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18th IFIP WG 11.12 International Symposium on Human Aspects of Information Security and Assurance, HAISA 2024

作者： Große, Christine Division of Computer Science Electrical and Space Engineering Department of Digital Services and Systems Luleå University of Technology Luleå971 87 Sweden

ISBN: (纸本)9783031725586

The ability to handle threats, such as disinformation, manipulation of public opinion, and disruption of critical supplies, is becoming increasingly important, thus, necessitating, among other strategies, efforts to establish a proper risk communication to the public. This paper addresses the need for more empirical research in this area to contribute to the development of an in-depth understanding of public risk communication that includes information-related threats and cyber issues. The study involves officials of three public organizations entrusted with safety and security in society: the police, the rescue service, and the county administrative board of a county in the middle of Sweden. The results detail the recognition of risks to be communicated, the organization of the communication process, the messages that these actors seek to bring forth, and to whom as well as challenges of public risk communication in the digital era. The findings indicate that information-related and cyber risks are increasingly essential to consider as an additional layer of public communication. Two implications emerged as particularly important: (1) all communication about risks and crises must consider the systemic risk of mis- and disinformation, and (2) tailored communication about the risks interrelated with disinformation should use human-centered, dialogue-based, and moderated approaches. Further research can focus on associated challenges, considering the distribution of responsibilities, inter-organizational information sharing and cooperation, and the possibly stochastic effects on critical (information) infrastructures and, ultimately, societal values. © IFIP International Federation for Information Processing 2025.

关键词： Stochastic systems

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A Novel Approach for Bridging the Gap Between SDN and MITRE for Agile Incident Response 5th

A Novel Approach for Bridging the Gap Between SDN and MITRE ...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Jimmington, Anjana Alfas Hakeem, P. Mukherjee, Preetam Thampi, Sabu M. Connected Systems and Intelligence Lab School of Computer Science and Engineering Digital University Kerala Technopark Phase IV Kerala Thiruvananthapuram India

ISBN: (纸本)9789819745395

The growing complexity of potential attacks in network infrastructure demands advanced incident response (IR) techniques. With organization adopting the Software-Defined Networks the response capabilities can be improved to a greater extend. SDN’s centralized control is utilized in this process. However the lacking of a list of possible defense mechanisms in the SDN, effects the swift respondence against the occurrence of a security incident. To address this challenge, we studied the MITRE D3FEND knowledge base, which provides various standard defense mechanisms. Later, within the knowledge base the defense mechanisms that can be implemented in SDN were identified. A valid list of possible implementable defense mechanisms in SDN was created. With the help of the developed implementer tool ANTq, which runs on top of the Ryu controller the suggested MITRE defense mechanisms were automatically implemented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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Exploring Impacts of Age of Information on Data Accuracy for Wireless Sensing systems: An Information Entropy Perspective

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4907-4924页

作者： Yang, Yaoqi Du, Hongyang Xiong, Zehui Xu, Renhui Niyato, Dusit Han, Zhu Army Engineering University of PLA College of Communications Engineering Nanjing210000 China University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore NTU College of Computing and Data Science Singapore University of Houston Department of Electrical and Computer Engineering Houston77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Wireless sensing systems have been employed in the field of healthcare, environment monitoring, and smart agriculture, etc. Since the freshness and accuracy indicators of the sensing data are critical to wireless sensing systems, it is of great significance to ensure their performances simultaneously, i.e., the Age of Information (AoI) and information entropy of the sensing data should be jointly optimized. In this regard, we first establish the wireless sensing system models, including AoI and information entropy expressions. Next, from the information entropy viewpoint, we theoretically analyze an impact of the AoI on data accuracy. Then, we formulate the joint optimization problem of AoI, information entropy, and sensing energy consumption. Furthermore, we propose two numerical algorithms to solve the formulated problem in the known or unknown transmission environment, respectively. Finally, we evaluate the correctness and effectiveness of our proposals under various parameter settings, where the proposed scheme can obtain a better sum-weighted performance on AoI, information entropy, and sensing energy consumption than baselines in the literature. © 2002-2012 IEEE.

关键词： Data accuracy

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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Artificial Intelligence-Based Cybersecurity for the Metaverse: Research Challenges and Opportunities

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 1008-1052页

作者： Awadallah, Abeer Eledlebi, Khouloud Zemerly, Mohamed Jamal Puthal, Deepak Damiani, Ernesto Taha, Kamal Kim, Tae-Yeon Yoo, Paul D. Raymond Choo, Kim-Kwang Yim, Man-Sung Yeun, Chan Yeob Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Indian Institute of Management Bodh Gaya IT Systems and Analytics Bodh Gaya824234 India Khalifa University Civil and Environmental Engineering Department Abu Dhabi United Arab Emirates University of London Department of Computer Science and Information Systems LondonWC1E 7HX United Kingdom The University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States Korea Advanced Institute of Science and Technology Department of Nuclear and Quantum Engineering Daejeon34141 Korea Republic of

The metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions. © 1998-2012 IEEE.

关键词： Cybersecurity

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All-optically linearized silicon modulator with ultrahigh SFDR of 131 dB · Hz6/7

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Photonics Research 2025年第2期13卷 433-441页

作者： QIANG ZHANG QIKAI HUANG PENGHUI XIA YAN LI XINGYI JIANG SHUYUE ZHANG SHENGYU FANG JIANYI YANG HUI YU Zhejiang Lab Institute of Integrated Microelectronic Systems College of Information Science and Electronic Engineering Zhejiang University Faculty of Electrical Engineering and Computer Science Ningbo University

Integrated high-linearity modulators are crucial for high dynamic-range microwave photonic （MWP）systems. Conventional linearization schemes usually involve the fine tuning of radio-frequency （RF） power distribution, which is rather inconvenient for practical applications and can hardly be implemented on the integrated photonics chip. In this paper, we propose an elegant scheme to linearize a silicon-based modulator in which the active tuning of RF power is eliminated. The device consists of two carrier-depletion-based Mach–Zehnder modulators （MZMs）, which are connected in series by a 1×2 thermal optical switch （OS）.The OS is used to adjust the ratio between the modulation depths of the two sub-MZMs. Under a proper ratio, the complementary third-order intermodulation distortion （IMD3） of the two sub-MZMs can effectively cancel each other out. The measured spurious-free dynamic ranges for IMD3 are 131, 127, 118, 110,and 109 d B·Hz6∕7at frequencies of 1, 10, 20, 30, and 40 GHz, respectively, which represent the highest linearities ever reached by the integrated modulator chips on all available material platforms.

关键词： Effective refractive index Integrated photonics Lithium niobate Modulation transfer function Optical signals Silicon modulators

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Design and Implementation of a MPPT Controller-Based Bidirectional DC-DC Converter for Electric Vehicles Using PUMA Algorithm 4

Design and Implementation of a MPPT Controller-Based Bidirec...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Subramaniam, G. Devayani, G. Gokulapriya, C. Janani, S. Dhanush, T. Shashhwath, S. M. Kumarasamy College of Engineering Electrical and Electronics Engineering Tamil Nadu Karur India M. Kumarasamy College of Engineering Computer Science and Business Systems Tamil Nadu Karur India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

The growing need for effective energy management in electric vehicles (EVs) has led to advancements in power conversion technologies, especially in systems that facilitate the use of renewable energy sources and efficient battery management. The design and implementation of bidirectional DC-DC converters in electric cars utilize Maximum Power Point Tracking (MPPT) for their controllers. This approach ensures optimal energy conversion through the PUMA (Perturb and Observe with Modified Algorithm) technique. By intelligently regulating the power flow between a renewable energy source and the vehicle's energy storage system (such as solar panels and batteries), the proposed converter maximizes energy conversion from the renewable source while keeping operational costs low and maintaining ideal battery conditions. A bidirectional converter enables the battery to both charge and discharge, while also providing a seamless interface for energy exchange between the grid, solar panels, and the car. The MPPT controller utilizes the PUMA algorithm, which is known for its faster convergence and greater stability, making it superior to traditional Disturb and Observe (P&O) algorithms. This device includes various components that monitor voltage and current values in real-time, employing adaptive control and improved power management to ensure high-quality energy conversion regardless of external conditions. Simulations and hardware implementations have shown that this method effectively enhances vehicle performance, extends the range of electric vehicles, and optimizes the use of renewable energy sources. The study also addresses several other issues, including the potential for large-scale implementation of modern electric vehicles (EVs), the robustness of the system, and the reduction of voltage and current ripple. © 2025 IEEE.

关键词： Adaptive control systems

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Colorectal cancer classification using weakly annotated whole slide images: Multiple instance learning optimization study

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computers in Biology and Medicine 2025年 186卷 109649-109649页

作者： Saeed, Ahmed Ismail, Mohamed A. Ghanem, Nagia M. Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt

Colorectal cancer (CRC) is considered one of the most deadly cancer types nowadays. It is rapidly increasing due to many factors, such as unhealthy lifestyles, water and food pollution, aging, and medical diagnosis development. Detecting CRC in its early stages can help stop its growth by providing the necessary treatments, thereby saving many people's lives. There are various tests that doctors can perform to diagnose CRC;however, biopsy using histopathological images is considered the "gold standard" for CRC diagnosis. Deep learning techniques can now be leveraged to build computer-aided diagnosis (CAD) systems that can affirm if an input sample shows any symptoms of cancer and determine its stage and location with an acceptable degree of confidence. In this research, we utilize deep learning to study the CRC classification problem using weakly annotated histopathological whole slide images (WSIs). We relax the constraints of the multiple instance learning (MIL) algorithm and primarily propose WSI-label prediction functions to be integrated with MIL, which significantly enhances the performance of WSI-level classification. We also applied efficient preprocessing techniques that output a computationally power-efficient dataset representation and performed multiple experiments to compose the most efficient CAD system. Our study introduces a notable improvement over the results obtained by the baseline research where we achieved an accuracy of 93.05% compared to 84.17%. Furthermore, our results using only the weakly annotated WSIs outperformed the baseline results that are based on performing initial pre-training using a strongly annotated part of the dataset. © 2025 Elsevier Ltd

关键词： Diseases

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration 31

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Chen, Yuzong Abouelhamayed, Ahmed F. Dai, Xilai Wang, Yang Andronic, Marta Constantinides, George A. Abdelfattah, Mohamed S. Cornell University Computer Systems Lab United States Systems and Networking Research Group Microsoft Research United States Imperial College London Department of Electrical and Electronic Engineering United Kingdom

ISBN: (纸本)9798331506476

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD1, an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight precision. On the algorithm side, BitMoD introduces fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights. Through the careful design of these new data types, BitMoD is able to quantize LLM weights to very low precision (e.g., 4 bits and 3 bits) while maintaining high accuracy. On the hardware side, BitMoD employs a bitserial processing element to easily support multiple numerical precisions and data types;our hardware design includes two key innovations: First, it employs a unified representation to process different weight data types, thus reducing the hardware cost. Second, it adopts a bit-serial dequantization unit to rescale the per-group partial sum with minimal hardware overhead. Our evaluation on six representative LLMs demonstrates that BitMoD significantly outperforms state-of-the-art LLM quantization and acceleration methods. For discriminative tasks, BitMoD can quantize LLM weights to 4 -bit with © 2025 IEEE.

关键词： Integrated circuit design

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