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arXiv 2019年

作者： Hosseini, Saeid Najafipour, Saeed Cheung, Ngai-Man Yin, Hongzhi Kangavari, Mohammad Reza Zhou, Xiaofang ST Electronics - SUTD Cyber Security Lab. Singapore University of Technology and Design Singapore School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia Computational Cognitive Model Research Lab. School of Computer Engineering Iran University of Science and Technology Iran

Given a graph over which the contagions (e.g. virus, gossip) propagate, leveraging subgraphs with highly correlated nodes is beneficial to many applications. Yet, challenges abound. First, the propagation pattern between a pair of nodes may change in various temporal dimensions. Second, not always the same contagion is propagated. Hence, state-of-the-art text mining approaches ranging from similarity measures to topic-modeling cannot use the textual contents to compute the weights between the nodes. Third, the word-word co-occurrence patterns may differ in various temporal dimensions, which increases the difficulty to employ current word embedding approaches. We argue that inseparable multi-aspect temporal collaborations are inevitably needed to better calculate the correlation metrics in dynamical processes. In this work, we showcase a sophisticated framework that on the one hand, integrates a neural network based time-aware word embedding component that can collectively construct the word vectors through an assembly of infinite latent temporal facets, and on the other hand, uses an elaborate generative model to compute the edge weights through heterogeneous temporal attributes. After computing the intra-nodes weights, we utilize our Max-Heap Graph cutting algorithm to exploit subgraphs. We then validate our model through comprehensive experiments on real-world propagation data. The results show that the knowledge gained from the versatile temporal dynamics is not only indispensable for word embedding approaches but also plays a significant role in the understanding of the propagation behaviors. Finally, we demonstrate that compared with other rivals, our model can dominantly exploit the subgraphs with highly coordinated nodes. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Stance-In-Depth Deep Neural Approach to Stance Classification

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Procedia computer science 2018年 132卷 1646-1653页

作者： Gayathri Rajendran Bhadrachalam Chitturi Prabaharan Poornachandran Dept. of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kerala India Amrita Center for Cyber Security & Networks

Understanding the user intention from text is a problem of growing interest. The social media like Twitter, Facebook etc. extract user intention to analyze the behaviour of a user which in turn is employed for bot recognition, satire detection, fake news detection etc.. The process of identifying stance of a user from the text is called stance detection. This article compares the headline and body pair of a news article and classifies the pair as related or unrelated. The related pair is further classified into agree, disagree, discuss. We call related as detailed classification and unrelated as broad classification. We employ deep neural nets for feature extraction and stance classification. RNN models and its extensions showed significant variations in the classification of detailed class. Bidirectional LSTM model achieved the best accuracy for broad as well as detailed classification.

关键词： Stance Classification deep learning RNN LSTM Gated Recurrent Unit (GRU) Bidirectional word2vec

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A blockchain-based framework for detecting malicious mobile applications in app stores

arXiv

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arXiv 2019年

作者： Homayoun, Sajad Dehghantanha, Ali Parizi, Reza M. Choo, Kim-Kwang Raymond Department of Computer Engineering and Information Technology Shiraz University of Technology Shiraz Iran School of Computer Science University of Guelph Ontario Canada Department of Software Engineering Game Development Kennesaw State University MariettaGA30060 United States Department of Information Systems Cyber Security Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX78249 United States

The dramatic growth in smartphone malware shows that malicious program developers are shifting from traditional PC systems to smartphone devices. Therefore, security researchers are also moving towards proposing novel antimalware methods to provide adequate protection. This paper proposes a Blockchain-Based Malware Detection Framework (B2MDF) for detecting malicious mobile applications in mobile applications marketplaces (app stores). The framework consists of two internal and external private blockchains forming a dual private blockchain as well as a consortium blockchain for the final decision. The internal private blockchain stores feature blocks extracted by both static and dynamic feature extractors, while the external blockchain stores detection results as blocks for current versions of applications. B2MDF also shares feature blocks with third parties, and this helps antimalware vendors to provide more accurate solutions. Copyright © 2019, The Authors. All rights reserved.

关键词： Blockchain

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Automated verification of noninterference property 20th

Automated verification of noninterference property

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20th International Conference on Information and Communications security, ICICS 2018

作者： Zhang, Fan Zhang, Cong Xu, Mingdi Liu, Xiaoli Hu, Fangning Chao, HanChieh Mathematics and Computer Science School Wuhan Polytechnic University Wuhan China School of Computer Science and Electrical Engineering Jacobs University Bremen Bremen Germany Wuhan Digital and Engineering Institute Wuhan China College of Information Science and Technology/College of Cyber Security Jinan University Guangzhou510632 China Department of Electrical Engineering National Dong Hwa University Hualien Taiwan Department of Computer Science and Information Engineering National Ilan University Ilan Taiwan

ISBN: (纸本)9783030019495

Noninterference is an important information flow model that is widely applied in building secure information systems. Although the noninterference model itself has been thoroughly investigated, verifying the noninterference property in an efficient and automated manner remains an open problem. In this study, we explore the noninterference verification problem from the perspective of the state-equivalence relations between two automata running synchronously. Our results are as follows. (1) To the best of our knowledge, we are the first to propose a recursive form of the necessary and sufficient condition of noninterference. We also for the first time disclose the fact that Rushby’s definition of noninterference model can also be formalized as a bi-simulation (over two automata) (2) We present an automated noninterference verification algorithm. The algorithm can finish the verification within O(|S|2 * |D|), where |D| is the number of security domains and |S| is the number of states. The time-complexity of our algorithm is the best among other existing studies. © Springer Nature Switzerland AG 2018.

关键词： Computational complexity

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Model for efficient development of security audit criteria 17

Model for efficient development of security audit criteria

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17th European Conference on cyber Warfare and security, ECCWS 2018

作者： Kelo, Tomi Eronen, Juhani Rousku, Kimmo Department of Pervasive Computing Tampere University of Technology Finland Department of Computer Science and Engineering University of Oulu Finland National Cyber Security Centre Finland Finnish Communications Regulatory Authority Finland Population Register Centre Finland

ISBN: (纸本)9781911218852

cyber-attacks have grown in importance to become a matter of national security. A growing number of states and organisations around the world have been developing defensive and offensive capabilities for cyber warfare. security criteria are important tools for defensive capabilities of critical communications and information systems (CIS). Various criteria have been developed for designing, implementing and auditing CIS. However, the development of criteria is inadequately supported by currently available guidance. The relevant guidance is mostly related to criteria selection. The abstraction level of the guidance is high. This may lead to inefficient criteria development work. In addition, the resulting criteria may not fully meet their goals. To ensure efficient criteria development, the guidance should be supported with concrete level implementation guidelines. This paper proposes a model for efficient development of security audit criteria. The model consists of criteria design goals and concrete implementation guidelines to achieve these goals. The model is based on the guidance given by ISACA and on the criteria development work by FICORA, the Finnish Communications Regulatory Authority. During the years 2008-2017, FICORA has actively participated in development and usage of three versions of Katakri, the Finnish national security audit criteria. The paper includes a case study that applies the model to existing security criteria. The case study covers a review of the criteria composed of the Finnish VAHTI-instructions. During the review, all supported design goals and implementation guidelines of the model were scrutinised. The results of the case study indicate that the model is useful for reviewing existing criteria. The rationale is twofold. First, several remarkable shortcomings were identified. Second, the identification process was time-efficient. The results also suggest that the model would be useful for criteria under development. Addressing the iden

关键词： National security

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Shielding collaborative learning: Mitigating poisoning attacks through client-side detection

arXiv

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arXiv 2019年

作者： Zhao, Lingchen Hu, Shengshan Wang, Qian Jiang, Jianlin Shen, Chao Luo, Xiangyang School of Cyber Science and Engineering Wuhan University Wuhan Hubei430072 China School of Computer Science Wuhan University Wuhan Hubei430072 China School of Cyberspace Security Xian Jiaotong University Xi’an Shaanxi710049 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450002 China

Collaborative learning allows multiple clients to train a joint model without sharing their data with each other. Each client performs training locally and then submits the model updates to a central server for aggregation. Since the server has no visibility into the process of generating the updates, collaborative learning is vulnerable to poisoning attacks where a malicious client can generate a poisoned update to introduce backdoor functionality to the joint model. The existing solutions for detecting poisoned updates, however, fail to defend against the recently proposed attacks, especially in the non-IID setting. In this paper, we present a novel defense scheme to detect anomalous updates in both IID and non-IID settings. Our key idea is to realize client-side cross-validation, where each update is evaluated over other clients’ local data. The server will adjust the weights of the updates based on the evaluation results when performing aggregation. To adapt to the unbalanced distribution of data in the non-IID setting, a dynamic client allocation mechanism is designed to assign detection tasks to the most suitable clients. During the detection process, we also protect the client-level privacy to prevent malicious clients from stealing the training data of other clients, by integrating differential privacy with our design without degrading the detection performance. Our experimental evaluations on two real-world datasets show that our scheme is significantly robust to two representative poisoning attacks. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Erratum to “Quantifying the need for supervised machine learning in conducting live forensics analysis of emergent configurations (ECO) in IoT environments” [Forensic Sci. Int.: Rep. 2 (2020) 100122]

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Forensic science International: Reports 2020年 2卷 100135-100135页

作者： Kebande, Victor R. Ikuesan, Richard A. Karie, Nickson M. Alawadi, Sadi Choo, Kim-Kwang Raymond Al-Dhaqm, Arafat Department of Computer Science Malmö University Sweden Cyber and Network Security Department Science and Technology Division Community College of Qatar Qatar School of Science Edith Cowan University Australia Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio TX 78249-0631 USA School of Computing Faculty of Engineering Universiti Teknologi Malysia Johor Malaysia

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Relation Structure-Aware Heterogeneous Graph Neural Network

Relation Structure-Aware Heterogeneous Graph Neural Network

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IEEE International Conference on Data Mining (ICDM)

作者： Shichao Zhu Chuan Zhou Shirui Pan Xingquan Zhu Bin Wang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Faculty of Information Technology Monash University Melbourne Australia Dept. of Computer & Electrical Engineering and Computer Science Florida Atlantic University Boca Raton USA Xiaomi AI Lab Beijing China

Heterogeneous graphs with different types of nodes and edges are ubiquitous and have immense value in many applications. Existing works on modeling heterogeneous graphs usually follow the idea of splitting a heterogeneous graph into multiple homogeneous subgraphs. This is ineffective in exploiting hidden rich semantic associations between different types of edges for large-scale multi-relational graphs. In this paper, we propose Relation Structure-Aware Heterogeneous Graph Neural Network (RSHN), a unified model that integrates graph and its coarsened line graph to embed both nodes and edges in heterogeneous graphs without requiring any prior knowledge such as metapath. To tackle the heterogeneity of edge connections, RSHN first creates a Coarsened Line Graph Neural Network (CL-GNN) to excavate edge-centric relation structural features that respect the latent associations of different types of edges based on coarsened line graph. After that, a Heterogeneous Graph Neural Network (H-GNN) is used to leverage implicit messages from neighbor nodes and edges propagating among nodes in heterogeneous graphs. As a result, different types of nodes and edges can enhance their embedding through mutual integration and promotion. Experiments and comparisons, based on semi-supervised classification tasks on large scale heterogeneous networks with over a hundred types of edges, show that RSHN significantly outperforms state-of-the-arts.

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E-trade Management System Architecture

E-trade Management System Architecture

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IEEE International Conference on Advanced Trends in Information Theory (ATIT)

作者： Oleg Pursky Anna Selivanova Oleksander Kharchenko Pavlo Demidov Volodymyr Kulazhenko department of computer science Kyiv national university of trade and economics Kyiv Ukraine department of software engineering and cyber security Kyiv national university of trade and economics Kyiv Ukraine department of cybernetics and system analysis Kyiv national university of trade and economics Kyiv Ukraine

ISBN: (数字)9781728161440

ISBN: (纸本)9781728161457

In this work we present the model for construction of e-trade management system architecture which consists of two basic blocks: the landing Web-pages management unit of the online store is responsible for interacting with the user, receiving orders and collecting primary information and Web-enterprise management unit, responsible for processing orders, their delivery, and all enterprise's business processes realization. We have focused our attention to developing an architecture model for e-trady management system. A service-oriented architecture (SOA) using enterprise service bus (ESB) concept is offered for architectural implementation. In developed e-trade management system architecture we used the method of integrating complex information systems within the information environment based on SOA and ESB technologies, which represents an independent, integrative subsystem for relatively integrated information systems. Typical information systems that belong to the e-trade system are used as services. These web-based services should be developed and used to enhance ESB efficiency. Such web-services implement SOAP and use WSDL (Web Services Description Language), as well as UDDI specification (Universal Description, Discovery and Integration). At that, each typical information system is simultaneously data supplier and data consumer. Each information system handles its own data class, while being able to both transmit and receive, adjust and modify data according to operational information from other information systems within main business processes.

关键词： Service-oriented architecture Information systems Web services

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Coherence-based automated essay scoring using self-attention 17th

Coherence-based automated essay scoring using self-attention

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17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018

作者： Li, Xia Chen, Minping Nie, Jianyun Liu, Zhenxing Feng, Ziheng Cai, Yingdan Key Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China School of Information Science and Technology/School of Cyber Security Guangdong University of Foreign Studies Guangzhou China Department of Computer Science and Operations Research University of Montreal Montreal Canada

ISBN: (纸本)9783030017156

Automated essay scoring aims to score an essay automatically without any human assistance. Traditional methods heavily rely on manual feature engineering, making it expensive to extract the features. Some recent studies used neural-network-based scoring models to avoid feature engineering. Most of them used CNN or RNN to learn the representation of the essay. Although these models can cope with relationships between words within a short distance, they are limited in capturing long-distance relationships across sentences. In particular, it is difficult to assess the coherence of the essay, which is an essential criterion in essay scoring. In this paper, we use self-attention to capture useful long-distance relationships between words so as to estimate a coherence score. We tested our model on two datasets (ASAP and a new non-native speaker dataset). In both cases, our model outperforms the existing state-of-the-art models. © Springer Nature Switzerland AG 2018.

关键词： Automation

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