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New security Attack and Defense Mechanisms Based on Negative Logic System and Its Applications

New Security Attack and Defense Mechanisms Based on Negative...

第一届网络空间安全科学国际会议(The 1st International Conference on Science of cyber security-SciSec 2018)

作者： Yexia Cheng Yuejin Du Jin Peng Shen He Jun Fu Baoxu Liu Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Secur Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Secur Department of Security Technology China Mobile Research InstituteBeijingChina Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Secur

Agenda Introduction Negative Logic System security Attack and Defense Mechanisms Based on NLS Applications of Attack and Defense Mechanisms Based on NLS Conclusion

ISBN: (纸本)9783030030254

Agenda Introduction Negative Logic System security Attack and Defense Mechanisms Based on NLS Applications of Attack and Defense Mechanisms Based on NLS Conclusion

关键词： Negative logic system security attack and defense mechanisms Application Active probe response Defence mechanisms security cyberspace security Active detection

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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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Modeling cyber-physical attacks on water networks with epanetCPA 1

Modeling cyber-physical attacks on water networks with epane...

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1st International Joint Conference in Water Distribution Systems Analysis and Computing and Control in the Water Industry, WDSA/CCWI 2018

作者： Taormina, R. Galelli, S. Douglas, H.C. Tippenhauer, N.O. Salomons, E. Ostfeld, A. ITrust Centre for Research in Cyber Security Singapore University of Technology and Design Singapore487372 Singapore Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore487372 Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore OptiWater Haifa Israel Faculty of Civil and Environmental Engineering Technion-Israel Institute of Technology Haifa32000 Israel

In this work, we discuss modeling of cyber-physical attacks and their effects on the hydraulic processes of water networks. In particular, we introduce our software epanetCPA, that allows users to quickly develop plausible attack scenarios and assess the hydraulic response of the system. epanetCPA is an open-source MATLABR toolbox, and leverages EPANET for both demand-driven and pressuredriven analysis. The toolbox extends EPANET's features to include a cyber layer of sensors, actuators, controllers, and a centralized SCADA system. The nodes of the cyber network and the attack scenarios to be simulated are defined in an additional input file, which has to be provided along with the EPANET input file describing the physical network. epanetCPA implements a wide range of cyberphysical threats such as denial-of-service of resources, manipulation of sensor readings, as well as eavesdropping, interruption and alteration of the communications between the cyber components. © 2018 1st International WDSA / CCWI 2018 Joint Conference. All rights reserved.

关键词： cyber Physical System

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Distortion-free watermarking scheme for compressed data in columnar database 15

Distortion-free watermarking scheme for compressed data in c...

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15th International Joint Conference on e-Business and Telecommunications, ICETE 2018

作者： Yaqub, Waheeb Kamel, Ibrahim Aung, Zeyar Center for Cyber Security New York University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science Khalifa University of Science and Technology Masdar Institute Abu Dhabi United Arab Emirates

ISBN: (纸本)9789897583193

Digital watermarking is an effective technique for protecting various databases against data copyright infringement, piracy, and data tampering in the columnar database. Increasing deployments of various database systems and their versatile applications have raised the need for better watermarking schemes that are tailored to the target database systems’ specific architecture. Most of the existing digital watermarking schemes do not take into consideration the side effects that watermarking might have on the database’s important characteristics such as data compression and overall performance. In this research, we propose a distortion-free fragile watermarking scheme for columnar database architecture without interfering its underlying data compression scheme and its overall performance. The proposed scheme is flexible and can be adapted to various distributions of data. We tested our proposed scheme on both synthetic and real-world data, and proved its effectiveness. Copyright © 2018 by SCITEPRESS – Science and technology Publications, Lda. All rights reserved

关键词： Database systems

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Joint extraction of entities and relations based on a novel decomposition strategy

arXiv

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arXiv 2019年

作者： Yu, Bowen Zhang, Zhenyu Su, Jianlin Wang, Yubin Liu, Tingwen Wang, Bin Li, Sujian Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Zhuiyi Technology Co. Ltd. Shenzhen China Xiaomi AI Lab Xiaomi Inc. Beijing China Key Laboratory of Computational Linguistics Peking University MOE China

Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model. Prior works typically solve this task in the extract-then-classify or unified labeling manner. However, these methods either suffer from the redundant entity pairs, or ignore the important inner structure in the process of extracting entities and relations. To address these limitations, in this paper, we first decompose the joint extraction task into two inner-related subtasks, namely HE extraction and TER extraction. The former subtask is to distinguish all head-entities that may be involved with target relations, and the latter is to identify corresponding tail-entities and relations for each extracted head-entity. Next, these two subtasks are further deconstructed into several sequence labeling problems based on our proposed span-based tagging scheme, which are conveniently solved by a hierarchical boundary tagger and a multi-span decoding algorithm. Owing to the reasonable decomposition strategy, our model can fully capture the semantic interdependency between different steps, as well as reduce noise from irrelevant entity pairs. Experimental results show that our method outperforms previous work by 5.6%, 17.2% and 3.7% (F1 score), achieving a new state-of-the-art on three public datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Extraction

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Erratum to “High-Efficiency Min-Entropy Estimation Based on Neural Network for Random Number Generators”

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security and Communication Networks 2020年第1期2020卷

作者： Na Lv Tianyu Chen Shuangyi Zhu Jing Yang Yuan Ma Jiwu Jing Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 *** Data Assurance and Communications Security Research Center Chinese Academy of Sciences Beijing 100093 *** School of Cyber Security University of Chinese Academy of Sciences Beijing 100093 *** China Information Technology Security Evaluation Center Beijing 100085 *** School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100093 ***

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Aesthetic Attributes Assessment of Images

arXiv

引用

arXiv 2019年

作者： Jin, Xin Wu, Le Zhao, Geng Li, Xiaodong Zhang, Xiaokun Ge, Shiming Zou, Dongqing Zhou, Bin Zhou, Xinghui Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing100070 China Cetc Big Data Research Institute Co. Ltd. Guiyang Guizhou550018 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China SenseTime Research Beijing100084 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Peng Cheng Laboratory Beijing100191 China

Image aesthetic quality assessment has been a relatively hot topic during the last decade. Most recently, comments type assessment (aesthetic captions) has been proposed to describe the general aesthetic impression of an image using text. In this paper, we propose Aesthetic Attributes Assessment of Images, which means the aesthetic attributes captioning. This is a newformula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute. We introduce a new dataset named DPC-Captions which contains comments of up to 5 aesthetic attributes of one image through knowledge transfer from a full-annotated small-scale dataset. Then, we propose Aesthetic Multi-Attribute Network (AMAN), which is trained on a mixture of fully-annotated small-scale PCCD dataset and weakly-annotated large-scale DPC-Captions dataset. Our AMAN makes full use of transfer learning and attention model in a single framework. The experimental results on our DPC-Captions and PCCD dataset reveal that our method can predict captions of 5 aesthetic attributes together with numerical score assessment of each attribute. We use the evaluation criteria used in image captions to prove that our specially designed AMAN model outperforms traditional CNN-LSTM model and modern SCA-CNN model of image captions. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Deep Reinforcement Learning based Smart Mitigation of DDoS Flooding in Software-Defined Networks

Deep Reinforcement Learning based Smart Mitigation of DDoS F...

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IEEE International Workshop on Computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Yandong Liu Mianxiong Dong Kaoru Ota Jianhua Li Jun Wu School of Cyber Security Shanghai Jiao Tong University Shanghai China Department of Information and Electronic Engineering Muroran Institute of Technology Muroran Japan

ISBN: (纸本)9781538661529

Distributed Denial-of-Service (DDoS) flooding attack has remained as one of the most destructive attacks for more than two decades. Although great efforts have been made to design the defense mechanism, it is still difficult to mitigate these attacks in real time smartly and effectively for the reason that attack traffic may mix with benign traffic. Software-Defined Networks (SDN) decouples control and data plane in the network. Its centralized control paradigm and global view of the network bring some new chances to enhance the defense ability against network attacks. In this paper, we propose a deep reinforcement learning based framework, which can smartly learn the optimal mitigation policies under different attack scenarios and mitigate the DDoS flooding attack in real time. This framework is an effective system to defend against a wide range of DDoS flooding attacks such as TCP SYN, UDP, and ICMP flooding. It can intelligently learn the patterns of attack traffic and throttle the attack traffic, while the traffic of benign users is forwarded normally. We compare our proposed framework with a baseline along with a popular state-of-the-art router throttling method. The experimental results show that our approach can outperform both of them in five attacking scenarios with different attack dynamics significantly.

关键词： Computer crime Servers Computational modeling Real-time systems Protocols Conferences

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Leakage-Resilient Cryptography from Puncturable Primitives and Obfuscation 24th

Leakage-Resilient Cryptography from Puncturable Primitives a...

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24th Annual International Conference on Theory and Application of Cryptology and Information security, ASIACRYPT 2018

作者： Chen, Yu Wang, Yuyu Zhou, Hong-Sheng State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China State Key Laboratory of Cryptology P.O. Box 5159 Beijing100878 China School of Cyber Security University of Chinese Academy of Sciences Beijing China Tokyo Institute of Technology Tokyo Japan IOHK Hong Kong China National Institute of Advanced Industrial Science and Technology Tokyo Japan Virginia Commonwealth University Richmond United States

ISBN: (纸本)9783030033286

In this work, we develop a framework for building leakage-resilient cryptosystems in the bounded leakage model from puncturable primitives and indistinguishability obfuscation The major insight of our work is that various types of puncturable pseudorandom functions (PRFs) can achieve leakage resilience on an obfuscated street. First, we build leakage-resilient weak PRFs from weak puncturable PRFs and which readily imply leakage-resilient secret-key encryption. Then, we build leakage-resilient publicly evaluable PRFs (PEPRFs) from puncturable PEPRFs and which readily imply leakage-resilient key encapsulation mechanism and thus public-key encryption. As a building block of independent interest, we realize puncturable PEPRFs from either newly introduced puncturable objects such as puncturable trapdoor functions and puncturable extractable hash proof systems or existing puncturable PRFs with Finally, we construct the first leakage-resilient public-coin signature from selective puncturable PRFs, leakage-resilient one-way functions and This settles the open problem posed by Boyle, Segev, and Wichs (Eurocrypt 2011). By further assuming the existence of lossy functions, all the above constructions achieve optimal leakage rate of Such a leakage rate is not known to be achievable for weak PRFs, PEPRFs and public-coin signatures before. This also resolves the open problem posed by Dachman-Soled, Gordon, Liu, O’Neill, and Zhou (PKC 2016, JOC 2018). © 2018, International Association for Cryptologic Research.

关键词： Public key cryptography

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