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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Shujie, Zhou School of Computer Science and Engineering School of Cyber Security University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Path planning in large, complex environments poses significant challenges, as a single robot must efficiently navigate to its target while avoiding obstacles. Traditional algorithms often struggle to balance computational efficiency and planning accuracy. Reinforcement learning methods, while promising, frequently suffer from convergence issues due to difficult-to-tune hyperparameters and sparse reward signals. This paper introduces a Proximal Policy Optimization (PPO)-based path planning approach with a dense bootstrap reward mechanism. The proposed method enhances learning efficiency and ensures smooth navigation by incorporating a structured reward design, significantly improving the agent's path-planning performance in complex scenarios. We conducted simulation experiments in a classical warehouse environment to demonstrate the effectiveness and superiority of our proposed method. Compared to traditional rewards, our method significantly improves the pathfinding efficiency of deep reinforcement learning algorithms. © 2024 IEEE.

关键词： Deep reinforcement learning

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The Role of AI in Vehicle-to-Infrastructure Communication for Tolling Systems

The Role of AI in Vehicle-to-Infrastructure Communication fo...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Mahalakshmi, S. Baskaran, Nithiya Partheeban, Pachaivanna Krishnara, Kowsalyadevi Arjun, S. Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur Chennai India Chennai Institute of Technology Department Cyber Security Kundrathur Chennai India Chennai Institute of Technology Department Civil EngDepartment Cyber Security Kundrathur Chennai India Sona Institute of Technology Department of Cyber Security Salem India

ISBN: (纸本)9798331535476

The number of vehicles people use is substantial;thus, surveillance must be accurate. License plates are detected and recognized by detecting static and real-time images captured by the camera using OCR technology, computer vision, and AI in the tool booth and other important roads. This model can be used for many things, such as automated services, increased security, determining whether a car is registered, and identifying and validating vehicles connected to traffic violations. Real-time investigations are part of the automated recognition of license plates and their application to other traffic control and highway surveillance aspects. To detect plates from the images and in real-time video, Use Easy OCR with the license plates to read the plate number, utilize tensor flow object detection to detect and recognize, and save the detected license plate for further analysis and search. © 2024 IEEE.

关键词： Highway traffic control

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MODELGUARD: Information-Theoretic Defense Against Model Extraction Attacks 33

MODELGUARD: Information-Theoretic Defense Against Model Extr...

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33rd USENIX security Symposium, USENIX security 2024

作者： Tang, Minxue Dai, Anna DiValentin, Louis Ding, Aolin Hass, Amin Gong, Neil Zhenqiang Chen, Yiran Li, Hai Department of Electrical and Computer Engineering Duke University United States Cyber Security Lab Accenture

ISBN: (纸本)9781939133441

Malicious utilization of a query interface can compromise the confidentiality of ML-as-a-Service (MLaaS) systems via model extraction attacks. Previous studies have proposed to perturb the predictions of the MLaaS system as a defense against model extraction attacks. However, existing prediction perturbation methods suffer from a poor privacy-utility balance and cannot effectively defend against the latest adaptive model extraction attacks. In this paper, we propose a novel prediction perturbation defense named MODELGUARD, which aims at defending against adaptive model extraction attacks while maintaining a high utility of the protected system. We develop a general optimization problem that considers different kinds of model extraction attacks, and MODELGUARD provides an information-theoretic defense to efficiently solve the optimization problem and achieve resistance against adaptive attacks. Experiments show that MODELGUARD attains significantly better defensive performance against adaptive attacks with less loss of utility compared to previous defenses. © USENIX security Symposium *** rights reserved.

关键词： Differential privacy

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FEDA: Private-Preserving Data Aggregation for Smart Grid via Functional Encryption 21

FEDA: Private-Preserving Data Aggregation for Smart Grid via...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Zhenjiang, Li Hongwei, Li Xinyuan, Qian School of Computer Science and Engineering School of Cyber Security University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Privacy-preserving data aggregation (P2DA) is a technique that enables the simultaneous availability of data and the preservation of privacy in smart grid systems. However, existing methods are only capable of fulfilling a subset of the essential criteria for the development of smart grids. The current methodologies are based on a number of strong assumptions, or else they result in an increase in the computational and communication overhead. To address this challenge, this paper introduces FEDA, a P2DA scheme based on functional encryption for smart grids, offering efficient encryption and decryption processes, along with verifiable aggregation. The results of our experiments demonstrate that our approach markedly reduces the computational and communication overheads in comparison to existing methods. Furthermore, it enhances the encryption speed by approximately eightfold and improves the aggregation and decryption efficiencies while maintaining a high level of security. © 2024 IEEE.

关键词： Data Aggregation Functional Encryption Privacy-preserving Smart Grid Verification

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Coronary Heart Disease Prediction Using CNN, RNN, LSTM and Attention DNN Model

Coronary Heart Disease Prediction Using CNN, RNN, LSTM and A...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Dhawale, Hrutuja Thakare, Dhanashree Dhule, Chetan Agrawal, Rahul Morris, Nekita Chavan IOT Cyber Security GH Raisoni College of Engineering Department of Data Science Nagpur India

ISBN: (纸本)9798331512965

Coronary Heart Disease popularly referred to as CHD is one of the leading causes of death and illness across the global population making it imperative for the identification of an effective approach to its predictive modeling for early detection. The purpose of this research includes studying a dataset containing a variety of clinical characteristics in order to forecast CHD using sophisticated deep learning structures. Specifically, we implemented and evaluated four distinct models: Convolutional Neural Networks, Long Short-Term Memory networks, Recurrent Neural Networks and Attention Deep Neural Networks (DNN). All models are trained and evaluated on data set and multiple performance measures like accuracy, recall, precision, and F1score were used for the purpose. The experimental outcome showed that the Attention-based DNN had the highest accuracy rate of 91 % followed by the RNN with accuracy rate of 89%. CNN and LSTM models gave moderate accuracy but they did not outperform the best models among options. © 2025 IEEE.

关键词： Convolutional neural networks

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Machine Learning for Threat Detection

Machine Learning for Threat Detection

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Sutanto, Dustin Fransisca, Febri Liem, Steven Chowanda, Andry Muliono, Yohan Izdihar, Zahra Nabila School of Computer Science Cyber Security Program Computer Science Department Bina Nusantara Jakarta Indonesia School of Computer Science Computer Science Program Computer Science Department Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9798350364910

These days almost all people in the world use the Internet as the internet is constantly evolving. cyber attack scale are increased thanks to cybercriminals that have become sophisticated in employing threats. Since the 1980s, Intrusion Detection Systems (IDS) have been developed to defend computer networks against malicious activity and unauthorized access. Companies choose to implement IDS the part of their cyber security strategy to protect their data and network from potential threats. in this case, IDS has played a crucial role in detecting abnormal traffic and protecting digital assets. furthermore, a high-accuracy IDS is needed in concern of having false positive feedback from the IDS machine itself that can cause various problems. to accompany the need for high accuracy and trainable IDS machines, we experimented with a hybrid system using machine learning that is combined with traditional IDS machines resulting in higher accuracy and lower error rate. By adjusting the parameter the accuracy improved and could achieve an accuracy of 99.66% of classifying the traffic. © 2024 IEEE.

关键词： Network intrusion

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Forward Legal Anonymous Group Pairing-Onion Routing for Mobile Opportunistic Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6595-6612页

作者： Zhu, Xiuzhen Lin, Limei Huang, Yanze Wang, Xiaoding Hsieh, Sun-Yuan Wu, Jie Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan Temple University Department of Computer and Information Science 1925 N. 12th St PhiladelphiaPA19122 United States

Mobile Opportunistic Networks (MONs) often experience frequent interruptions in end-to-end connections, which increases the likelihood of message loss during delivery and makes users more susceptible to various cyber attacks. However, most currently proposed anonymous routing protocols are primarily designed for networks with stable connections, making it challenging to protect user identities in MONs. To address these challenges, we propose FLAG-POR (Forward Legal Anonymous Group Pairing-Onion Routing), a novel anonymous routing protocol specifically tailored to enhance message delivery anonymity and security in MONs. Specifically, we abstract the mobile opportunistic network as a contact graph. By introducing the concept of "groups» into the pairing-onion routing protocol, which encrypts messages and relay nodes layer by layer, we develop a novel group-based pairing-onion routing protocol. This protocol ensures message confidentiality and relay node anonymity, while also improving message forwarding rates, as any node within a group can potentially act as a relay. To ensure message authenticity, we employ the efficient SM2 signing algorithm to generate signatures for the message source. Furthermore, by incorporating parameters such as the public key validity period and master key validity period into the group pairing-onion routing protocol, we achieve forward security in message delivery. We conduct a thorough theoretical analysis of the protocol's security and performance. The experimental results demonstrate that our FLAG-POR protocol outperforms baseline anonymous protocols in terms of delivery success rate, traceability rate, path anonymity, and node anonymity. Additionally, the FLAG-POR scheme effectively resists three potential threats to the routing system: collusion attack threat, node identification threat, and path identification threat, in any situation. © 2002-2012 IEEE.

关键词： Anonymity

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security Challenges and Issues in Cloud Computing

Security Challenges and Issues in Cloud Computing

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Zakiyyah, Rika Permana, Daniel Valenecio, Devan Chowanda, Andry Muliono, Yohan Izdihar, Zahra Nabila Cyber Security Program Computer Science Department School of Computer Science Bina Nusantara Jakarta Indonesia Computer Science Program Computer Science Department School of Computer Science Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9798350364910

Managing data has changed significantly because of cloud computing, which offers scalabe, flexible and reasonably priced solutions to enterprises and to people as well such as Amazon, Google, and Microsoft expanding their infrastructure to meet the demand from the customer. With such advantages there will also be disadvantages such as security problems. This paper will discuss about security problems such as encryption, access control, multi-factor authentication, regular audits, secure configurations, incident response planning. This paper will also talk about security measures to deal with threats such as malicious insiders, data abuse, unsecured interfaces and APIs, shared technology complications, data loss or leakage, hijacking, and enigmatic risk profiles. This study examines and evaluates a range of cloud computing security concerns and issues through a systematic analysis of the literature. The goal of the research is to raise public awareness of the need for cloud computing security and to offer possible remedies. Identify and classify network security threats and vulnerabilities in cloud computing by doing an extensive examination of the body of research and empirical investigations is one of the answer to the questions from the paper. In the end, this take a look at unearths vulnerabilities and their practical implications through very well reading network safety in cloud computing. © 2024 IEEE.

关键词： Cloud security

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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