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A Novel Probabilistic Saturating Counter Design for Secure Branch Predictor

Journal of computer Science & technology 2021年第5期36卷 1022-1036页

作者： Lu-Tan Zhao Rui Hou Kai Wang Yu-Lan Su Pei-Nan Li Dan Meng State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences Beijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 100093China Department of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

In a modern processor,branch prediction is crucial in effectively exploiting the instruction-level parallelism for high-performance ***,recently exposed vulnerabilities reveal the urgency to improve the security of branch *** vital cause of the branch predictor vulnerabilities is that the update strategy of the saturating counter is *** a fundamental building block in a modern branch predictor,previous studies have paid too much attention to the performance and hardware cost and ignored the security of saturating *** leaves attackers with the opportunities to perform side-channel attacks on the branch *** paper focuses on the saturating counter to explore a secure and lightweight design to mitigate branch predictor side-channel *** of applying the isolation mechanism to branch predictor resources,we propose a novel probabilistic saturating counter design to confuse the attacker's perception of the victim's *** changes the conventional deterministic state transition function to a probabilistic state transition *** a branch is committed,the conventional saturating counter needs to be updated about whether the prediction results are correct or *** for the probabilistic saturating counter,the branch predictor determines whether the update is performed based on the update *** probabilistic saturating counter dramatically reduces the ability of the attacker to spy the saturating counter's *** analyses using a cycle-accurate simulator suggest that the proposed mechanism incurs 2.4%performance overhead and hardware cost while providing strong protection.

关键词： branch predictor side-channel attack saturating counter

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A Review of Privacy-Preserving Authentication Schemes for Future Internet of Vehicles 12

A Review of Privacy-Preserving Authentication Schemes for Fu...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Chavali, Sruthi Cheema, Hashim Delgado, Ruth Nolan, Emily Ibrahem, Mohamed I. Fouda, Mostafa M. George Mason University Department of Cyber Security Engineering VA Fairfax United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Faculty of Engineering at Shoubra Benha University Department of Electrical Engineering Cairo11672 Egypt

ISBN: (纸本)9781665462617

The Vehicle to Grid (V2G) network relationship is complex and contradictory in nature. For the grid to authenticate electric vehicles (EVs) and allow them to enter safely, they need a certain set of information that is personal to the EV. Unfortunately, as this information is often transmitted, it can be intercepted by unauthorized or malicious viewers, leaving EVs in a vulnerable position. In this paper, we explore existing solutions proposed in the literature to address these concerns. Authentication mechanisms are often combined with a key exchange between local aggregators (LAGs) and EVs, without the use of pseudonyms, to reduce the overhead associated with authentication. This scheme ensures anonymity, confidentiality, unlinkability, nonrepudiation, traceability, and revocation. Further, physical unclonable functions can be used to reduce the overhead of authentication. A three-layered infrastructure can also be used in which an EV only needs to authenticate once when entering a section of the grid. We conclude by providing a comparison of current works and making suggestions for further study to enhance the security of future electric vehicle transportation systems. © 2023 IEEE.

关键词： Authentication

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Statistical Approach of Big Data Analytics in Health care

Statistical Approach of Big Data Analytics in Health care

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Deepak P B. Sanjai Prasada Rao M SilpaRaj DBK Kamesh Balaram Allam Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Computer Science and Engineering (Cyber Security) CVR College of Engineering Hyderabad Department of CSE Malla Reddy Engineering College for Women Maisammaguda

Scientific statistics systems, virtual scientific records, wearables, smart devices, and handhelds have altered healthcare. Academics and practitioners can extract and visualise clinical vast data in creative ways using medical massive statistics and healthcare computational approaches. Medical data from EHRs or patients is one of the fastest-growing statistics. EMRs store medical data in different formats. Professional carers base clinical judgements on symptoms and treatments. Build a huge, high-quality graph relating patients, conditions, and medicines in electronic medical data to capture the relationship. Clinical data is growing rapidly, therefore you need tools and ways to manage and extract value and expertise from it to enhance patient care and minimise healthcare costs. This study proves extemporaneous healthcare statistics with large data. Advanced analytics on massive data can also be used by eternal statistics analytics.

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Influence of Access Points’ Height and High Signal Relation in WLAN Fingerprinting-Based Indoor Positioning Systems’ Accuracy 1

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26th International Conference on Distributed computer and Communication Networks: Control, Computation, Communications, DCCN 2023

作者： Nicholaus, Mrindoko R. Ruambo, Francis A. Masanga, Elijah E. Muthanna, Mohammed Saleh Ali Lashchev, Andrei Computer Science and Engineering Department College of Information and Communication Technology Mbeya University of Science and Technology 131 Mbeya Tanzania United Republic of Information Systems and Technology Department College of Information and Communication Technology Mbeya University of Science and Technology 131 Mbeya Tanzania United Republic of School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Research and Development Department Aifrruis Laboratories Mbeya Tanzania United Republic of Institute of Computer Technologies and Information Security Southern Federal University Taganrog347922 Russia

ISBN: (数字)9783031504822

ISBN: (纸本)9783031504815

Wireless Local Area Network (WLAN) Fingerprinting-based Indoor Positioning Systems (IPS) offer several key advantages over other indoor positioning technologies, including cost-effectiveness with high accuracy and passive tracking of entities’ location without Global Positioning System (GPS) dependence. However, achieving a satisfactory accuracy performance of the WLAN fingerprinting-based IPS is still challenging, as it is affected by several factors, such as obstacles (e.g., walls and furniture) that can block or weaken wireless signals. Additionally, the IPS’s accuracy can be affected by environmental changes, such as the height, addition, or removal of a WLAN Access Point (AP) or changes in radio frequency interference. This paper presents a novel algorithm incorporating a probabilistic analytical model employing only high signal relations that mitigate the effect of low signal relation on the WLAN fingerprinting-based IPS, thus improving the accuracy performance. Furthermore, it analyses the impact of AP heights on the accuracy performance of the WLAN IPS. Upon extensive experiments, the proposed algorithm improves the accuracy of the IPS by an average of 32.74% in terms of Root Mean Square Error (RMSE). These results imply that incorporating APs’ height and high signal relations can significantly improve the accuracy performance of the WLAN fingerprinting-based IPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cost effectiveness

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Empowering cyber Defense by Unveiling AI-Powered Expert Systems and Software engineering 2

Empowering Cyber Defense by Unveiling AI-Powered Expert Syst...

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2nd International Conference on Foundation and Large Language Models, FLLM 2024

作者： Tawalbeh, Lo'Ai Franklin, John Ramirez, Christian Garcia, Jonathan Tawalbeh, Abdelrahman Muheidat, Fadi Jordan University of Science and Technology Department of Computer Engineering Irbid22110 Jordan Texas A&M University Department of Computing and Cyber Security San AntonioTX78224 United States Johnson High School San AntonioTX78259 United States School of Computer Science and Engineering California State University San BernardinoCA92407 United States

ISBN: (纸本)9798350354799

People all over the world are subject to cyber-attacks. The abundance of information on the internet gives hackers the tools to exploit vulnerabilities in computer systems, from home desktops to the most considerable servers hosted by organizations. cyber security is a practice dedicated to defending electronic devices from cybercrime. Top cyber security practices apply expert systems that can detect patterns and adapt to changing variables in hacking methods. An expert system in computing is a complex program that utilizes artificial intelligence (AI) to simulate human tasks or calculate intricate functions. Artificial Intelligence techniques can be used to determine any abnormal activities and unusual data traffic in computer network which can be utilized to protect these networks against any malicious activities. Today, organizations seek the latest expert systems for cyber defense. This paper aims to describe the software engineering behind artificial intelligence and how it supports expert machines used in cyber defense systems. The intricate functions of artificial intelligence in cyber security are discussed, such as deep learning, intrusion detection trees, natural language processing, and artificial immune systems. © 2024 IEEE.

关键词： cyber attacks

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A Unified Deep Learning Approach for Integrating Retinal Image Diagnosis and Surgical Tool Classification 1

A Unified Deep Learning Approach for Integrating Retinal Ima...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Jesica, J. Anitha, J. Sujitha Juliet, D. Belfin, R.V. Karunya Institute of Technology and Sciences Dept. of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India University of North Carolina Dept. of Neurology Chapel Hill United States

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Early and accurate diagnosis by using retinal image processing is critical for enabling optimized patient care. Existing techniques for the diagnosis in medical image processing often face limitations. This research study leverages the comparative analysis of advanced Convolutional Neural Networks (CNNs), including AlexNet, InceptionV3, Xception, and EfficientNetB3;out of which EfficientNetB3 gives the comparatively higher accuracy of 84%. This research work presents a novel deep learning-based approach for detecting diseases along with the integration of Lenscraft AI. Lenscraft AI helps to segment surgical masks and identify the surgical tools used in surgical recordings using YOLOv8. The developed tool and tracking method offers a higher precision value of 0.8863 and efficiency in identifying the surgical instruments and potentially improving surgical technique for the trainee surgeons and patient safety. © 2024 IEEE.

关键词： Convolutional neural networks

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Secure and Transparent Smart Agriculture: Integrating Blockchain technology with IoT for Enhanced Data Integrity and Efficiency in Agricultural System

Secure and Transparent Smart Agriculture: Integrating Blockc...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Muruganantham U Chandini P Palanivel N Gubbala Lakshmi Balaji G Sri Kamesh M Sivasanker S Department of CSE (Internet of Things and Cyber Security Including Blockchain Technology) Manakula Vinayagar Institute of Technology Puducherry India Department of Computer Science and Engineering RAAK College of Engineering and Technology Puducherry India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

A viable strategy for improving the integrity of information, safety, and operational effectiveness in contemporary agricultural systems is the combination of blockchain-based technology and the Internet of Things. To simplify agricultural operations, this study suggests an accessible and safe architecture that offers end-to-end verification, continuous surveillance, and trustworthy information sharing across participants. The structure guarantees the precision and permanence of information gathered from IoT devices, including sensors that track temperatures, health of crops, moisture levels in the soil, and machinery efficiency, by utilizing the decentralized record of transactions and cryptographic properties of blockchain technology. Information tampering, illegal access, and inaccuracy are frequent problems in agricultural management of information that are reduced by this open method of processing data. Additionally, by enabling computerized processes like condition-based responses and transactional confirmation, smart contracts increase reactivity and decrease the need for manual involvement. Potential advantages of the proposed structure include cost savings, improved trust between agriculturalists, suppliers, and customers, and resource efficiency. Ultimately, this blockchain-IoT connection strengthens the food supply chain security and supports environmentally friendly methods by creating a more robust, based-on-information farming environment.

关键词： Smart agriculture Temperature sensors Data integrity Scalability Smart contracts Real-time systems Blockchains Safety Internet of Things Farming

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Advantages of Redis in-memory database to efficiently search for healthcare medical supplies using geospatial data 16

Advantages of Redis in-memory database to efficiently search...

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16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022

作者： Muradova, Gulara Hematyar, Mehran Jamalova, Jala Azerbaijan Technical University Department of Computer Technologies and Cyber Security Baku Azerbaijan Azerbaijan State Oil and Industry University Department of Computer Engineering Baku Azerbaijan

ISBN: (纸本)9781665451628

According to diagnostic criteria, a patient can find clinics he needs depending on the symptoms of disease. The paper shows an effective solution that allows rapid access to information about clinic using Redis in memory database technology. In this paper using Redis help us to collect a wide array of geospatial capabilities finding the best way and build out this type of functionality. Redis has the capability to store by Redis intelligent optimized systems in their native format, and update and serve them with minimal computing infrastructure needed to implement these algorithms at scale. Also, our approach is to study the effect of the databases on system's working speed, comparing Redis and MS SQL. © 2022 IEEE.

关键词： Metadata

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FedProphet: Memory-Efficient Federated Adversarial Training via Theoretic-Robustness and Low-Inconsistency Cascade Learning

arXiv

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arXiv 2024年

作者： Tang, Minxue Wang, Yitu Zhang, Jingyang DiValentin, Louis Ding, Aolin Hass, Amin Chen, Yiran Li, Hai Helen Department of Electrical and Computer Engineering Duke University United States Cyber Security Lab Accenture

Federated Learning (FL) provides a strong privacy guarantee by enabling local training across edge devices without training data sharing, and Federated Adversarial Training (FAT) further enhances the robustness against adversarial examples, promoting a step toward trustworthy artificial intelligence. However, FAT requires a large model to preserve high accuracy while achieving strong robustness, and it is impractically slow when directly training with memory-constrained edge devices due to the memory-swapping latency. Moreover, existing memory-efficient FL methods suffer from poor accuracy and weak robustness in FAT because of inconsistent local and global models, i.e., objective inconsistency. In this paper, we propose FedProphet, a novel FAT framework that can achieve memory efficiency, adversarial robustness, and objective consistency simultaneously. FedProphet partitions the large model into small cascaded modules such that the memory-constrained devices can conduct adversarial training module-by-module. A strong convexity regularization is derived to theoretically guarantee the robustness of the whole model, and we show that the strong robustness implies low objective inconsistency in FedProphet. We also develop a training coordinator on the server of FL, with Adaptive Perturbation Adjustment for utility-robustness balance and Differentiated Module Assignment for objective inconsistency mitigation. FedProphet empirically shows a significant improvement in both accuracy and robustness compared to previous memory-efficient methods, achieving almost the same performance of end-to-end FAT with 80% memory reduction and up to 10.8× speedup in training time. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Secure Access Control Schemes for Cloud-Based Networks 15

Secure Access Control Schemes for Cloud-Based Networks

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sreeramulu, Madhusudhan Dasari Gopal, Santhosh Kumar Mohammed, Abdul Sajid Sathish Kumar, D. American Express Dept of Cyber Security United States Technology Lead CharlotteNC United States University of the Cumberlands Dept of School of Computer and Information Sciences KY United States Sri Eshwar College of Engineering Department of ECE Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350370249

This abstract provides a state-of-the-art review of secure access control schemes for cloud networks. The rise of cloud computing and the security of the cloud have increased the need for robust access control for securing data and resources. In this paper, role-based, attribute-based, multi-factor authentication access control methods and their strengths and weaknesses are addressed. It goes further to tackle some of the challenges of using these schemes in a cloud environment and offers solutions for the same. Following that, it will focus on what the research means for future directions in this field, in terms of the security of cloud networks & beyond. © 2024 IEEE.

关键词： Cloud security

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