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2025 International Conference on Computing, Networking and Communications, ICNC 2025

作者： Rayan Ranasinghe, Kuranage Roche Ge, Yao Freitas De Abreu, Giuseppe Thadeu Liang Guan, Yong Constructor University School of Computer Science and Engineering Bremen28759 Germany Nanyang Technological University Continental-NTU Corporate Lab 639798 Singapore Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore

ISBN: (纸本)9798331520960

In this paper, we propose a novel low complexity time domain (TD) oversampling receiver framework under affine frequency division multiplexing (AFDM) waveforms for joint channel estimation and data detection (JCEDD). Leveraging a generalized doubly-dispersive channel model, we first derive the input-output (I/O) relationship for arbitrary waveforms when oversampled in the TD and present the I/O relationship for AFDM as an example. Subsequently, utilizing the multiple sample streams created via the oversampling procedure, we use the parametric bilinear Gaussian belief propagation (PBiGaBP) technique to conduct JCEDD for decoding the transmitted data and estimating the complex channel coefficients. Simulation results verify significant performance improvements both in terms of data decoding and complex channel coefficient estimation with improved robustness against a varying number of pilots over a conventional Nyquist sampling rate receiver. © 2025 IEEE.

关键词： Orthogonal frequency division multiplexing

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Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention

Heterogeneous Anomaly Detection for Software Systems via Sem...

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International Conference on Software engineering (ICSE)

作者： Cheryl Lee Tianyi Yang Zhuangbin Chen Yuxin Su Yongqiang Yang Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Sun Yat-sen University Guangzhou China Computing and Networking Innovation Lab Cloud BU Huawei

Prompt and accurate detection of system anomalies is essential to ensure the reliability of software systems. Unlike manual efforts that exploit all available run-time information, existing approaches usually leverage only a single type of monitoring data (often logs or metrics) or fail to make effective use of the joint information among different types of data. Consequently, many false predictions occur. To better understand the manifestations of system anomalies, we conduct a systematical study on a large amount of heterogeneous data, i.e., logs and metrics. Our study demonstrates that logs and metrics can manifest system anomalies collaboratively and complementarily, and neither of them only is sufficient. Thus, integrating heterogeneous data can help recover the complete picture of a system's health status. In this context, we propose Hades, the first end-to-end semi-supervised approach to effectively identify system anomalies based on heterogeneous data. Our approach employs a hierarchical architecture to learn a global representation of the system status by fusing log semantics and metric patterns. It captures discriminative features and meaningful interactions from heterogeneous data via a cross-modal attention module, trained in a semi-supervised manner. We evaluate Hades extensively on large-scale simulated data and datasets from Huawei cloud. The experimental results present the effectiveness of our model in detecting system anomalies. We also release the code and the annotated dataset for replication and future research.

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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Parasitic Parameters Effect on the Operation of High Frequency Electric Drive System

Parasitic Parameters Effect on the Operation of High Frequen...

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International Conference on electrical Machines, ICEM

作者： Fawzy A. Abdo Kotb B. Tawfiq Peter Sergeant Department of Electromechanical Systems and Metal Engineering Ghent University Ghent Belgium Flanders Make@UGent Core Lab MIRO Leuven Belgium Department of Electrical Engineering Menoufia University Menoufia Egypt Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

The rapid switching of semiconductor power modules in electric drive systems generates high frequency (HF) common mode voltage at the motor terminals which causes bearing failure and winding degradation. Therefore, accurate characterization of the inverter and motor parameters - including parasitic effects - is vitally important to understand the high frequency performance as well as to model and mitigate the switching oscillations of the electric drive system. In this paper, the high frequency operation of the inverter in the electric drive system, as well as the direct effect of the parasitic components on the complete system are thoroughly investigated. Moreover, a detailed extraction process of the high frequency parasitic parameters is introduced. An equivalent circuit model of the complete drive system based on the impedance characteristics is introduced. Additionally, a HF model of a 750W three-phase induction motor is developed with parameter values determined from measurements on this motor.

关键词： Semiconductor device measurement Voltage measurement Induction motors Switches Inverters Frequency measurement High frequency Integrated circuit modeling Oscillators Equivalent circuits

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Deep Learning Innovations in Diagnosing Diabetic Retinopathy: The Potential of Transfer Learning and the DiaCNN Model

arXiv

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arXiv 2024年

作者： Shoaib, Mohamed R. Emara, Heba M. Zhao, Jun El-Shafai, Walid Soliman, Naglaa F. Mubarak, Ahmed S. Omer, Osama A. Abd El-Samie, Fathi E. Esmaiel, Hamada School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Electrical Engineering Department Aswan Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and the prevention of eventual blindness. Traditional diagnostic methods, relying on human interpretation of these medical images, face challenges in terms of accuracy and efficiency. In the present research, we introduce a novel method that offers superior precision in DR diagnosis, compared to these traditional methods, by employing advanced deep learning techniques. Central to this approach is the concept of transfer learning. This entails using pre-existing, well-established models, specifically InceptionResNetv2 and Inceptionv3, to extract features and fine-tune select layers to cater to the unique requirements of this specific diagnostic task. Concurrently, we also present a newly devised model, DiaCNN, which is tailored for the classification of eye diseases. To validate the efficacy of the proposed methodology, we leveraged the Ocular Disease Intelligent Recognition (ODIR) dataset, which comprises eight different eye disease categories. The results were promising. The InceptionResNetv2 model, incorporating transfer learning, registered an impressive 97.5% accuracy in both the training and testing phases. Its counterpart, the Inceptionv3 model, achieved an even more commendable 99.7% accuracy during training, and 97.5% during testing. Remarkably, the DiaCNN model showcased unparalleled precision, achieving 100% accuracy in training and 98.3% in testing. These figures represent a significant leap in classification accuracy when juxtaposed with existing state-of-the-art diagnostic methods. Such advancements hold immense promise for the future, emphasizing the potential of our propos

关键词： Deterioration

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Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation

arXiv

引用

arXiv 2024年

作者： Chu, Zhaoyang Wan, Yao Li, Qian Wu, Yang Zhang, Hongyu Sui, Yulei Xu, Guandong Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia School of Big Data and Software Engineering Chongqing University China School of Computer Science and Engineering University of New South Wales Australia School of Computer Science University of Technology Sydney Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding approach for vulnerability detection, owing to their ability to capture the underlying semantic structure of source code. However, GNNs face significant challenges in explainability due to their inherently black-box nature. To this end, several factual reasoning-based explainers have been proposed. These explainers provide explanations for the predictions made by GNNs by analyzing the key features that contribute to the outcomes. We argue that these factual reasoning-based explanations cannot answer critical what-if questions: "What would happen to the GNN's decision if we were to alter the code graph into alternative structures?" Inspired by advancements of counterfactual reasoning in artificial intelligence, we propose CFExplainer, a novel counterfactual explainer for GNN-based vulnerability detection. Unlike factual reasoning-based explainers, CFExplainer seeks the minimal perturbation to the input code graph that leads to a change in the prediction, thereby addressing the what-if questions for vulnerability detection. We term this perturbation a counterfactual explanation, which can pinpoint the root causes of the detected vulnerability and furnish valuable insights for developers to undertake appropriate actions for fixing the vulnerability. Extensive experiments on four GNN-based vulnerability detection models demonstrate the effectiveness of CFExplainer over existing state-of-the-art factual reasoning-based explainers. © 2024, CC BY.

关键词： Graph neural networks

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Focal volume optics for composite structuring in transparent solids

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International Journal of Extreme Manufacturing 2025年第1期7卷 357-366页

作者： Bo Zhang Zhuo Wang Dezhi Tan Min Gu Yuanzheng Yue Jianrong Qiu State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027People’s Republic of China Zhejiang Lab Hangzhou 311100People’s Republic of China School of Materials Science and Engineering Zhejiang UniversityHangzhou 310027People’s Republic of China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghaiPeople’s Republic of China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghaiPeople’s Republic of China Department of Chemistry and Bioscience Aalborg University9220 AalborgDenmark

Achieving high-level integration of composite micro-nano structures with different structural characteristics through a minimalist and universal process has long been the goal pursued by advanced manufacturing research but is rarely explored due to the absence of instructive ***,we revealed a controllable ultrafast laser-induced focal volume light field and experimentally succeeded in highly efficient one-step composite structuring in multiple transparent solids.A pair of spatially coupled twin periodic structures reflecting light distribution in the focal volume are simultaneously created and independently tuned by engineering ultrafast laser-matter *** demonstrated that the generated composite micro-nano structures are applicable to multi-dimensional information integration,nonlinear diffractive elements,and multi-functional optical *** work presents the experimental verification of highly universal all-optical fabrication of composite micro-nano structures with independent controllability in multiple degrees of freedom,expands the current cognition of ultrafast laser-based material modification in transparent solids,and establishes a new scientific aspect of strong-field optics,namely,focal volume optics for composite structuring transparent solids.

关键词： ultrafast laser focal volume light field composite structuring transparent solids advanced manufacturing

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Cyber Twin Technology for AI-Driven Real-Time Software Security in IoT Ecosystems

Cyber Twin Technology for AI-Driven Real-Time Software Secur...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Krishna Chaitanya Sunkara Gulshan Dhasmana Ravi K S Nayana Yadav M Ishan Desai Rajmani Shankar Oracle Cloud Engineering Data Center Design & Modeling AL/ML Expert North Carolina USA Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering Vidyavardhaka College of Engineering Mysuru India Department of Computer Science and Engineering AJ Institute of Engineering and Technology Mangalore India Department of Electrical Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Gujarat India Faculty of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

As Internet of Things (IoT) ecosystems grow more complex, ensuring real-time security has become a major challenge. Traditional security approaches are insufficient for handling dynamic and interconnected IoT networks, which are increasingly targeted by sophisticated cyber-attacks. To address these issues, new methodologies that combine real-time monitoring and adaptive security mechanisms are needed. Cyber Twin technology, an innovative extension of digital twin technology, presents a promising solution by creating AI-driven digital replicas of IoT devices and software systems for continuous security monitoring and management. This paper introduces a Cyber Twin Technology Framework for AI-driven real-time software security in IoT ecosystems. The framework employs advanced AI models, including Convolutional Neural Networks (CNNs) for anomaly detection and Generative Adversarial Networks (GANs) for synthetic data generation to simulate potential attack scenarios. A dynamic reinforcement learning module is integrated to optimize threat response strategies based on evolving threat patterns. By creating real-time digital replicas of IoT components, the Cyber Twin framework continuously monitors device behaviors, identifies anomalies, and autonomously initiates mitigation actions. The system is evaluated in a simulated IoT environment with over 500 interconnected devices. Experimental results demonstrate that the Cyber Twin framework achieved a 99.2% detection accuracy in identifying cyber threats, with a false positive rate of 1.3%. The dynamic response module reduced incident response time by 35% compared to traditional methods, enhancing the system's ability to neutralize potential threats in real-time. The use of GAN-based synthetic data also enabled proactive defense strategies, reducing attack success rates by 40% during testing. The Cyber Twin Technology Framework provides a robust solution for real-time software security in complex IoT ecosystems. By leveraging A

关键词： Accuracy Ecosystems Software systems Real-time systems Threat assessment Internet of Things Security Time factors Monitoring Synthetic data

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Two-Server Private Information Retrieval with High Throughput and Logarithmic Bandwidth

Two-Server Private Information Retrieval with High Throughpu...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Huang, Cheng Yang, Anjia Liu, Dongxiao Lu, Rongxing Shen, Xuemin Sherman University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Jinan University College of Cyber Security Guangzhou510632 China University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada Pazhou Lab Guangzhou China

ISBN: (纸本)9798350345384

In this paper, we propose a high-throughput, logarithmic-bandwidth private information retrieval (PIR) scheme, which enables a client to securely retrieve any record from a database replicated on two remote servers without revealing the record index. Specifically, based on the puncturable pseudorandom function (PPRF), a client first leverages randomized mapping to locate a logarithmic-size punctured PRF key capable of replacing a randomized set of arbitrary size while concealing a chosen element within the set. With the key, the client then generates corresponding PIR queries for two servers, achieving logarithmic communication complexity. Upon receiving the queries, the servers independently perform level-I PPRF evaluations on partitioned databases, i.e., buckets, to obtain intermediate results, and cooperatively perform level-II PPRF evaluations on these results to provide fast PIR responses. The two-level recursive approach can significantly reduce the servers' computational complexity from linear to sublinear, ensuring high throughput. Finally, we conduct a comprehensive security analysis and develop a proof-of-concept prototype to demonstrate the efficiency and security of the proposed PIR scheme. © 2023 IEEE.

关键词： Bandwidth

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Biotic Browser: Applying StreamingLLM as a Persistent Web Browsing Co-Pilot

arXiv

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arXiv 2024年

作者： Dunnell, Kevin F. Stoddard, Andrew P. Media Lab Massachusetts Institute of Technology CambridgeMA02139 United States Electrical Engineering & Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper presents "Biotic Browser," an innovative AI assistant leveraging StreamingLLM to transform web navigation and task execution. Characterized by its ability to simulate the experience of a passenger in an autonomous vehicle, the Biotic Browser excels in managing extended interactions and complex, multi-step web-based tasks. It marks a significant advancement in AI technology, particularly in the realm of long-term context management, and offers promising applications for enhancing productivity and efficiency in both personal and professional settings. © 2024, CC BY-NC-ND.

关键词： Web browsers

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