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VMScan: an out-of-VM malware scanner

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The Journal of China Universities of Posts and Telecommunications 2020年第4期27卷 59-68页

作者： Lin Jie Liu Chuanyi Fang Binxing School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Information and Electronic Engineering Chinese Academy of EngineeringBeijing 100088China

The harm caused by malware in cloud computing environment is more and more serious. Traditional anti-virus software is in danger of being attacked when it is deployed in virtual machine on a large scale, and it tends not to be accepted by tenants in terms of performance. In this paper, a method of scanning malicious programs outside the virtual machine is proposed, and the prototype is implemented. This method transforms the memory of the virtual machine to the host machine so that the latter can access it. The user space and kernel space of virtual machine memory are analyzed via semantics, and suspicious processes are scanned by signature database. Experimental results show that malicious programs can be effectively scanned outside the virtual machine, and the performance impact on the virtual machine is low, meeting the needs of tenants.

关键词： security virtualization cloud malware virus detection signature scanning

EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection

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arXiv 2023年

作者： Li, Xiang Lan, Long Lahza, Husam Yang, Shaowu Wang, Shuihua Yang, Wenjing Liu, Hengzhu Zhang, Yudong College of Computer Science and Technology National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Biological Sciences Xi'an Jiaotong-Liverpool University Jiangsu Suzhou215123 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China Department of Mathematical Sciences University of Liverpool LiverpoolL69 3BX United Kingdom

In the face of rapid advances in medical imaging, cross-domain adaptive medical image detection is challenging due to the differences in lesion representations across various medical imaging technologies. To address this issue, we draw inspiration from large language models to propose EAFP-Med, an efficient adaptive feature processing module based on prompts for medical image detection. EAFP-Med can efficiently extract lesion features of different scales from a diverse range of medical images based on prompts while being flexible and not limited by specific imaging techniques. Furthermore, it serves as a feature preprocessing module that can be connected to any model front-end to enhance the lesion features in input images. Moreover, we propose a novel adaptive disease detection model named EAFP-Med ST, which utilizes the Swin Transformer V2 – Tiny (SwinV2-T) as its backbone and connects it to EAFP-Med. We have compared our method to nine state-of-the-art methods. Experimental results demonstrate that EAFP-Med ST achieves the best performance on all three datasets (chest X-ray images, cranial magnetic resonance imaging images, and skin images). EAFP-Med can efficiently extract lesion features from various medical images based on prompts, enhancing the model's performance. This holds significant potential for improving medical image analysis and diagnosis. © 2023, CC BY.

关键词： Magnetic resonance imaging

Securing the Future Internet of Things with Post-Quantum Cryptography

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arXiv 2022年

作者： Kumar, Adarsh Ottaviani, Carlo Gill, Sukhpal Singh Buyya, Rajkumar Department of Systemics School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science York Centre for Quantum Technologies University of York United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

Traditional and lightweight cryptography primitives and protocols are insecure against quantum attacks. Thus, a real-time application using traditional or lightweight cryptography primitives and protocols does not ensure full-proof security. Post-quantum Cryptography is important for the Internet of Things (IoT) due to its security against Quantum attacks. This paper offers a broad literature analysis of post-quantum cryptography for IoT networks, including the challenges and research directions to adopt in real-time applications. The work draws focus towards post-quantum cryptosystems that are useful for resource-constraint devices. Further, those quantum attacks are surveyed, which may occur over traditional and lightweight cryptographic primitives. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

EGRC-Net: Embedding-induced Graph Refinement Clustering Network

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arXiv 2022年

作者： Peng, Zhihao Liu, Hui Jia, Yuheng Hou, Junhui The Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong The School of Computing & Information Sciences Caritas In-stitute of Higher Education Hong Kong School of Computer Science and Engineering SoutheastUniversity Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Existing graph clustering networks heavily rely on a predefined yet fixed graph, which can lead to failures when the initial graph fails to accurately capture the data topology structure of the embedding space. In order to address this issue, we propose a novel clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net), which effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. To begin, we leverage both semantic and topological information by employing a vanilla auto-encoder and a graph convolution network, respectively, to learn a latent feature representation. Subsequently, we utilize the local geometric structure within the feature embedding space to construct an adjacency matrix for the graph. This adjacency matrix is dynamically fused with the initial one using our proposed fusion architecture. To train the network in an unsupervised manner, we minimize the Jeffreys divergence between multiple derived distributions. Additionally, we introduce an improved approximate personalized propagation of neural predictions to replace the standard graph convolution network, enabling EGRC-Net to scale effectively. Through extensive experiments conducted on nine widely-used benchmark datasets, we demonstrate that our proposed methods consistently outperform several state-of-the-art approaches. Notably, EGRC-Net achieves an improvement of more than 11.99% in Adjusted Rand Index (ARI) over the best baseline on the DBLP dataset. Furthermore, our scalable approach exhibits a 10.73% gain in ARI while reducing memory usage by 33.73% and decreasing running time by 19.71%. The code for EGRC-Net will be made publicly available at https://***/ZhihaoPENG-CityU/EGRC-Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET

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Wireless Communications and Mobile computing 2023年第1期2023卷

作者： Xie, Xiaoyun Navaei, Yahya Dorostkar Einy, Sajad School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou341000 China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou341000 China Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Istanbul Aydin University Department of Application and Research Center for Advanced Studies Istanbul Turkey

In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases the data delivery rate in VANET. To overcome this issue, predicting the path of vehicles can play an important role in sending data from the source to the destination. When an accident occurs on the road, the messages that are sensed by the imbedded sensors in the vehicles need to be sent, and if they are sent by the vehicles that change their route, these messages will not be sent to the destination and the performance of the network will be disturbed. Previous methods in the literature for data transmission in intervehicular networks have focused more on reliability and trust, and little attention has been paid to the prediction of vehicle movement paths in these types of networks. Therefore, for fast and reliable data transmission in VANET, accurate prediction of vehicle movement and creation of movement patterns can be effective in message transmission delay and data delivery rate. In this paper, we present an approach using a combination of cluster-based routing protocols and pattern discovery methods to minimize latency in VANETs. The outline of the proposed method has four modules: primary data collection and analysis, primary data preparation and analysis, pattern extraction and vehicle route discovery, and vehicle clustering and data/information transmission routing. The simulation results show that the proposed method with a delivery rate of 88.56% has significantly i

关键词： Vehicular ad hoc networks

Training of Spiking Neural Network joint Curriculum Learning Strategy

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arXiv 2023年

作者： Tang, Lingling Chu, Jielei Gong, Zhiguo Li, Tianrui The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL. Copyright © 2023, The Authors. All rights reserved.

关键词： Curricula

Achieving Distributed and Privacy-Preserving Cross-Chain Transactions in Account-Model Blockchain Systems

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Achieving Distributed and Privacy-Preserving Cross-Chain Tra...

Metaverse computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Chuan Zhang Weijie Wang Weiting Zhang Jiangtian Nie Jinwen Liang Liehuang Zhu School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Guangdong China School of Computer Science & Technology Beijing Institute of Technology Beijing China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing Hong Kong Polytechnic University China

Cross-Chain is an emerging technology to enable transaction circulation across different blockchain systems, breaking down transaction isolation among decentralized ecosystems. Existing cross-chain solutions are often constructed based on centralized trusted third parties, which introduce additional threats to users’ identities due to potentially compromised risks on the centralized third parties. To exclude the single-point compromised risk in centralized systems, this paper focuses on a fully decentralized scenario and develops a distributed cross-chain transaction scheme, which enables cross-chain transaction exchanges while ensuring the unlinkability between payees and payers. Specifically, we first propose a cross-chain cryptocurrency exchange scheme by leveraging Ethereum’s smart contracts, which achieves transaction exchanges in decentralized settings. Then, we leverage zero-knowledge succinct non-interactive argument of knowledge (i.e., zk-SNARKs) to explore a privacy-preserving method for cross-chain transactions in account-model blockchain systems, such that the unlinkability between payees and payers and the privacy of cross-chain participants can be guaranteed. Security analysis and experimental evaluations demonstrate that the proposed scheme achieves the security goal at acceptable costs.

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Deepfake Detection: A Comprehensive Survey from the Reliability Perspective

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arXiv 2022年

作者： Wang, Tianyi Liao, Xin Chow, Kam Pui Lin, Xiaodong Wang, Yinglong Department of Computer Science The University of Hong Kong Hong Kong College of Computer Science and Electronic Engineering Hunan University China School of Computer Science University of Guelph Canada Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. We identify three reliability-oriented research challenges in the current Deepfake detection domain: transferability, interpretability, and robustness. Moreover, while solutions have been frequently addressed regarding the three challenges, the general reliability of a detection model has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake-related cases in court. We, therefore, introduce a model reliability study metric using statistical random sampling knowledge and the publicly available benchmark datasets to review the reliability of the existing detection models on arbitrary Deepfake candidate suspects. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of the reliably qualified detection models as reviewed in this survey. Reviews and experiments on the existing approaches provide informative discussions and future research directions for Deepfake detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Data reliability

Cloud ubiquitous learning approach based on multi-agents system

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Cloud ubiquitous learning approach based on multi-agents sys...

information Technology (ACIT)

作者： Manel Guettala Samir Bourekkache Okba Kazar Saad Harous Belkacem Athamena Zina Houhamdi Department of Computer Science LINFI Laboratory University of Biskra Biskra Algeria Department of information Systems and Security College of Information Technology UAE University Department of Computer Science College of Computing and Informatics University of Sharjah College of Business Al Ain University Al Ain UAE College of engineering Al Ain University Al Ain UAE

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

information technology (IT) plays an important role in the education field, with ubiquitous computing technology the ubiquitous learning (U-learning) is now a practical possibility. Cloud computing can offer many benefits to the learning field, as it provides all resources such as software (SaaS), platforms (PaaS), and infrastructure (IaaS) as a service. The multi-agent systems approach is an excellent way to implement flexible systems whose structure evolves dynamically. Based on the discussion of the benefits of cloud computing and the multi-agent systems approach to support adaptive and personalized learning content, the paper presents a hybrid approach based on cloud computing that uses the multi-tenant architecture where tenants share the system software. Multi-agent system is used in this approach to support a ubiquitous learning system. A multi-tenant SaaS application addresses the unique requirements of each tenant by enabling customization at the tenant level with the adoption of the concept of virtualization (virtual machines). This paper explores the challenges of achieving performance isolation in the context of multi-tenant SaaS applications. As an output of this study, U-learning on the cloud-based multi-agent system architecture has been proposed. To achieve lower cost of resources and server allocation and provide isolation among learners in the ubiquitous learning environment.

关键词： Cloud computing Software as a service computer architecture Ubiquitous computing Virtual machining System software Servers