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Inaudible Sound Covert Channel with Anti-Jamming Capability: Attacks vs. Countermeasure

Journal of Electronic science and Technology 2022年第4期20卷 396-415页

作者： Xiao-Hang Wang Shou-Bin Li Ying-Tao Jiang Amit Kumar Singh Bi-Yun Ma Le-Tian Huang Mei Yang Fen Guo School of Cyber Science and Technology Zhejiang UniversityHangzhou 310012 ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310012 School of Software Engineering South China University of TechnologyGuangzhou 510006 Department of Electrical and Computer Engineering University of NevadaLas Vegas 89154 School of Computer Science and Electronic Engineering University of EssexColchester CO43SQ School of Electronics and Information Engineering South China University of TechnologyGuangzhou 510640 School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054

When an inaudible sound covert channel(ISCC)attack is launched inside a computer system,sensitive data are converted to inaudible sound waves and then *** receiver at the other end picks up the sound signal,from which the original sensitive data can be *** a forceful countermeasure against the ISCC attack,strong noise can be used to jam the channel and literally shut down any possible sound data *** this paper,enhanced ISCC is proposed,whose transmission frequency can be dynamically ***,if the transmitter detects that the covert channel is being jammed,the transmitter and receiver both will switch to another available frequency and re-establish their communications,following the proposed communications *** results show that the proposed enhanced ISCC can remain connected even in the presence of a strong jamming noise ***,a detection method based on frequency scanning is proposed to help to combat such an anti-jamming sound *** the proposed countermeasure,the bit error rate(BER)of the data communications over enhanced ISCC soars to more than 48%,essentially shutting down the data transmission,and thus neutralizing the security threat.

关键词： Countermeasure inaudible sound covert channel(ISCC)

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Reproducible reformation of a bilayer lipid membrane using microair bubbles

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Droplet 2023年第3期2卷 90-98页

作者： Izumi Hashimoto Toshihisa Osaki Hirotaka Sugiura Hisatoshi Mimura Sho Takamori Norihisa Miki Shoji Takeuchi Artificial Cell Membrane Systems Group Kanagawa Institute of Industrial Science and TechnologyKanagawaJapan Department of Mechanical Engineering Keio UniversityKanagawaJapan Institute of Industrial Science The University of TokyoTokyoJapan Graduate School of Information Science and Technology The University of TokyoTokyoJapan

Planar bilayer lipid membranes(BLMs)are widely used as models for cell membranes in various applications,including drug discovery and ***,the nanometer-thick bilayer structure,assembled through hydrophobic interactions of amphiphilic lipid molecules,makes such BLM systems mechanically and electrically *** this study,we developed a device to reform BLMs using a microair *** device consists of a double well divided by a separator with a microaperture,where a BLM was formed by infusing a lipiddispersed solvent and an aqueous droplet into each well in *** the BLM ruptured,a microair bubble was injected from the bottom of the well to split the merged aqueous droplet at the microaperture,which resulted in the reformation of two lipid monolayers on the split *** bringing the two droplets into contact,a new BLM was *** angled step design was introduced in the BLM device to guide the bubble and ensure the splitting of the merged *** also elucidated the optimal bubble inflow rate for the reproducible BLM *** a 4-channel parallel device,we demonstrated the individual and repeatable reformation of *** approach will aid the development of automated and arrayed BLM systems.

关键词： bubble bringing unstable

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CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face Anti-Spoofing 18

CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face A...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Zhang, Ying Zhu, Xiangyu Liu, Ajian Lin, Xun Wan, Jun Zhang, Jing Lei, Zhen Beihang University School of Computer Science and Engineering China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation China University of Chinese Academy of Sciences School of Artificial Intelligence China Macau University of Science and Technology School of Computer Science and Engineering China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation China

ISBN: (纸本)9798350364132

Face anti-spoofing (FAS) is pivotal in safeguarding the integrity of face recognition systems. Flexible-modal FAS utilizes multi-modal data and trains a unified model adaptable to any single-modal testing scenario. This innovation addresses the shortcomings of conventional multi-modal FAS approaches, which typically demand separate model training and deployment for each modality. However, existing flexible-modal FAS approaches activate specific network branches based on the modality of the tested sample. This not only increases the model's parameters but also necessitates the provision of the image's modality for testing, thereby constraining deployment flexibility. To address the issue, we present Compound Prompt Learning CLIP (CPL-CLIP), a novel method for flexible-modal FAS. This approach capitalizes on a learned textual prompt that is nearly independent of modality, thus bolstering class-based classification across arbitrary modalities. Specifically, our CPL-CLIP introduces a Dual-Branch Prompt (DBP), consisting of class and modal prompts that describe and guide classification, where each prompt is composed of learnable vectors and fixed templates. To further render the class prompt as modality-agnostic as possible, a Cosine Similarity Loss (CSL) is proposed to facilitate the maximal separation of the class prompt from the modality prompt. With only the class prompt utilized during testing, CPL-CLIP enables deployment in diverse modal testing scenarios without the necessity of the test image's modality to be known. Extensive experiments demonstrate CPL-CLIP's superiority over existing methods on several flexible-modal FAS benchmarks. © 2024 IEEE.

关键词： Face recognition

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Metaverse User Portrait Construction Based on Human Gaze-tracking Data 9

Metaverse User Portrait Construction Based on Human Gaze-tra...

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9th IEEE Smart World Congress, SWC 2023

作者： Deng, Xiaomei Wu, Sissi Xiaoxiao Ding, Huijun Zhu, Yingying Shenzhen University School of Electronic and Information Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China University of Texas Arlington Computer Science Texas United States

ISBN: (纸本)9798350319804

With the rapid growth of virtual reality technology in the Metaverse, where a digital virtual world is integrated with the real world, users are exposed to a variety of content in different scenarios. Similar to the Web2 scenario, accurately profiling users in Metaverse applications is critical for improving user satisfaction. This involves collecting and inferring information such as users' basic attributes, interests, and consumption habits. In this study, we propose a new approach for profiling users in virtual worlds based on gaze-tracking. We developed a Metaverse shopping experiment platform and recruited volunteers to record their attention time on different commodities in 3-D scenes by wearing eye-tracking devices. We treat this gaze-tracking data as implicit feedback to capture users' interests, thereby predicting users' attributes. We tried different ways of extracting features from collected gaze-tracking data. To our knowledge, we are the first to mine user portraits using gaze-tracking sequences. Our experimental results demonstrate that the fusion of sequence and image features significantly improves the accuracy of user gender prediction, reaching 88.89%, which is better than using sequence or image features only. These findings demonstrated the practical and effective advantages of using gaze-tracking data to build user portraits in the Metaverse. © 2023 IEEE.

关键词： feature fusion gaze-tracking Metaverse time sequence

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Programmer Performance Prediction with Cognitive Tests: A Granular Approach

Programmer Performance Prediction with Cognitive Tests: A Gr...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Farghaly, Ayman Aboulmagd El-Kafrawy, Passent Mohammed School of Information Technology and Computer Science Nile University Cairo Egypt Effat College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (纸本)9798350335590

Universities and students face the challenge of finding the career that best suits the students. Programming is currently one of the highly sought-after careers and is one of the highest paying jobs. However not all students succeed in this career. In fact failure rates in programming field is relatively high globally. In this research, we used a spatial rotation test and Non-Verbal reasoning test as well as gender, at the beginning of the semester to observe the relationship between those tests and students' grades at the end of the semester for 2 groups. Group 1 are computer science students studying programming course and group 2 are non-computer science students studying programming course. For each group, we created a predictive model using 2 methods. Method 1 uses the conventional aggregate score of cognitive tests and method 2 uses answers to selected questions in each test. In method 2, we applied a data-driven methodology which utilize individual answers to questions as input to the model. To the best of our knowledge we are the first in this field to apply this method. The modeling results show usefulness of those test to predict programming and non-programming course grades using both methods, however method 2 demonstrates much higher superiority over the aggregate method 1. © 2023 IEEE.

关键词： Students

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Xabcd: A Better Model for Named Entity Recognition in Cyber Threat Intelligence 9

Xabcd: A Better Model for Named Entity Recognition in Cyber ...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Wang, Ruiqi Zheng, Zhiqiang Ao, Shengqin Fu, Yuxia Wang, Xuren Jiang, Zhengwei Xie, Gang School of Big Data and Computer Science Guizhou Normal University Guiyang China Information Engineering College Capital Normal University Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350376548

Due to the ambiguity between naming entity nouns and daily usage nouns in cyber threat intelligence, such as hacker organizations, attack tools, and attack techniques, the understanding of their content heavily relies on cyber security knowledge. How to quickly and accurately extract named entities related to network security from multi-source texts has become a hot and difficult research topic in the current threat intelligence field. In this paper, a named entity recognition framework from cyber threat intelligence unstructured text is designed. For the business requirements in the field of cyber threat intelligence, this framework completely covers data acquisition, text classification, entity and relationship definition, entity and relationship analysis and modeling and ability application. The framework is available for experimental exploration of entity recognition technology for threat intelligence. In addition, this paper proposes an entity recognition model for cyber threat intelligence, Xabcd, based on data and feature knowledge. Based on open network security text corpus, Xabcd model obtained the key indicators such as precision, recall, and F1-score as 97.35%, 97.15% and 97.23% respectively, which are all higher than the baseline model levels, so the experimental results demonstrate the feasibility and effectiveness of the model. © 2024 IEEE.

关键词： Cyber attacks

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A Comprehensive Study on Mechanism Knowledge Techniques for Intrusion Detection in Network Security

A Comprehensive Study on Mechanism Knowledge Techniques for ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Manivasagam Agarwal, Ankita Bishnoi, Amit Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

This technical abstract provides a comprehensive observation of the system studying strategies for intrusion detection in network protection. Various tactics are discussed, along with supervised getting-to-know, unsupervised studying, and deep Knowledge. Facts units, KDD Cup '99 and NSL-KDD, are used to research the overall performance of Mechanism Knowledge algorithms. Experiments were carried out on the datasets to validate the performance of the algorithms in terms of accuracy, precision, consideration, and F-degree. The outcomes show that Mechanism-getting-to-know algorithms can efficaciously achieve better overall performance compared to standard techniques. Primarily based on the effects, this paper offers contributions in phrases of (1) discovering newer algorithms, (2) knowledge of the overall performance of present algorithms on distinctive intrusion detection duties, and (three) locating relationships among the features. © 2024 IEEE.

关键词： Algorithms Comprehensive Datasets Experiments Features Knowledge Mechanism Knowledge

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Physically Secure and Privacy-Preserving Authentication Scheme for VANET

Physically Secure and Privacy-Preserving Authentication Sche...

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2024 International Conference on Artificial Intelligence of Things and systems, AIoTSys 2024

作者： Chen, Dongmei Liang, Yangfan Li, Lihui Liu, Yining School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China

ISBN: (纸本)9798331507886

The communication between vehicles and other entities occurs over open wireless networks, making them susceptible to various security attacks In Vehicular Ad Hoc Network (VANET). Consequently, many researchers have proposed numerous data transmission and key exchange protocols to ensure secure communication within VANET. The keys and sensitive information involved in these protocols need to be stored long-Term in the tamper-resistant devices of vehicles. However, due to the limited resources of these devices, many encryption schemes are difficult to embed. Even if effective encryption schemes can be embedded in VANET, the long-Term storage of data in vehicles still carries a significant risk of being attacked. To tackle this problem, we propose an authentication protocol designed for VANET that emphasizes both physical security and privacy protection. The protocol primarily leverages the properties of Physical Unclonable Functions to achieve enhanced security. This approach eliminates the need for vehicles to store keys and sensitive messages long-Term, reducing the storage burden on vehicles while enhancing the system's security and efficiency. Detailed security analyses demonstrate that our proposed scheme achieves the goals of physical security and privacy protection. A comparison with similar approaches demonstrates that our solution outperforms them in both time efficiency and communication overhead. © 2024 IEEE.

关键词： Authentication Protocol

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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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computer Modeling in engineering & sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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A Three-Model Deep Learning Framework for ASL Recognition: Integrating CNN, Skeletal Features, and Multi-Modal Fusion

A Three-Model Deep Learning Framework for ASL Recognition: I...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Karthikeyan J Vedant Passi Akshit Mahajan Md Zaid Zeya Divyam Jain Manas Gautam School of Computer Science Engineering and Information System VIT University Vellore India School of Computer Science and Engineering VIT University Vellore India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

American Sign Language (ASL) recognition is an important way of enhancing deaf and hard-of-hearing individu-als' access to communication. In this present paper, we propose a multi-modal deep learning model with the incorporation of image-based, skeletal-based, and hybrid recognition methods in the attempt to foster enhanced ASL classification accuracy. Our method takes real-time video input and utilizes Convolutional Neural Networks (CNNs) for image-based recognition as well as Multi-Layer Perceptrons (MLPs) for skeletal feature extraction. MediaPipe Hands is used to identify 21 hand landmarks, which are further used as input for skeletal-based classification. The dataset, which is gathered from Kaggle, comprises 78,000 images of 26 ASL alphabets (A-Z), and each model is trained individually to achieve the best performance. An ensemble final model uses the most confident classification out of the three approaches to achieve maximum accuracy and reliability. The system is real-time with confidence-based filtering and TTS support for better usability. The experimental results show that the multi-modal fusion approach outperforms single-modality models with high accuracy even in the case of diverse lighting conditions and hand orientations. Future directions involve using the model for sentence-level ASL recognition with LSTMs and deployment with optimizations on mobile and edge devices using TensorFlow Lite.

关键词： Hands Deep learning Adaptation models Sign language Accuracy Translation Computational modeling Real-time systems Mobile handsets Convolutional neural networks

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