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arXiv 2020年

作者： Qu, Xiaoye Tang, Pengwei Zou, Zhikang Cheng, Yu Dong, Jianfeng Zhou, Pan Xu, Zichuan School of Electronic Information and Communication Huazhong University of Science and Technology Huawei Tech. Hangzhou China School of Electronic Information and Communication Huazhong University of Science and Technology China Baidu Inc. China Microsoft Dynamics 365 AI Research RedmondWA United States School of Computer and Information Engineering Zhejiang Gongshang University China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Dalian University of Technology China

Temporal language localization in videos aims to ground one video segment in an untrimmed video based on a given sentence query. To tackle this task, designing an effective model to extract grounding information from both visual and textual modalities is crucial. However, most previous attempts in this field only focus on unidirectional interactions from video to query, which emphasizes which words to listen and attends to sentence information via vanilla soft attention, but clues from query-by-video interactions implying where to look are not taken into consideration. In this paper, we propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction. Specifically, in the iterative attention module, each word in the query is first enhanced by attending to each frame in the video through fine-grained attention, then video iteratively attends to the integrated query. Finally, both video and query information is utilized to provide robust cross-modal representation for further moment localization. In addition, to better predict the target segment, we propose a content-oriented localization strategy instead of applying recent anchor-based localization. We evaluate the proposed method on three challenging public benchmarks: ActivityNet Captions, TACoS, and Charades-STA. FIAN significantly outperforms the state-of-the-art approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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A Novel Congestion-Aware Interest Flooding Attacks Detection Mechanism in Named Data Networking

A Novel Congestion-Aware Interest Flooding Attacks Detection...

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International Conference on computer Communications and Networks (ICCCN)

作者： Ahmed Benmoussa Abdou el Karim Tahari Nasreddine Lagaa Abderrahmane Lakas Farhan Ahmad Rasheed Hussain Chaker Abdelaziz Kerrache Fatih Kurugollu Department of Computer Science University of Laghouat Algeria College of Information Technology United Arab Emirates University Al Ain UAE Cyber Security Research Group University of Derby Derby UK Institute of Security and Cyber-Physical Systems Innopolis University Innopolis Russia Department of Maths and Computer Science University of Ghardaia Ghardaia Algeria

Named Data Networking (NDN) is a promising candidate for future internet architecture. It is one of the implementations of the Information-Centric Networking (ICN) architectures where the focus is on the data rather than the owner of the data. While the data security is assured by definition, these networks are susceptible of various Denial of Service (DoS) attacks, mainly Interest Flooding Attacks (IFA). IFAs overwhelm an NDN router with a huge amount of interests (Data requests). Various solutions have been proposed in the literature to mitigate IFAs; however; these solutions do not make a difference between intentional and unintentional misbehavior due to the network congestion. In this paper, we propose a novel congestion-aware IFA detection and mitigation solution. We performed extensive simulations and the results clearly depict the efficiency of our proposal in detecting truly occurring IFA attacks.

关键词： Internet security computer architecture Floods IP networks Delays computer science

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STR: Secure computation on additive shares using the share-transform-reveal strategy

arXiv

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arXiv 2020年

作者： Xia, Zhihua Gu, Qi Zhou, Wenhao Xiong, Lizhi Weng, Jian Xiong, Naixue N. College of Cyber Security Jinan University Guangzhou510632 China Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing210044 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China The College of Intelligence and Computing Tianjin University Tianjin300350 China

The rapid development of cloud computing probably benefits many of us while the privacy risks brought by semi-honest cloud servers have aroused the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks to servers while ensuring the security of private data. The tasks to be outsourced are countless;however, the computations involved are similar. In this paper, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) on arbitrary n ≥ 2 servers. All protocols only require constant rounds of interactions and achieve low computation complexity. Moreover, the proposed n-party protocols ensure the security of private data even though n − 1 servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols. Copyright © 2020, The Authors. All rights reserved.

关键词： Additives

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Infrared and Visible Image Fusion via Multi-discriminators Wasserstein Generative Adversarial Network

Infrared and Visible Image Fusion via Multi-discriminators W...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jing Li Hongtao Huo Kejian Liu Chang Li Shuo Li Xin Yang School of Information Technology and Cyber Security People’s Public Security University of China BeiJing China Remote sensing center of public security People’s Public Security University of China BeiJing China Department of Biomedical Engineering Hefei University of Technology Hefei China

Generative adversarial network (GAN) has been widely applied to infrared and visible image fusion. However, the existing GAN-based image fusion methods only establish one discriminator in the network to make the fused image capture gradient information from the visible image, which may result in the loss of some infrared intensity information and texture information on the fused images. To solve this problem and improve the performance of GAN, we extend GAN to multiple discriminators and propose an end-to-end multi-discriminators Wasserstein generative adversarial network (MD-WGAN). In this framework, the fused image can preserve major infrared intensity and detail information from the first discriminator, and keep more texture information that existing in visible image from the second discriminator. We also design a texture loss function via local binary patterns to preserve more texture from visible image. The extensive qualitative and quantitative experiments show the advantages of our method compared with other state-of-the-art fusion methods.

关键词： Generators Generative adversarial networks Image fusion Gallium nitride Machine learning security Information technology

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Correction to: A biometric cryptosystem scheme based on random projection and neural network

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Soft Computing 2021年第11期25卷 7671-7671页

作者： Peng, Jialiang Yang, Bian Gupta, B. B. El-Latif, Ahmed A. Abd School of Data Science and Technology Heilongjiang University Harbin China Guangxi Key Laboratory of Cryptography and Information Security Guilin China Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik Norway Computer Engineering Department NIT Kurukshetra Kurukshetra India Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

A correction to this paper has been published: https://***/10.1007/s00500-021-05804-3

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Task-feature collaborative learning with application to personalized attribute prediction

arXiv

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arXiv 2020年

作者： Yang, Zhiyong Xu, Qianqian Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 Peng Cheng Laboratory Shenzhen518055 China

As an effective learning paradigm against insufficient training samples, Multi-Task Learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the overall performance. In MTL, a major challenge springs from the phenomenon that sharing the knowledge with dissimilar and hard tasks, known as negative transfer, often results in a worsened performance. Though a substantial amount of studies have been carried out against the negative transfer, most of the existing methods only model the transfer relationship as task correlations, with the transfer across features and tasks left unconsidered. Different from the existing methods, our goal is to alleviate negative transfer collaboratively across features and tasks. To this end, we propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL). Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks and suppressing inter-group knowledge sharing. We then propose an optimization method for the model. Extensive theoretical analysis shows that our proposed method has the following benefits: (a) it enjoys the global convergence property and (b) it provides a block-diagonal structure recovery guarantee. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks. We further apply it to the personalized attribute prediction problem with fine-grained modeling of user behaviors. Finally, experimental results on both simulated dataset and real-world datasets demonstrate the effectiveness of our proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge management

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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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Adaptive feedforward neural network control with an optimized hidden node distribution

arXiv

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arXiv 2020年

作者： Liu, Qiong Li, Dongyu Ge, Shuzhi Sam Ouyang, Zhong Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen Guangdong518055 China The School of Cyber Science and Technology Beihang University Beijing100191 China Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Department of Mechanical and Aerospace Engineering The Ohio State University ColumbusOH43202 United States

Composite adaptive radial basis function neural network (RBFNN) control with a lattice distribution of hidden nodes has three inherent demerits: 1) the approximation domain of adaptive RBFNNs is difficult to be determined a priori;2) only a partial persistence of excitation (PE) condition can be guaranteed;and 3) in general, the required number of hidden nodes of RBFNNs is enormous. This paper proposes an adaptive feedforward RBFNN controller with an optimized distribution of hidden nodes to suitably address the above demerits. The distribution of the hidden nodes calculated by a K-means algorithm is optimally distributed along the desired state trajectory. The adaptive RBFNN satisfies the PE condition for the periodic reference trajectory. The weights of all hidden nodes will converge to the optimal values. This proposed method considerably reduces the number of hidden nodes, while achieving a better approximation ability. The proposed control scheme shares a similar rationality to that of the classical PID control in two special cases, which can thus be seen as an enhanced PID scheme with a better approximation ability. For the controller implemented by digital devices, the proposed method, for a manipulator with unknown dynamics, potentially achieves better control performance than model-based schemes with accurate dynamics. Simulation results demonstrate the effectiveness of the proposed scheme. This result provides a deeper insight into the coordination of the adaptive neural network control and the deterministic learning theory. Impact Statement-Adaptive RBFNN control learns to control a robot manipulator when both the structures and parameters of the target robot are unknown in advance. Unfortunately, current adaptive RBFNN controllers need a large-scale neural network to approximate the dynamics of the robot manipulator, and the learning performance cannot be guaranteed to converge. The proposed method in this paper not only reduces the scale of neural netwo

关键词： Radial basis function networks

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An Enhanced Feature Selection Approach based on Mutual Information for Breast Cancer Diagnosis 6

An Enhanced Feature Selection Approach based on Mutual Infor...

引用

6th International Conference on Image and Signal Processing and their Applications, ISPA 2019

作者： Zemmal, Nawel Azizi, Nabiha Ziani, Amel Benzebouchi, Nacer Eddine Aldwairi, Monther Badji Mokhtar University Labged Laboratory Annaba23000 Algeria Department of Mathematics and Computer Science Mohamed Cherif Messaadia University Souk-Ahras41000 Algeria Lri Laboratory: Information Research Laboratory Computer Science Department Annaba23000 Algeria Jordan University of Science and Technology Department of Network Engineering and Security Irbid22110 Jordan

ISBN: (纸本)9781728131566

Breast cancer is the most feared disease in the female population. Early detection plays an important role to improve prognosis. Mammography is the best examination for the detection of breast cancer. However, in some cases, reading mammograms is difficult for radiologists. For this reason, several researches have been conducted to develop computer Aided Diagnosis tools (CAD) for this disease which aims to interpret mammography images. This paper investigates a new CAD system based on Transductive scheme and Mutual Information for breast abnormalities diagnosis. In the proposed method, a feature vector contains a combination of two features extraction method: Grey Level Co-occurrence Matrix and local Binary Pattern. In the next step, a novel scheme combining Mutual Information and Correlation-based feature selection was applied for selecting the most relevant features. Finally, the classification was achieved using a Transductive Support Vector Machine classifier. The effectiveness of the proposed CAD is examined on the DDSM dataset using classification accuracy, recall and precision. Experimental results demonstrate that the proposed CAD system is clinically significant and can be used to classify the abnormalities of the breast. © 2019 IEEE.

关键词： computer aided diagnosis

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COVID-19: The information warfare paradigm shift

arXiv

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arXiv 2020年

作者： Kallberg, Jan Burk, Rosemary A. Thuraisingham, Bhavani Army Cyber Institute at West Point U.S. Military Academy West PointNY United States Department of Social Sciences U.S. Military Academy West PointNY United States U.S. Fish and Wildlife Service Department of the Interior Head Quarters Falls ChurchVA United States Cyber Security Institute Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas RichardsonTX United States

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