions in cybersecurity.

关键词： Measurement Machine learning algorithms Explainable AI Large language models Predictive models Transformers Threat assessment computer security computer crime Faces

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