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arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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A game-theoretic analysis of shard-based permissionless blockchains

arXiv

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arXiv 2018年

作者： Manshaei, Mohammad Hossein Jadliwala, Murtuza Maiti, Anindya Fooladgar, Mahdi Department of Electrical and Computer Engineering Isfahan University of Technology Iran Department of Computer Science University of Texas San Antonio United States Institute for Cyber Security University of Texas San Antonio United States

Low transaction throughput and poor scalability are significant issues in public blockchain consensus protocols such as Bitcoins. Recent research efforts in this direction have proposed shard-based consensus protocols where the key idea is to split the transactions among multiple committees (or shards), which then process these shards or set of transactions in parallel. Such a parallel processing of disjoint sets of transactions or shards by multiple committees significantly improves the overall scalability and transaction throughout of the system. However, one significant research gap is a lack of understanding of the strategic behavior of rational processors within committees in such shard-based consensus protocols. Such an understanding is critical for designing appropriate incentives that will foster cooperation within committees and prevent free-riding. In this paper, we address this research gap by analyzing the behavior of processors using a game-theoretic model, where each processor aims at maximizing its reward at a minimum cost of participating in the protocol. We first analyze the Nash equilibria in an Nplayer static game model of the sharding protocol. We show that depending on the reward sharing approach employed, processors can potentially increase their payoff by unilaterally behaving in a defective fashion, thus resulting in a social dilemma. In order to overcome this social dilemma, we propose a novel incentive-compatible reward sharing mechanism to promote cooperation among processors. Our numerical results show that achieving a majority of cooperating processors (required to ensure a healthy state of the blockchain network) is easier to achieve with the proposed incentive-compatible reward sharing mechanism than with other reward sharing mechanisms. Copyright © 2018, The Authors. All rights reserved.

关键词： Blockchain

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Intelligent feature selection for detecting HTTP/2 denial of service attacks 15

Intelligent feature selection for detecting HTTP/2 denial of...

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15th Australian Information security Management Conference, AISM 2017

作者： Adi, Erwin Baig, Zubair Australian Centre for Cyber Security School of Engineering and Information Technology University of New South Wales Australia Security Research Institute School of Science Edith Cowan University PerthWA Australia

ISBN: (纸本)9780648127086

Intrusion-detection systems employ machine learning techniques to classify traffic into attack and legitimate. Network flooding attacks can leverage the new web communications protocol (HTTP/2) to bypass intrusiondetection systems. This creates an urgent demand to understand HTTP/2 characteristics and to devise customised cyber-attack detection schemes. This paper proposes Step Sister;a technique to generate an optimum network traffic feature set for network intrusion detection. The proposed technique demonstrates that a consistent set of features are selected for a given HTTP/2 dataset. This allows intrusion-detection systems to classify previously unseen network traffic samples with fewer false alarm than when techniques used in literature were employed. The results show that the proposed technique yields a set of features that, when used for network traffic classification, yields low numbers of false alarms. © 2017, All Rights Reserved, Edith Cowan University.

关键词： HTTP

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Exponentially weighted distance-based detection for radiometric identification

Exponentially weighted distance-based detection for radiomet...

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作者： Jia, Yong Qiang Gan, Lu Liao, Hong Shu Center for Cyber Security School of Electronic Engineering University of Electronic Science and Technology of China Chengdu Sichuan China Southwest Electronics and Telecommunication Technology Research Institute Chengdu Sichuan China

Radio signals show characteristics of minute differences, which result from various idiosyncratic hardware properties between different radio emitters. A robust detector based on exponentially weighted distances is proposed to detect the exact reference instants of the burst communication signals. Based on the exact detection of the reference instant, in which the radio emitter finishes the power-up ramp and enters the first symbol of its preamble, the features of the radio fingerprint can be extracted from the transient signal section and the steady-state signal section for radiometric identification. Experiments on real data sets demonstrate that the proposed method not only has a higher accuracy that outperforms correlation-based detection, but also a better robustness against noise. The comparison results of different detectors for radiometric identification indicate that the proposed detector can improve the classification accuracy of radiometric identification. Copyright © 2017 The institute of Electronics, Information and Communication Engineers.

关键词： Palmprint recognition

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Privacy-preserving mobile crowdsensing for located-based applications

Privacy-preserving mobile crowdsensing for located-based app...

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2017 IEEE International Conference on Communications, ICC 2017

作者： Ni, Jianbing Zhang, Kuan Lin, Xiaodong Xia, Qi Shen, Xuemin Sherman Department of Electrical and Computer Engineering University of Waterloo Canada Faculty of Business and Information Technology University of Ontario Institute of Technology Canada Center of Cyber Security University of Electronic Science and Technology of China China

ISBN: (纸本)9781467389990

Mobile crowdsensing is a new paradigm which explores the mobility and intelligence of mobile users to collect high-quality data from social events and phenomena for conducting complex sensing tasks. Nevertheless, privacy preservation and task allocation become main obstacles that need additional attention. To achieve accurate task allocation, it is inevitable to share some sensitive information of mobile users and customers, such as identities, location and points of interest. In this paper, we propose a privacy-preserving mobile crowdsensing framework (PPMC) for location-based applications to balance the tradeoff between privacy preservation and task allocation. In PPMC, we develop a matrix-based location matching mechanism for the service provider to achieve location-based task allocation without disclosing the location of mobile users and the sensing area of tasks. We also extend BBS+ signature and proxy reencryption to preserve identity privacy and data privacy for both customers and mobile users under the condition that they are honest to release and perform tasks, respectively. Finally, we discuss security properties and demonstrate the efficiency of PPMC in terms of computational and communication overhead. © 2017 IEEE.

关键词： Mobile communication Sensors Privacy Resource management security Mobile handsets Information systems

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ODN: Opening the Deep Network for Open-Set Action Recognition

ODN: Opening the Deep Network for Open-Set Action Recognitio...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yu Shu Yemin Shi Yaowei Wang Yixiong Zou Qingsheng Yuan Yonghong Tian Peking University Beijing Beijing CN School of Information and Electronics Beijing Institute of Technology Beijing China National Engineering Laboratory for Video Technology Peking University Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

In recent years, the performance of action recognition has been significantly improved with the help of deep neural networks. Most of the existing action recognition works hold the closed-set assumption that all action categories are known beforehand while deep networks can be well trained for these categories. However, action recognition in the real world is essentially an open-set problem, namely, it is impossible to know all action categories beforehand and consequently infeasible to prepare sufficient training samples for those emerging categories. In this case, applying closed-set recognition methods will definitely lead to unseen-category errors. To address this challenge, we propose the Open Deep Network (ODN) for the open-set action recognition task. Technologically, ODN detects new categories by applying a multiclass triplet thresholding method, and then dynamically reconstructs the classification layer and “opens” the deep network by adding predictors for new categories continually. In order to transfer the learned knowledge to the new category, two novel methods, Emphasis Initialization and Allometry Training, are adopted to initialize and incrementally train the new predictor so that only few samples are needed to fine-tune the model. Extensive experiments show that ODN can effectively detect and recognize new categories with little human intervention, thus applicable to the open-set action recognition tasks in the real world. Moreover, ODN can even achieve comparable performance to some closed-set methods.

关键词： Training Task analysis Support vector machines Labeling Neural networks Electronic mail Cats

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Blockchain-based Mutual Authentication security Protocol for Distributed RFID Systems

Blockchain-based Mutual Authentication Security Protocol for...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Siye Wang Shaoyi Zhu Yanfang Zhang Chinese Academy of Sciences School of Computer and Information Technology Institute of Information Engineering Beijing Jiaotong University Beijing China Chinese Academy of Sciences School of Cyber Security Institute of Information Engineering University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China

Since radio frequency identification (RFID) technology has been used in various scenarios such as supply chain, access control system and credit card, tremendous efforts have been made to improve the authentication between tags and readers to prevent potential attacks. Though effective in certain circumstances, these existing methods usually require a server to maintain a database of identity related information for every tag, which makes the system vulnerable to the SQL injection attack and not suitable for distributed environment. To address these problems, we now propose a novel blockchain-based mutual authentication security protocol. In this new scheme, there is no need for the trusted third parties to provide security and privacy for the system. Authentication is executed as an unmodifiable transaction based on blockchain rather than database, which applies to distributed RFID systems with high security demand and relatively low real-time requirement. Analysis shows that our protocol is logically correct and can prevent multiple attacks.

关键词： Protocols Authentication Radiofrequency identification Servers Computers

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Persistent fault analysis on block ciphers

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2018年第3期2018卷 150-172页

作者： Zhang, Fan Lou, Xiaoxuan Zhao, Xinjie Bhasin, Shivam He, Wei Ding, Ruyi Qureshi, Samiya Ren, Kui College of Information Science and Electronic Engineering Zhejiang University China Institute of Cyber Security Research Zhejiang University China College of Computer Science and Technology Zhejiang University China The Institute of North Electronic Equipment China Nanyang Technological University Singapore Shield Laboratory Huawei International Pte. Ltd Singapore Georgia Institute of Technology GA United States

Persistence is an intrinsic nature for many errors yet has not been caught enough attractions for years. In this paper, the feature of persistence is applied to fault attacks, and the persistent fault attack is proposed. Different from traditional fault attacks, adversaries can prepare the fault injection stage before the encryption stage, which relaxes the constraint of the tight-coupled time synchronization. The persistent fault analysis (PFA) is elaborated on different implementations of AES-128, specially fault hardened implementations based on Dual Modular Redundancy (DMR). Our experimental results show that PFA is quite simple and efficient in breaking these typical implementations. To show the feasibility and practicability of our attack, a case study is illustrated on the shared library Libgcrypt with rowhammer technique. Approximately 8200 ciphertexts are enough to extract the master key of AES-128 when PFA is applied to Libgcrypt1.6.3 with redundant encryption based DMR. This work puts forward a new direction of fault attacks and can be extended to attack other implementations under more interesting scenarios. © 2018, Ruhr-University of Bochum. All rights reserved.

关键词： Side channel attack

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Deep-Net: Deep neural network for cyber security use cases

arXiv

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arXiv 2018年

作者： Vinayakumar, R. Barathi Ganesh, H.B. Poornachandran, Prabaharan Anand, Kumar M. Soman, K.P. Amrita School of Engineering Coimbatore India Arnekt Solutions Pvt Ltd PuneMaharashtra India Center for Cyber Security Systems and Networks Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India Department of Information Technology National Institute of Technology Karnataka Surathkal. Mangalore India

Deep neural networks (DNNs) have witnessed as a powerful approach in this year by solving long-standing Artificial intelligence (AI) supervised and unsupervised tasks exists in natural language processing, speech processing, computer vision and others. In this paper, we attempt to apply DNNs on three different cyber security use cases: Android malware classification, incident detection and fraud detection. The data set of each use case contains real known benign and malicious activities samples. The efficient network architecture for DNN is chosen by conducting various trails of experiments for network parameters and network structures. The experiments of such chosen efficient configurations of DNNs are run up to 1000 epochs with learning rate set in the range [0.01-0.5]. Experiments of DNN performed well in comparison to the classical machine learning algorithms in all cases of experiments of cyber security use cases. This is due to the fact that DNNs implicitly extract and build better features, identifies the characteristics of the data that lead to better accuracy. The best accuracy obtained by DNN and XGBoost on Android malware classification 0.940 and 0.741, incident detection 1.00 and 0.997 fraud detection 0.972 and 0.916 *** Codes 68T50 Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Smartly deploying wechat mobile application on cloud foundry paas 3rd

Smartly deploying wechat mobile application on cloud foundry...

引用

3rd International Conference on Smart Computing and Communications, SmartCom 2018

作者： Lu, Zhihui Wan, Xiaoli Qiu, Meikang Zu, Lijun Huang, Shih-Chia Wu, Jie Liu, Meiqin School of Computer Science Fudan University Shanghai200433 China Information Center Zhejiang International Business Group Co. Ltd. Hangzhou China Department of Electrical Engineering Columbia University New YorkNY10027 United States Research Institute of Electronic Payment China UnionPay Shanghai China Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education Shanghai200433 China College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9783030057541

WeChat has become the mainstream social network mobile applications in China. Based on the WeChat API, there are many attached mobile applications. Cloud Foundry is a lightweight mainstream PaaS platform. In this paper, we studied how to develop and deploy WeChat applications on the Cloud Foundry platform to achieve lightweight development and deployment. The implementation of a product maintenance system proves the effectiveness of our deployment method with WeChat and Cloud Foundry. © 2018, Springer Nature Switzerland AG.

关键词： Foundries

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