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arXiv 2024年

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

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. © 2024, CC BY.

关键词： Bamboo

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HomDroid: Detecting android covert malware by social-network homophily analysis

arXiv

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arXiv 2021年

作者： Wu, Yueming Zou, Deqing Yang, Wei Li, Xiang Jin, Hai Huazhong University of Science and Technology China University of Texas at Dallas United States Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Hust Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hust Wuhan430074 China Shenzhen Hust Research Institute Shenzhen518057 China Cluster and Grid Computing Lab School of Computer Science and Technology Hust Wuhan430074 China

Android has become the most popular mobile operating system. Correspondingly, an increasing number of Android malware has been developed and spread to steal users' private information. There exists one type of malware whose benign behaviors are developed to camouflage malicious behaviors. The malicious component occupies a small part of the entire code of the application (app for short), and the malicious part is strongly coupled with the benign part. In this case, the malware may cause false negatives when malware detectors extract features from the entire apps to conduct classification because the malicious features of these apps may be hidden among benign features. Moreover, some previous work aims to divide the entire app into several parts to discover the malicious part. However, the premise of these methods to commence app partition is that the connections between the normal part and the malicious part are weak (e.g., repackaged malware). In this paper, we call this type of malware as Android covert malware and generate the first dataset of covert malware. To detect covert malware samples, we first conduct static analysis to extract the function call graphs. Through the deep analysis on call graphs, we observe that although the correlations between the normal part and the malicious part in these graphs are high, the degree of these correlations has a unique range of distribution. Based on the observation, we design a novel system, HomDroid, to detect covert malware by analyzing the homophily of call graphs. We identify the ideal threshold of correlation to distinguish the normal part and the malicious part based on the evaluation results on a dataset of 4,840 benign apps and 3,385 covert malicious apps. According to our evaluation results, HomDroid is capable of detecting 96.8% of covert malware while the False Negative Rates of another four stateof- the-art systems (i.e., PerDroid, Drebin, MaMaDroid, and IntDroid) are 30.7%, 16.3%, 15.2%, and 10.4%, respectiv

关键词： Malware

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A Shortest Path Query Approach for Encrypted Graphs Based on Padding Dictionary Structure

A Shortest Path Query Approach for Encrypted Graphs Based on...

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Chinese Automation Congress (CAC)

作者： Ming Yang Kaiyang Zhang Chao Mu Xin Wang Yuanlong Liu Heng Zhang Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Grid Shandong Electric Power Company Jinan China School of Computer Engineering Jiangsu Ocean University Lianyungang China

Hybrid intelligence has emerged as an innovative approach that synergistically combines the strengths of human intelligence and artificial intelligence (AI) to address complex problems. In this paradigm, knowledge graphs play a pivotal role by supplying vital background information and contextual understanding to hybrid intelligence systems. However, due to limitations in local storage capacity, managing large-scale graph data often requires outsourcing to cloud servers. Nevertheless, the use of untrusted cloud servers poses a risk of potential user data leaks. To address these concerns, this paper presents an encryption scheme specifically designed to protect graph data, enabling queries involving intermediate nodes in the shortest path between two nodes within the graph. The proposed scheme ensures the privacy and security of outsourced data by employing a novel method that utilizes padding dictionary structure encryption. This method ensures strict limitations on the cloud server's capabilities, restricting it solely to query operations, it effectively prevents any compromise of user data or unauthorized access to sensitive information, users can securely store and process their graph data on servers, significantly reducing the potential risk of data leakage and unauthorized breaches. To validate the feasibility and effectiveness of the proposed solution, practical experiments were conducted using real-world data.

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Securing Sdn/Nfv-Enabled Campus Networks with Software-Defined Perimeter-Based Zero-Trust Architecture

SSRN

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SSRN 2023年

作者： Ruambo, Francis A. Zou, Deqing Lopes, Ivandro O. Yuan, Bin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on Big Data Security Wuhan China National Engineering Research Center for Big Data Technology and System Wuhan China Services Computing Technology and System Lab Wuhan China Cluster and Grid Computing Lab Wuhan China Mbeya university of Science and Technology Mbeya131 Tanzania United Republic of Nucleo Operacional para a Sociedade de Informacao Cape Verde Songshan Laboratory Zhengzhou China

Network softwarization is a breakthrough in designing modern networks and providing numerous new network operations and services. This change is exemplified by Software Defined Networks (SDN) and Network Function Virtualization (NFV). As a result, the combination of SDN and NFV (SDN/NFV) frameworks and individual SDN and NFV frameworks have gained substantial traction in network security over the past decade, significantly supporting on-demand protection measures. However, the inherent security shortcomings of SDN, NFV, and SDN/NFV frameworks provide the fundamental issue in implementing security procedures via network softwarization. To solve this issue, we present a Software-Defined Perimeter (SDP)-powered Zero-Trust Architecture (ZTA) to improve security in the SDN/NFV-enabled campus network holistically. In addition to this, we also propose a threat model and quantitatively assess the suggested approach's performance and security. Furthermore, we highlight potential zero-trust network security function implementations in SDN/NFV-enabled infrastructure to increase protection for endpoint devices, network resources, and flows for detecting threats and responding effectively with minimal latency and overhead. © 2023, The Authors. All rights reserved.

关键词： Access control

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基于污点和概率的逃逸恶意软件多路径探索

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Security and Safety 2023年第3期2卷 83-106页

作者：徐钫洲张网羌卫中金海 National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Lab Cluster and Grid Computing LabWuhan 430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecurityWuhan 430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Jinyinhu Laboratory Wuhan 430040China

Static analysis is often impeded by malware obfuscation techniques,such as encryption and packing,whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution ***,malware can employ evasive techniques to detect the analysis environment and alter its behavior *** known evasive techniques can be explicitly dismantled,the challenge lies in generically dismantling evasions without full knowledge of their conditions or implementations,such as logic bombs that rely on uncertain conditions,let alone unsupported evasive techniques,which contain evasions without corresponding dismantling strategies and those leveraging unknown *** this paper,we present Antitoxin,a prototype for automatically exploring evasive *** utilizes multi-path exploration guided by taint analysis and probability calculations to effectively dismantle evasive *** probabilities of branch execution are derived from dynamic coverage,while taint analysis helps identify paths associated with evasive techniques that rely on uncertain ***,Antitoxin prioritizes branches with lower execution probabilities and those influenced by taint analysis for multi-path *** is achieved through forced execution,which forcefully sets the outcomes of branches on selected ***,Antitoxin employs active anti-evasion countermeasures to dismantle known evasive techniques,thereby reducing exploration ***,Antitoxin provides valuable insights into sensitive behaviors,facilitating deeper manual *** experiments on a set of highly evasive samples demonstrate that Antitoxin can effectively dismantle evasive techniques in a generic *** probability calculations guide the multi-path exploration of evasions without requiring prior knowledge of their conditions or implementations,enabling the dismantling of unsupported techniques such as C2 and signific

关键词： Malware analysis dynamic binary instrumentation forced execution taint analysis evasion detection

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gECC: A GPU-based high-throughput framework for Elliptic Curve Cryptography

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ACM Transactions on Architecture and Code Optimization 1000年

作者： Qian Xiong Weiliang Ma Xuanhua Shi Yongluan Zhou Hai Jin Kaiyi Huang Haozhou Wang Zhengru Wang Huazhong University of Science and Technology Wuhan China School of Computer Huazhong University of Science and Technology Wuhan China University of Copenhagen Copenhagen Denmark School of computer science and technology Huazhong University of Science and Technology Services Computing Technology and System Laboratory / Cluster and Grid Computing Laboratory Wu hanwu China NVIDIA Corp Shanghai China

Elliptic Curve Cryptography (ECC) is an encryption method that provides security comparable to traditional techniques like Rivest–Shamir–Adleman (RSA) but with lower computational complexity and smaller key sizes, making it a competitive option for applications such as blockchain, secure multi-party computation, and database security. However, the throughput of ECC is still hindered by the significant performance overhead associated with elliptic curve (EC) operations, which can affect their efficiency in real-world scenarios. This paper presents gECC, a versatile framework for ECC optimized for GPU architectures, specifically engineered to achieve high-throughput performance in EC operations. To maximize throughput, gECC incorporates batch-based execution of EC operations and microarchitecture-level optimization of modular arithmetic. It employs Montgomery’s trick [40] to enable batch EC computation and incorporates novel computation parallelization and memory management techniques to maximize the computation parallelism and minimize the access overhead of GPU global memory. Furthermore, we analyze the primary bottleneck in modular multiplication by investigating how the user codes of modular multiplication are compiled into hardware instructions and what these instructions’ issuance rates are. We identify that the efficiency of modular multiplication is highly dependent on the number of Integer Multiply-Add (IMAD) instructions. To eliminate this bottleneck, we propose novel techniques to minimize the number of IMAD instructions by leveraging predicate registers to pass the carry information and using addition and subtraction instructions (IADD3) to replace IMAD instructions. Our experimental results show that, for ECDSA and ECDH, the two commonly used ECC algorithms, gECC can achieve performance improvements of 5.56 × and 4.94 ×, respectively, compared to the state-of-the-art GPU-based system. In a real-world blockchain application, we can achieve performance impr

关键词： Elliptic curve cryptography modular multiplication GPU acceleration

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Corrigendum to “Fine-Grained Control-Flow Integrity Based on Points-to Analysis for CPS”

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Security and Communication Networks 2018年第1期2018卷

作者： Weizhong Qiang Shizhen Wang Hai Jin Jiangying Zhong Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 ***

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A Resilient Decentralized Learning Approach against Byzantine Attack via Reputation Evaluation

A Resilient Decentralized Learning Approach against Byzantin...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Heng Zhang Wenting Wang Xuguo Jiao Ming Yang Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan P. R. China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan P. R. China College of Control Science and Engineering Zhejiang University Hangzhou China State Grid Shandong Electric Power Research Institute Jinan China School of Information and Control Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Machine learning is a key component of many applications, among which decentralized learning has attracted wide attention because of its cost-effectiveness and high efficiency. However, decentralized learning is vulnerable to various Byzantine attacks. Existing defense models face challenges in defending against complex attacks and are often constrained by strict network topologies. To address this issue, a parameter aggregation rule based on reputation evaluation (RPV) is proposed in this paper. This rule establishes a reputation model for neighboring nodes and continuously updates it based on their distance performance during each iteration. By applying the reputation model of neighboring nodes to the Beta distribution, their reliability is obtained. Based on the reliability of neighboring nodes, the set of reliable nodes for this iteration and select parameter values from them for aggregation are determined. Finally, we gradually eliminate the influence of Byzantine nodes. Our experimental results on the MNIST dataset demonstrate that the proposed algorithm is resilient to attacks from any number of Byzantine nodes and outperforms previous defense models in terms of network topology constraints, training accuracy, and computational costs.

关键词： Training Learning systems Accuracy Network topology Computational modeling Machine learning Classification algorithms

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AMSPM: Adaptive Model Selection and Partition Mechanism for Edge Intelligence-driven 5G Smart City with Dynamic computing Resources

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ACM Transactions on Sensor Networks 1000年

作者： Xin Niu Xuejiao Cao Chen Yu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However, the constantly changing available computing resources of end devices and edge servers cannot continuously guarantee the performance of intelligent inference. In order to guarantee the sustainability of intelligent services in smart city, we propose the Adaptive Model Selection and Partition Mechanism (AMSPM) in 5G smart city where EI provides services, which mainly consists of Adaptive Model Selection (AMS) and Adaptive Model Partition (AMP). In AMSPM, the model selection and partition of deep neural network (DNN) are formulated as an optimization problem. Firstly, we propose a recursive-based algorithm named AMS based on the computing resources of edge devices to derive an appropriate DNN model that satisfies the latency demand of intelligent services. Then, we adaptively partition the selected DNN model according to the computing resources of edge devices. The experimental results demonstrate that, when compared with state-of-the-art model selection and partition mechanisms, AMSPM not only reduces latency but also enhances computing resource utilization.

关键词： Edge intelligence changing available computing resources adaptive model selection and partition smart city sustainable intelligent services

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Corrigendum to “Improved Algorithm for Permutation Testing” [Theoretical computer science 986 (2024) 114316]

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Theoretical computer science 2024年 999卷

作者： Xiaojin Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

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