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IEEE International Conference on e-Business engineering (ICEBE)

作者： Yi-Ju Cho Shin-Jie Lee Department of Computer Science and Information Engineering National Cheng Kung University Tainan City Taiwan Department of Computer Science and Information Engineering/Computer and Network Center National Cheng Kung University Tainan City Taiwan

A significant proportion of web elements might manifest identical behaviors and functionalities, further complicating the test case creation process. We have noted that elements demonstrating congruent behavior and functionality frequently exhibit visual resemblances within the web page interface. In this paper, we propose a machine learning-based method to identify visually similar web elements. These identified elements can be brought to the attention of the tester. If the tester determines that these elements exhibit similar behavior, they can be visually marked on the web page, facilitating focused test case creation. A case study demonstrates its potential to make accurate judgments on several complex webpages.

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The Principles of Information Security and the Challenges Facing the Internet of Things in Developing Countries

The Principles of Information Security and the Challenges Fa...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Mohammed Abdulwahed M. Abdu Abdullah Qaid Mohammed Murshed Akram Saeed Aqlan Alhammadi IT Department Alsaeed College for Engineering & IT Taiz University Taiz Yemen Computer network and cyber security Department Alsaeed College for Engineering & IT Taiz University Taiz Yemen

The modern age is witnessing a significant development in information technology. The Internet of Things (henceforth IoT) technology is one such advancement. Since its emergence, it has affected people's life positively. For instance, it has provided many services to people, made their life easier and reduced effort with more accurate information in concurrent time. Although IoT technology has considerably advanced since its inception, it still faces many challenges. The biggest of which is how to protect the privacy and confidentiality of data. Consequently, information security challenges have increased to keep up with IoT technology's large amount of information. In this paper, the researchers collected, analysed and summarised reliable, relevant sources to obtain information that addressed various security threats. This paper, therefore, sheds light on the most prominent challenges facing information security in the revolution of IoT in developing countries.

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Lattice-Based Cryptography and NTRU: Quantum-Resistant Encryption Algorithms

Lattice-Based Cryptography and NTRU: Quantum-Resistant Encry...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： F. Nisha J. Lenin S.K. Saravanan V. Robin Rohit P. D. Selvam M. Rajmohan School of Engineering and Technology Computer Science & Engineering CMR University Bengaluru Karnataka India Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Bengaluru Karnataka India Department of BigData and Network Security Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Bengaluru Karnataka India Department of Electronics and Communication Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

The quantum-resistant qualities of various encryption methods, such as N-th degree Truncated polynomial Ring Units (NTRU) Encrypt, NTRU Sign, Ring-Lizard, and Kyber protocols are becoming more important in light of the ever-evolving nature of digital threats. An-depth analysis of the mathematical derivation of these algorithms, as well as a determination of their computing efficiency and resistance to quantum assaults is discussed in this paper. Techniques of the Ring-Lizard and Kyber algorithms to lattice-based encryption are analyzed. The study gives an overview of quantum-resistant capabilities of these algorithms and their performance measures. Strengths and weaknesses of the NTRU Encrypt, NTRU Sign, Ring-Lizard, and Kyber algorithms are discussed. The results give useful insights for cybersecurity practitioners, academics, and policymakers, aiding them in making educated choices to protect digital infrastructures against quantum attacks.

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Zero-Csc: Low-Light Image Enhancement with Zero-Reference Color Self-Calibration

SSRN

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SSRN 2024年

作者： Jin, Haiyan Chen, Yujia Zuo, Fengyuan Su, Haonan Zhang, YuanLin Department of Computer Science and Engineering Xi’an University of Technology Xi’an 710048 China Shaanxi Key Laboratory for Network Computing and Security Technology

Zero-reference low-light image enhancement (LLIE) techniques mainly focus on grey-scale inhomogeneities, and few approaches consider how to explicitly recover a given dark scene for achieving color and holistic illuminance enhancement. In this paper, we propose a novel Zero-Reference Color Self-Calibration framework for low-light image enhancement, namely Zero-CSC, that effectively accentuates channel-wise representations that contain fine-grained color information, achieving a natural result in a progressive manner. Furthermore, a novel Light Up (LU) module with large-kernel convolutional blocks is proposed to improve the holistic illuminance, which is implemented with a simple U-Net. The experiments are conducted on representative datasets, showing that our model consistently achieves SOTA performance on all datasets with the same architecture. © 2024, The Authors. All rights reserved.

关键词： Color

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More simplicity for trainers, more opportunity for attackers: black-box attacks on speaker recognition systems by inferring feature extractor 24

More simplicity for trainers, more opportunity for attackers...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yunjie Ge Pinji Chen Qian Wang Lingchen Zhao Ningping Mou Peipei Jiang Cong Wang Qi Li Chao Shen School of Cyber Science and Engineering Wuhan University School of Cyber Science and Engineering Wuhan University and Department of Computer Science City University of Hong Kong Department of Computer Science City University of Hong Kong Institute of Network Sciences and Cyberspace Tsinghua University School of Cyber Science and Engineering Xi'an Jiaotong University

ISBN: (纸本)9781939133441

Recent studies have revealed that deep learning-based speaker recognition systems (SRSs) are vulnerable to adversarial examples (AEs). However, the practicality of existing black-box AE attacks is restricted by the requirement for extensive querying of the target system or the limited attack success rates (ASR). In this paper, we introduce VoxCloak, a new targeted AE attack with superior performance in both these aspects. Distinct from existing methods that optimize AEs by querying the target model, VoxCloak initially employs a small number of queries (e.g., a few hundred) to infer the feature extractor used by the target system. It then utilizes this feature extractor to generate any number of AEs locally without the need for further queries. We evaluate Vox-Cloak on four commercial speaker recognition (SR) APIs and seven voice assistants. On the SR APIs, VoxCloak surpasses the existing transfer-based attacks, improving ASR by 76.25% and signal-to-noise ratio (SNR) by 13.46 dB, as well as the decision-based attacks, requiring 33 times fewer queries and improving SNR by 7.87 dB while achieving comparable ASRs. On the voice assistants, VoxCloak outperforms the existing methods with a 49.40% improvement in ASR and a 15.79 dB improvement in SNR.

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Securing Smart Grids: Deep Reinforcement Learning Approach for Detecting Cyber-Attacks

Securing Smart Grids: Deep Reinforcement Learning Approach f...

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International Conference on Smart Communications and networking (SmartNets)

作者： Ahmed T. El-Toukhy Islam Elgarhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Mohamed I. Ibrahem Fathi Amsaad Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Electrical Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Egypt Department of Network and Computer Security: Cybersecurity SUNY Polytechnic Institute Utica NY USA Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Computer Science and Engineering Wright State University Dayton OH USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Smart meters (SMs) are deployed in smart power grids to monitor customer power consumption and facilitate energy management. However, fraudulent customers can compromise these SMs to manipulate power readings and engage in electricity theft cyber-attacks, resulting in reduced electricity bills. While various machine learning approaches have been employed for detecting such attacks, the potential of reinforcement learning (RL) remains unexplored. To bridge this gap, we propose a deep reinforcement learning (DRL) approach that leverages RL's adapt-ability to dynamic cyber-attacks and consumption patterns. This approach integrates exploration and exploitation mechanisms, enabling optimal decision-making. In this study, we present our approach in two scenarios. Firstly, we develop comprehensive detection models using deep Q networks (DQN) and double deep Q networks (DDQN) with various deep neural network architectures. Secondly, we address the challenges of defending against newly launched cyber-attacks. Extensive experimentation provides strong evidence of the effectiveness of our DRL approach in improving the detection of electricity theft cyber-attacks, as well as its capacity to efficiently adapt and defend against newly launched cyber-attacks.

关键词： Adaptation models Power demand Electricity Power system dynamics Detectors Deep reinforcement learning Smart meters

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An Accurate and Wide-Range AFC for Modified Walsh-Hadamard Code Division Multiplexing

An Accurate and Wide-Range AFC for Modified Walsh-Hadamard C...

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International Conference on Advanced Technologies for Communications, ATC

作者： Toshiharu Kojima Koki Iwabuchi Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo Japan

This paper proposes a novel automatic frequency control (AFC) for modified Walsh-Hadamard code division multiplexing (MWHCDM). While MWHCDM is suitable for helicopter satellite communications, where rotor blades periodically block the transmission channel, it is affected by a time-varying Doppler shift due to helicopter maneuvers. The conventional AFC has a problem where its pull-in range is significantly narrower than the fluctuation range of the Doppler shift. The proposed AFC utilizes the structure of MWHCDM signals, and its operating range covers the fluctuation range with enough margin. computer simulation shows that the proposed AFC outperforms the conventional one on both operating/pull-in range and frequency estimation accuracy.

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Study of Whisker Growth from SAC0307-SiC Composite Solder Alloy 46

Study of Whisker Growth from SAC0307-SiC Composite Solder Al...

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46th International Spring Seminar on Electronics Technology, ISSE 2023

作者： Choi, Halim Skwarek, Agata Hurtony, Tamás Byun, Jaeduk Illés, Balázs Budapest University of Technology and Economics Faculty of Electrical Engineering and Computer Science Department of Electronics Technology Budapest Hungary Lukasiewicz Research Network - Institute of Microelectronics and Photonics Department of Microelectronics Kraków Poland Dankook University College of Science & Technology Department of Physics Cheonan Korea Republic of

ISBN: (纸本)9798350334845

In the current study, tin (Sn) whisker growth was observed on the surface of the SAC0307-SiC composite solder joint. A commercial SAC0307 solder alloys were reinforced with SiC nanophases to produce composite solder joints. SiC nanoparticles and nanowires were mixed with SAC0307 solder paste using the ball milling method. The solder paste was deposited by stencil printing on the test PCBs, and 0805 chip resistors were soldered by IR reflow method. The solder joints facilitated the growth of the Sn whisker through accelerated lifetime tests. The samples were observed by scanning electron microscopy (SEM) every 1000 hours of the test. On the SiC composite solder joints, Sn whiskers and large corrosion spots were observed after 1000 hours of the THB test. In the case of SAC0307 reference samples, only minor corrosion signs were found after 1000 hours of the THB test. The main finding was that the addition of SiC nanophases promoted Sn whisker growth and decreased the corrosion resistance of the composite solder. © 2023 IEEE.

关键词： Silicon carbide

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Multi-agent Reinforcement Learning Based Delay and Power Optimization for UAV-WMN Substation Inspection

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IEEE Transactions on network and Service Management 2025年

作者： Tang, Qingwei Sun, Wei Liu, Zhi Xiao, Yang Li, Qiyue Yuan, Xiaohui Zhang, Qian Hefei University of Technology School of Electrical and Automation Engineering Anhui Hefei230009 China Anhui Engineering Technology Research Center of Industrial Automation 193 Tunxi Rd Hefei230009 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan The University of Alabama Department of Computer Science TuscaloosaAL35487 United States University of North Texas Department of Science and Engineering DentonTX76207 United States

Unmanned aerial vehicles (UAV), due to their flexibility and extensive coverage, have gradually become essential for substation inspections. Wireless mesh networks (WMN) provide a scalable and resilient network environment for UAVs, where each node can serve as either an access point or a relay point, thereby enhancing the network’s fault tolerance and overall resilience. However, the UAV-WMN combined system is complex and dynamic, facing the challenge of dynamically adjusting node transmission power to minimize end-to-end (E2E) delay while ensuring channel utilization efficiency. Real-time topology changes, high-dimensional state spaces, and large solution spaces make it difficult for traditional algorithms to guarantee convergence and stability. Generic reinforcement learning (RL) methods also struggle with stable convergence. This paper introduces a new Lyapunov function-based proof to address these issues and provide a stable condition for dynamic control strategies. Then, we developed a specialized neural network power controller and combined it with the MATD3 algorithm, effectively enhancing the system’s convergence and E2E performance. Simulation experiments validate the effectiveness of this method and demonstrate its superior performance in complex scenarios compared to other algorithms. © 2004-2012 IEEE.

关键词： Lyapunov functions

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Fine Classification of PolSAR Land Cover Types Based on Multi-Band

Fine Classification of PolSAR Land Cover Types Based on Mult...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fangzhou Han Tianci Liu Lamei Zhang Shang Fang Dept.of Information Engineering Harbin Institute of Technology Harbin China Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan

Considering the distinct scattering characteristics observed in diverse frequency bands for terrain surfaces, the utilization of complementary information from multi-band PolSAR data proves advantageous in effectively distinguishing targets that may present challenges when assessed in isolation within a single band. In this study, we employ the polarimetric target decomposition methods to investigate the scattering characteristics of representative targets within the C and L bands. Based on these findings, we construct a random forest fine classification model specifically tailored for different types of urban vegetation. Notably, experimental results demonstrate a noteworthy enhancement in classification accuracy following the integration of multi-band data.

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