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arXiv 2021年

作者： Li, Qian Li, Jianxin Sheng, Jiawei Cui, Shiyao Wu, Jia Hei, Yiming Peng, Hao Guo, Shu Wang, Lihong Beheshti, Amin Yu, Philip S. The School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The Institute of Information Engineering Chinese Academy of Sciences Beijing100083 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The School of Computing Macquarie University Sydney Australia The Department of Computer Science University of Illinois at Chicago Chicago60607 United States

Event extraction is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, event extraction based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain event extraction based on deep learning models. We introduce a new literature classification of current general domain event extraction research according to the task definition. Afterwards, we summarize the paradigm and models of event extraction approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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Why Do Developers Remove Lambda Expressions in Java? 21

Why Do Developers Remove Lambda Expressions in Java?

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IEEE International Conference on Automated Software engineering (ASE)

作者： Mingwei Zheng Jun Yang Ming Wen Hengcheng Zhu Yepang Liu Hai Jin Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China The Hong Kong University of Science and Technology Hong Kong China Southern University of Science and Technology Shenzhen China Cluster and Grid Computing Lab School of Computer Science and Technology HUST Wuhan China

ISBN: (纸本)9781665447843

Java 8 has introduced lambda expressions, a core feature of functional programming. Since its introduction, there is an increasing trend of lambda adoptions in Java projects. Developers often adopt lambda expressions to simplify code, avoid code duplication or simulate other functional features. However, we observe that lambda expressions can also incur different types of side effects (i.e., performance issues and memory leakages) or even severe bugs, and developers also frequently remove lambda expressions in their implementations. Consequently, the advantages of utilizing lambda expressions can be significantly compromised by the collateral side effects. In this study, we present the first large-scale, quantitative and qualitative empirical study to characterize and understand inappropriate usages of lambda expressions. Particularly, we summarized seven main reasons for the removal of lambdas as well as seven common migration patterns. For instance, we observe that lambdas using customized functional interfaces are more likely to be removed by developers. Moreover, from a complementary perspective, we performed a user study over 30 developers to seek the underlying reasons why they remove lambda expressions in practice. Finally, based on our empirical results, we made suggestions on scenarios to avoid lambda usages for Java developers and also pointed out future directions for researchers.

关键词： Java Codes computer bugs Market research Functional programming Open source software Software engineering

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Dynamically redactable blockchain based on decentralized Chameleon hash

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Digital Communications and Networks 2024年

作者： Xinzhe Huang Yujue Wang Yong Ding Qianhong Wu Changsong Yang Hai Liang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin 541004 China Hangzhou Innovation Institute of Beihang University Hangzhou 311228 China Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong 999077 China School of Cyber Science and Technology Beihang University Beijing 100083 China

The immutability is a crucial property for blockchain applications, however, it also leads to problems such as the inability to revise illegal data on the blockchain and delete private data. Although redactable blockchains enable on-chain modification, they suffer from inefficiency and excessive centralization, the majority of redactable blockchain schemes ignore the difficult problems of traceability and consistency check. In this paper, we present a Dynamically Redactable Blockchain based on decentralized Chameleon hash (DRBC). Specifically, we propose an Identity-Based Decentralized Chameleon Hash (IDCH) and a Version-Based Transaction structure (VT) to realize the traceability of transaction modifications in a decentralized environment. Then, we propose an efficient block consistency check protocol based on the Bloom filter tree, which can realize the consistency check of transactions with extremely low time and space cost. security analysis and experiment results demonstrate the reliability of DRBC and its significant advantages in a decentralized environment.

关键词： Privacy protection Redactable blockchain Chameleon hash Consistency check Scalability

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MalScan: Android Malware Detection Based on Social-Network Centrality Analysis

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Yueming Suo, Wenqi Feng, Siyue Zou, Deqing Yang, Wei Liu, Yang Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Jinyinhu Laboratory Wuhan430074 China University of Texas at Dallas United States Nanyang Technological University Singapore Huazhong University of Science and Technology 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 Wuhan430074 China

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use syntax-based features that can be evaded by transformation attacks or semantic-based features which are usually extracted by expensive program analysis. Therefore, to address the scalability challenges of traditional heavyweight static analysis, we propose a graph-based lightweight approach MalScan for Android malware detection. MalScan considers the function call graph as a complex social network and employs centrality analysis on sensitive application program interfaces (APIs) to express the semantic characteristics of the graph. On this basis, machine learning algorithms and ensemble learning algorithms are applied to classify the extracted features. We evaluate MalScan on datasets of 104,892 benign apps and 108,640 malwares, and the results of experiments indicate that MalScan outperforms six state-of-the-art detectors and can quickly detect Android malware with an f-value as high as 99%. In addition, there are also significant improvements in the robustness of Android app evolution and robustness to obfuscation. Finally, we conduct an exhaustive statistical study of over one million applications in the Google-Play app market and successfully identify 498 zero-day malware, which further validates the feasibility of MalScan on market-wide malware scanning. © 2004-2012 IEEE.

关键词： Android malware

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Event extraction by associating event types and argument roles

arXiv

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arXiv 2021年

作者： Li, Qian Guo, Shu Wu, Jia Li, Jianxin Sheng, Jiawei Wang, Lihong Dong, Xiaohan Peng, Hao Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Department of Computing Macquarie University Sydney Australia Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Event extraction (EE), which acquires structural event knowledge from texts, can be divided into two sub-tasks: event type classification and element extraction (namely identifying triggers and arguments under different role patterns). As different event types always own distinct extraction schemas (i.e., role patterns), previous work on EE usually follows an isolated learning paradigm, performing element extraction independently for different event types. It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles. This paper proposes a novel neural association framework for the EE task. Given a document, it first performs type classification via constructing a document-level graph to associate sentence nodes of different types, and adopting a graph attention network to learn sentence embeddings. Then, element extraction is achieved by building a universal schema of argument roles, with a parameter inheritance mechanism to enhance role preference for extracted elements. As such, our model takes into account type and role associations during EE, enabling implicit information sharing among them. Experimental results show that our approach consistently outperforms most state-of-the-art EE methods in both sub-tasks. Particularly, for types/roles with less training data, the performance is superior to the existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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Towards Efficient Co-audit of Privacy-Preserving Data on Consortium Blockchain via Group Key Agreement

Towards Efficient Co-audit of Privacy-Preserving Data on Con...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Xiaoyan Hu Xiaoyi Song Guang Cheng Jian Gong Lu Yang Honggang Chen Zhichao Liang School of Cyber Science & Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University) China Research Base of International Cyberspace Governance (Southeast University) Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Jiangsu Province Engineering Research Center of Security for Ubiquitous Network Nanjing China JiangSu Peerfintech Technology Co. Ltd.

ISBN: (纸本)9781665406697

Blockchain is well known for its storage consistency, decentralization and tamper-proof, but the privacy disclosure and difficulty in auditing discourage the innovative application of blockchain technology. As compared to public blockchain and private blockchain, consortium blockchain is widely used across different industries and use cases due to its privacy-preserving ability, auditability and high transaction rate. However, the present co-audit of privacy-preserving data on consortium blockchain is inefficient. Private data is usually encrypted by a session key before being published on a consortium blockchain for privacy preservation. The session key is shared with transaction parties and auditors for their access. For decentralizing auditorial power, multiple auditors on the consortium blockchain jointly undertake the responsibility of auditing. The distribution of the session key to an auditor requires individually encrypting the session key with the public key of the auditor. The transaction initiator needs to be online when each auditor asks for the session key, and one encryption of the session key for each auditor consumes resources. This work proposes GAChain and applies group key agreement technology to efficiently co-audit privacy-preserving data on consortium blockchain. Multiple auditors on the consortium blockchain form a group and utilize the blockchain to generate a shared group encryption key and their respective group decryption keys. The session key is encrypted only once by the group encryption key and stored on the consortium blockchain together with the encrypted private data. Auditors then obtain the encrypted session key from the chain and decrypt it with their respective group decryption key for co-auditing. The group key generation is involved only when the group forms or group membership changes, which happens very infrequently on the consortium blockchain. We implement the prototype of GAChain based on Hyperledger Fabric framework. Our

关键词： Industries Privacy Data privacy Distributed ledger Public key Prototypes Fabrics

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SEGIVE: A Practical Framework of Secure GPU Execution in Virtualization Environment

SEGIVE: A Practical Framework of Secure GPU Execution in Vir...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Ziyang Wang Fangyu Zheng Jingqiang Lin Guang Fan Jiankuo Dong State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center CAS Beijing China School of Cyber Security University of Science and Technology of China Hefei School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728198293

ISBN: (纸本)9781728198309

With the advancement of processor technology, general-purpose GPUs have become popular parallel computing accelerators in the cloud. However, designed for graphics rendering and high-performance computing, GPUs are born without sound security mechanisms. Consequently, the GPU-based service in the cloud is vulnerable to attacks from the potentially compromised guest OS as large amounts of sensitive code and data are offloaded directly to the unprotected *** this paper, we propose SEGIVE, a practical framework of secure GPU execution in the virtualization environment, which protects offloaded device code and data from disclosure or tampering by malicious guest OSes through the full life cycle of security-critical GPU applications. First, SEGIVE secures all the traffic transferred to GPUs with Intel SGX technology, including the users' sensitive data and GPU binaries. Second, with various memory isolation mechanisms, SEGIVE enhances security in multi-user execution scenarios by sharing a GPU among multiple workloads, which avoids underutilization of device resources. Besides, SEGIVE requires no modifications to application source codes, the GPU architecture, or I/O interconnection to fulfill security principles, and thus almost all prevailing GPU-based applications can easily benefit from SEGIVE with little porting effort. We have implemented SEGIVE with KVM-QEMU on off-the-shelf NVIDIA GPUs and CPUs. Evaluation results show that with security-enhances, the performance of SEGIVE prototype is still competitive to the native execution on compute-intensive applications, especially for the public-key cryptography algorithm.

关键词： Cloud computing Graphics processing units computer architecture Public key cryptography Software security Virtualization

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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 trust chain assessment method based on blockchain for SDN network nodes 3

A trust chain assessment method based on blockchain for SDN ...

引用

3rd IEEE International Conference on Smart Internet of Things, SmartIoT 2019

作者： Liu, Yifan Zhao, Bo Li, Xiaofei Wang, Shuo Zhang, Bin Liu, Zhenpeng School of Cyber Science and Engineering Wuhan University Wuhan China Information Technology Center Hebei University Baoding China School of Cyberspace Security and Computer Hebei University Baoding China

ISBN: (纸本)9781728134888

The SDN node can complete the forwarding task efficiently and reliably does not attract enough attention. A new method called TrustBlock was proposed, which introduces the SDN node trust evaluation and block chain to ensure the trust value authenticity, irreparability and openness. The weight of each evaluation attribute is determined by the entropy method, which avoids the problem that the subjective judgment method is not adaptable to the weight setting. Simulation results show that TrustBlock can effectively identify untrusted nodes in SDN networks. © 2019 IEEE.

关键词： Software defined networking

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Transferring knowledge distillation for multilingual social event detection

arXiv

引用

arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

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