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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Kazari, Kiarash Kanellopoulos, Aris Dán, György Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)157735897X

We consider correlated equilibria in strategic games in an adversarial environment, where an adversary can compromise the public signal used by the players for choosing their strategies, while players aim at detecting a potential attack as soon as possible to avoid loss of utility. We model the interaction between the adversary and the players as a zero-sum game and we derive the maxmin strategies for both the defender and the attacker using the framework of quickest change detection. We define a class of adversarial strategies that achieve the optimal trade-off between attack impact and attack detectability and show that a generalized CUSUM scheme is asymptotically optimal for the detection of the attacks. Our numerical results on the Sioux-Falls benchmark traffic routing game show that the proposed detection scheme can effectively limit the utility loss by a potential adversary. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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IoT Authentication Protocols: Classification, Trend and Opportunities

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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The Proof is in the Pudding: Decision-Oriented Machine-Type Wireless Video Transmission

The Proof is in the Pudding: Decision-Oriented Machine-Type ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Song, Jie Gao, Yun Wu, Dan Zhou, Liang Qian, Yi Nanjing University of Posts and Telecommunications Ministry of Education Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China Army Engineering University of Pla Institute of Communications Engineering Nanjing China University of Nebraska-Lincoln Department of Electrical and Computer Engineering Lincoln United States

ISBN: (纸本)9798350351255

With the rise of the Internet of Everything, machine-type wireless video applications like intelligent surveillance are increasingly becoming mainstream network services. However, the massive transmission of video streams heavily burdens wireless networks. Existing methods typically establish a correlation between the transmitted content and the quality of experience or service, making it challenging to balance trade-offs among decision quality, bandwidth utilization, and latency requirements. To address this issue fundamentally, this paper proposes a novel perspective by designing wireless video transmission strategies from the angle of decision quality, where only the content that impacts the decision outcomes is transmitted. The highlight of this paper is designing a lightweight yet efficient binary classifier that predicts whether the current content will change decision outcomes based on content discrepancies, measured only through pixel-level differences and macroblock-level similarity. Furthermore, these discrepancies can be used to distinguish the background, thereby further saving bandwidth by reusing the background information. Additionally, a fine-tuning module is incorporated to flexibly update the classifier model, ensuring adaptability across various scenarios. Extensive results demonstrate that the proposed strategy achieves over 81% redundant bandwidth savings while maintaining decision quality and catering to strict latency requirements on computation-constrained devices. © 2024 IEEE.

关键词： Television transmission

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Enhancing Non-Destructive Examining of Composite Materials Using Lightweight Structures And Machine Learning

Enhancing Non-Destructive Examining of Composite Materials U...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Gangwar, Prashant Kumar Pattanaik, Balachandra Madiajagan, M. Sohail, Mohammed Ali Babu, M. Dinesh Sarala, B. Woldia University Department of Construction Technology & Management Amhara Region Ethiopia Wollega University College of Engineering and Technology Department of Electrical and Computer Engineering Nekemte Ethiopia Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Jazan University College of Computer Science & Information Technology Department of Computer & Network Engineering Jazan Saudi Arabia Rajalakshmi Institute of Technology Department of Mechanical Engineering Chembarabakkam Chennai India R.M.K Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350344387

Composite materials and structures are becoming more efficient and cost-effective, and new special features are being developed to improve their specific properties. Their use in a variety of applications such as wind turbines, aeronautical, medical equipment, transportation, and other load-bearing structures is becoming increasingly important. It is necessary for non-destructive composite examining to be robust and trustworthy in order to limit the risk of damage as well as the cost of maintenance. In order to ensure the product quality throughout its life cycle, many non-destructive examining procedures based on a wide range of concepts have been created. In this paper, nondestructive examining was used to obtain vibration measurements of composites in high-frequency regimes, which were then analysed. Both end-to-end classifications of the data and classifications of data acquired by feature engineering with modal analysis employ Support Vector Machine (SVM) to carry out damage localization in their respective applications. Finally, the findings display a large number of plates made of various materials and with defects of varying magnitudes. © 2023 IEEE.

关键词： Composite Localization Machine Learning Nondestructive Examining

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Evaluation Model of Learner's Cognitive Level Based on RoBERTa Fused with CNN 3

Evaluation Model of Learner's Cognitive Level Based on RoBER...

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3rd International Conference on Neural networks, Information and Communication engineering, NNICE 2023

作者： Cai, Yingying Zhao, Yuancheng Luo, Haibo Huang, Zhongyou Huang, Zhongying Zhang, Feng Neusoft Institute Guangdong Department of Computer Network Engineering Foshan China Gd Midea Heating & Ventilating Equipment Co. Ltd Advanced Research Center Foshan China

ISBN: (纸本)9798350335972

The cognitive level is an essential indicator of the learning process evaluation. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which cannot fully mine the implicit cognitive semantic information in unstructured text data. In this study, a fusion model named RoBERTa-CNN is proposed, which uses RoBERTa pre-training model to capture the cognitive semantic features implied between words in the sentence. Furthermore, it combines a convolution neural network to extract the global semantic features of sentences, to extract the deep-level features of the text. Using Bloom's cognitive goal classification theory as the framework of cognitive level evaluation, 9657 text data from MOOC were evaluated automatically. The results show that the mixed RoBERTa-CNN model has the best evaluation effect, and the overall accuracy rate of the six cognitive levels is 85.26%, demonstrating the model's validity. © 2023 IEEE.

关键词： Semantics

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Study on Effect of Aerodynamic Configuration on Aerodynamic Performance of Mars Ascent Vehicles

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Space(Science & Technology) 2022年第1期2022卷 120-130页

作者： Qi Li Wu Yuan Rui Zhao Haogong Wei Beijing Institute of Spacecraft System Engineering CASTBeijingChina 100094 Computer Network Information Center Chinese Academy of SciencesBeijingChina 100190 School of Aeronautics and Astronautics Beijing Institute of TechnologyBeijingChina 100081

The Mars surface take-off and ascent technology is one of the key technologies for realizing the Mars sample return *** from that on the moon,the gravity acceleration on the surface of Mars is 3.71 m/s^(2),so that the gravity loss is larger than that on the moon;a rarefied atmosphere is found on the surface of Mars,and although it is only about 1%of the Earth’s atmosphere,its effect on aerodynamic drag in the process of ascent shall also be *** this paper,the aerodynamic performance demand of ascent vehicles is analyzed in light of the mission requirements for take-off and ascent from the surface of *** on the results of literature research and supersonic CFD static simulation,the influence of forebody and afterbody shapes of ascent vehicles on aerodynamic drag and static stability is studied,*** forebody shape of ascent vehicles with better aerodynamic performance is proposed,and the subsequent improvement direction of aerodynamic configuration is clarified,providing necessary theoretical and data support for the aerodynamic selection of Mars ascent vehicles.

关键词： Configuration clarified subsequent

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Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning 38

Parameter Disparities Dissection for Backdoor Defense in Het...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Huang, Wenke Ye, Mang Shi, Zekun Wan, Guancheng Li, He Du, Bo National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

Backdoor attacks pose a serious threat to federated systems, where malicious clients optimize on the triggered distribution to mislead the global model towards a predefined target. Existing backdoor defense methods typically require either homogeneous assumption, validation datasets, or client optimization conflicts. In our work, we observe that benign heterogeneous distributions and malicious triggered distributions exhibit distinct parameter importance degrees. We introduce the Fisher Discrepancy Cluster and Rescale (FDCR) method, which utilizes Fisher Information to calculate the degree of parameter importance for local distributions. This allows us to reweight client parameter updates and identify those with large discrepancies as backdoor attackers. Furthermore, we prioritize rescaling important parameters to expedite adaptation to the target distribution, encouraging significant elements to contribute more while diminishing the influence of trivial ones. This approach enables FDCR to handle backdoor attacks in heterogeneous federated learning environments. Empirical results on various heterogeneous federated scenarios under backdoor attacks demonstrate the effectiveness of our method. © 2024 Neural information processing systems foundation. All rights reserved.

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Zenith: Real-time Identification of DASH Encrypted Video Traffic with Distortion 24

Zenith: Real-time Identification of DASH Encrypted Video Tra...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Tang, Weitao Li, Jianqiang Du, Meijie Hu, Die Liu, Qingyun Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (纸本)9798400706868

Some video traffic carries harmful content, such as hate speech and child abuse, primarily encrypted and transmitted through Dynamic Adaptive Streaming over HTTP (DASH). Promptly identifying and intercepting traffic of harmful videos is crucial in network regulation. However, QUIC is becoming another DASH transport protocol in addition to TCP. On the other hand, complex network environments and diverse playback modes lead to significant distortions in traffic. The issues above have not been effectively addressed. This paper proposes a real-time identification method for DASH encrypted video traffic with distortion, named Zenith. We extract stable video segment sequences under various itags as video fingerprints to tackle resolution changes and propose a method of traffic fingerprint extraction under QUIC and VPN. Subsequently, simulating the sequence matching problem as a natural language problem, we propose Traffic Language Model (TLM), which can effectively address video data loss and retransmission. Finally, we propose a frequency dictionary to accelerate Zenith's speed further. Zenith significantly improves accuracy and speed compared to other SOTA methods in various complex scenarios, especially in QUIC, VPN, automatic resolution, and low bandwidth. Zenith requires traffic for just half a minute of video content to achieve precise identification, demonstrating its real-time effectiveness. © 2024 Owner/Author.

关键词： Video streaming

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FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference 38

FedSSP: Federated Graph Learning with Spectral Knowledge and...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tan, Zihan Wan, Guancheng Huang, Wenke Ye, Mang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that the spectral nature of graphs can well reflect inherent domain structural shifts. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module. Combining both strategies, we propose our pFGL framework FedSSP which Shares generic Spectral knowledge while satisfying graph Preferences. Furthermore, We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. The code is available at https://***/OakleyTan/FedSSP. © 2024 Neural information processing systems foundation. All rights reserved.

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Brain Tumor Detection Using Image Processing 5

Brain Tumor Detection Using Image Processing

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Wijerathna, Udari Dissanayake, Nimavi Nimasha, Dinithi Senarathne, Udara Weerasinghe, Lokeesha Kahandawaarachchi, Chathurangika Sri Lanka Institute of Information Technology Faculty of Computing Dept of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Dept of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Dept of Computer System Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Abnormal formation of cells in a layer. The brain is known as the brain cancer complex, and a brain cancer detection system is needed to detect cancer in its early stages Phase. As a result, the primary goal of this initiative is to assist existing doctors in their training for the early identification o f brain cancer by utilizing magnetic resonance imaging (MRI), which is the most effective approach to identifying brain cancer. This system is done through four components, and it has made it easier to detect brain tumors. These techniques with different image processing techniques include noise removal, segmentation, classification, a nd u se o fa nalysis segments f or detection. Pre-processing techniques prepare the image for further processing by removing image noise. During segmentation, the brain tumor system determines the size of the brain tumor and estimates its size. Specialists should examine them and classify brain tumors as meningiomas, gliomas, and pituitary tumors. In analysis, this component is expected to be designed so that users or medical experts can know the future through a dashboard. We explored the methodology of using generative adversarial networks (GANs) implemented in TensorFlow to denominate brain tumor images. Analyses the outcomes of various types of established deep learning models on the brain tumor MR image datasets, such as classification methods ResNet-50, VGG 16, CNN, and Inception V3. The validation precision, AUC, recall, and validation loss for CNN are 95.3%, 99.43%, 92.1 %, and 0.360 adjustments respectively. © 2023 IEEE.

关键词： Magnetic resonance imaging

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