检索结果-内蒙古大学图书馆

Ring compute-and-forward over block-fading channels

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Lyu, Shanxiang Campello, Antonio Ling, Cong College of Information Science and Technology College of Cyber Security Jinan University Guangzhou510632 China Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

The Compute-and-Forward protocol in quasi-static channels normally employs lattice codes based on the rational integers Z, Gaussian integers Z [i] or Eisenstein integers Z [ω], while its extension to more general channels often assumes channel state information at transmitters (CSIT). In this paper, we propose a novel scheme for Compute-and-Forward in block-fading channels without CSIT, which is referred to as Ring Compute-and-Forward because the fading coefficients are quantized to the canonical embedding of a ring of algebraic integers. Thanks to the multiplicative closure of the algebraic lattices employed, a relay is able to decode an algebraic-integer linear combination of lattice codewords. We analyze its achievable computation rates and show it outperforms conventional Compute-and-Forward based on Z-lattices. By investigating the effect of Diophantine approximation by algebraic conjugates, we prove that the degrees-of-freedom (DoF) of the optimized computation rate is n/L, where n is the number of blocks and L is the number of users. Copyright © 2018, The Authors. All rights reserved.

关键词： Channel state information

The continued risks of unsecured public Wi-Fi and why users keep using it: Evidence from Japan

学校读者我要写书评

暂无评论

The continued risks of unsecured public Wi-Fi and why users ...

Annual Conference on Privacy, security and Trust, PST

作者： Nissy Sombatruang Youki Kadobayashi M. Angela Sasse Michelle Baddeley Daisuke Miyamoto Department of Security and Crime Science University College London London UK Laboratory for Cyber Resilience Nara Institute of Science and Technology Nara Japan Department of Computer Science University College London London UK Institute for Choice University of South Australia Adelaide Australia

ISBN: (纸本)9781538674949

Many people today use public Wi-Fi networks but they harbor security and privacy risks. We investigated the extent of these risk today, and what factors influenced users to use the networks, adapting the design of a previous UK study, this time in Japan. We first set up an experimental open public Wi-Fi network at ll locations in downtown Nara and captured Internet traffic. From approximately 7.7 million packets captured from 196 unique mobile devices during a 150-hour experiment, we found private photos, emails, documents, and login credentials being transmitted without encryption - showing that many people use unsecured public Wi-Fi networks, and many applications do not encrypt data they send. We then examined why people use public Wi-Fi in a range of scenarios through a survey with 103 participants. We found that the desire to conserve mobile data allowance was linked to a risk-taking attitude, and use of unsecured public Wi-Fi, especially among participants with a low monthly data allowance. Gender and education also played a role; female participants and those with high school education were more likely to use public Wi-Fi.

关键词： Wireless fidelity Decision making Electronic mail Encryption Internet Education

CloudMe forensics: A case of big-data investigation

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Teing, Yee-Yang Dehghantanha, Ali Choo, Kim-Kwang Raymond Department of Computer Science Faculty of Computer Science and Technology Universiti Putra Malaysia Serdang Selangor43400 Malaysia School of Computing Science and Engineering University of Salford Salford Greater ManchesterM5 4WT United Kingdom Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249-0631 United States

—The issue of increasing volume, variety and velocity of has been an area of concern in cloud forensics. The high volume of data will, at some point, become computationally exhaustive to be fully extracted and analysed in a timely manner. To cut down the size of investigation, it is important for a digital forensic practitioner to possess a well-rounded knowledge about the most relevant data artefacts from the cloud product investigating. In this paper, we seek to tackle on the residual artefacts from the use of CloudMe cloud storage service. We demonstrate the types and locations of the artefacts relating to the installation, uninstallation, log-in, log-off, and file synchronisation activities from the computer desktop and mobile clients. Findings from this research will pave the way towards the development of data mining methods for cloud-enabled big data endpoint forensics investigation. Copyright © 2018, The Authors. All rights reserved.

关键词： Big data

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

学校读者我要写书评

暂无评论

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

Improved phrase search construction over encrypted data in cloud storage

学校读者我要写书评

暂无评论

International Journal of Innovative Computing and Applications 2017年第1期8卷 3-11页

作者： Kong, Fanyu Wang, Jie Yu, Jia Wang, Xu An Institute of Network Security Shandong University Jinan250100 China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Jinan250100 China Department of Computer Science University of Massachusetts Lowell LowellMA01854 United States College of Information Engineering Qingdao University Qingdao266071 China Department of Electronics Technology Engineering University of Armed Police Force Xi'an China

Searchable encryption schemes are important security techniques for guaranteeing cloud customers' data privacy in cloud storage outsourcing service. Kissel proposed a verifiable single-phase phrase search scheme using symmetric encryption algorithms, which was proved non-adaptively secure in the semi-honest-but-curious (SHBC) model. We present a security analysis of Kissel's scheme and show that the cloud service provider can obtain some new phrases' trapdoors and search outcomes based on the client's a phrase query. We propose an improvement to simplify Kissel's scheme and enhance its security. Finally, we give a security analysis and efficiency analysis of the improved phrase search scheme on encrypted data. The analysis shows that our improved scheme is proved non-adaptively secure and has better efficiency. Copyright © 2017 Inderscience Enterprises Ltd.

关键词： Big data

Inter- and Intra-Patient ECG Heartbeat Classification For Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Mousavi, Sajad Afghah, Fatemeh Acharya, U. Rajendra School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff United States Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Biomedical Engineering Faculty of Engineering University of Malaya

Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. In this paper, we propose a solution to address this limitation of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models. We evaluated the proposed method on the MIT-BIH arrhythmia database, considering the intra-patient and inter-patient paradigms, and the AAMI EC57 standard. The evaluation results for both paradigms show that our method achieves the best performance in the literature (a positive predictive value of 96.46% and sensitivity of 100% for the category S, and a positive predictive value of 98.68% and sensitivity of 97.40% for the category F for the intra-patient scheme;a positive predictive value of 92.57% and sensitivity of 88.94% for the category S, and a positive predictive value of 99.50% and sensitivity of 99.94% for the category V for the inter-patient scheme.). The source code is available at https://***/SajadMo/ECG-Heartbeat-Classification-seq2seq-model 1. Copyright © 2018, The Authors. All rights reserved.

关键词： Diseases

Power Attack and Protected Implementation on Lightweight Block Cipher SKINNY

学校读者我要写书评

暂无评论

Power Attack and Protected Implementation on Lightweight Blo...

Asia Joint Conference on Information security (Asia JCIS)

作者： Jing Ge Yifan Xu Ruiqian Liu Enze Si Ning Shang An Wang School of Computer Science State Key Laboratory of Cryptology Beijing China School of Computer Science Beijing Institute of Technology Beijing China Network Security Squadron of Public Security Department of Henan Province Zhengzhou China Chinese Academy of Sciences China Key laboratory of network assessment technology & Institute of Information Engineering Beijing China

SKINNY is a new lightweight tweakable block cipher family, which can compete to other lightweight cipher in terms of hardware or software implementations. While its theoretical security has been widely studied, little effort has been made to analyze its implementation security such as side-channel attacks protection. In this paper, we first give correlation power attack and its experiment on SKINNY. Then, a masking scheme for software SKINNY is proposed against side-channel attacks. Its implementation shows that the protected SKINNY only costs 8.42%, 183.27%, and 90.85% of extra code, time, and RAM, respectively.

关键词： Encryption Correlation Power demand Ciphers Hamming weight

Stop annoying me! an empirical investigation of the usability of app privacy notifications 17

学校读者我要写书评

暂无评论

Stop annoying me! an empirical investigation of the usabilit...

29th Australian computer-Human Interaction Conference, OzCHI 2017

作者： Micallef, Nicholas Just, Mike Baillie, Lynne Alharby, Maher Australian Centre for Cyber Security School of Engineering and Information Technology University of New South Wales Canberra Australia School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh United Kingdom College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (纸本)9781450353793

Privacy nudges are a "soft-paternalistic" method to nudge (instead of force) users to make more informed privacy decisions. While previous work has shown that privacy nudges are effective in encouraging users to adjust their privacy settings, current privacy nudges are considered to be annoying. Previous research found that modalities influence the effectiveness of responses to system messages. Hence, with the aim of improving the usability of privacy nudges, we conducted both a lab and a 3-day field study to empirically investigate how users perceive receiving privacy nudges using different modalities (combinations of visual, vibration, audio and speech). Our results suggest that app designers should implement privacy nudges which cede the decision of their delivery time to the users themselves. Most importantly, our findings reveal that to minimize annoyance, intrusiveness and interruption, while still being read, low priority notifications should not be delivered using salient modalities (i.e., audio or speech).1. © 2017 Association for Computing Machinery. All rights reserved.

关键词： Interactive computer systems

Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance

学校读者我要写书评

暂无评论

Nature Water

Nature Water 2025年 1-11页

作者： Eva Aßmann Christopher Irrgang Hugues Richard Martin Hölzer Timo Greiner Alexander Ullrich Matthew Wade Shelesh Agrawal Susanne Lackner Fabian Amman Sindy Böttcher Markus Landthaler Emanuel Wyler Serghei Mangul Viorel Munteanu Fotis Psomopoulos Maureen Smith Maria Trofimova Max von Kleist Centre for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Genome Competence Center (MF1) Robert Koch Institute Berlin Germany Department of Infectious Disease Epidemiology Robert Koch Institute Berlin Germany Chief Data Officer Group UK Health Security Agency London UK Chair of Water and Environmental Biotechnology Institute IWAR Department of Civil and Environmental Engineering Sciences Technical University of Darmstadt Darmstadt Germany CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Institute for Hygiene and Applied Immunology Medical University of Vienna Vienna Austria Gastroenteritis and Hepatitis Pathogens and Enteroviruses (FG15) Robert Koch Institute Berlin Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Institute for Biology Humboldt-Universität zu Berlin Berlin Germany Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles CA USA Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava Romania Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau Moldova Institute of Applied Biosciences Center for Research and Technology Hellas Thessaloniki Greece Systems Medicine of Infectious Disease (P5) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

Wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the evolution and spread of global health threats, from pathogens to antimicrobial resistances. Throughout the COVID-19 pandemic, multiple wastewater surveillance programmes have advanced statistical and machine learning methods for detecting pathogens from wastewater sequencing data and correlating measured targets with the represented population to infer meaningful conclusions for public health. Integrating contextual data can account for measurement uncertainties across the WBE workflow that affect the reliability of analyses. However, the broader availability and harmonization of data are major obstacles to method development. Here we review the benefits and limitations of wastewater-related data streams, highlighting the potential of machine learning to leverage these streams for normalization and other WBE applications. We emphasize the relevance of developing global frameworks for integrating WBE with other health surveillance systems and discuss next steps to address current and foreseeable challenges for robust and interpretable machine learning-enhanced WBE.

关键词：