EAI Endorsed Trans. Security Safety最新文献_第9页

Analysis of Targeted Mouse Movements for Gender Classification 目标鼠标运动的性别分类分析

EAI Endorsed Trans. Security Safety Pub Date : 2017-12-07 DOI: 10.4108/eai.7-12-2017.153395

Nicolas Van Balen, C. Ball, Haining Wang

{"title":"Analysis of Targeted Mouse Movements for Gender Classification","authors":"Nicolas Van Balen, C. Ball, Haining Wang","doi":"10.4108/eai.7-12-2017.153395","DOIUrl":"https://doi.org/10.4108/eai.7-12-2017.153395","url":null,"abstract":"Gender is one of the essential characteristics of personal identity that is often misused by online impostors for malicious purposes. This paper proposes a naturalistic approach for identity protection with a specific focus on using mouse biometrics to ensure accurate gender identification. Our underpinning rationale lies in the fact that men and women differ in their natural aiming movements of a hand held object in twodimensional space due to anthropometric, biomechanical, and perceptual-motor control differences between the genders. Although some research has been done on classifying user by gender using biometrics, to the best of our knowledge, no research has provided a comprehensive list of which metrics (features) of movements are actually relevant to gender classification, or method by which these metrics may be chosen. This can lead to researchers making unguided decisions on which metrics to extract from the data, doing so for convenience or personal preference. Making choices this way can lead to negatively affecting the accuracy of the model by the inclusion of metrics with little relevance to the problem, and excluding metrics of high relevance. In this paper, we outline a method for choosing metrics based on empirical evidence of natural differences in the genders, and make recommendations on the choice of metrics. The efficacy of our method is then tested through the use of a logistic regression model. Received on 29 November 2017; accepted on 02 December 2017; published on 07 December 2017","PeriodicalId":335727,"journal":{"name":"EAI Endorsed Trans. Security Safety","volume":"333 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124697415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Re-identification of Vehicular Location-Based Metadata 基于车辆位置的元数据重新识别

EAI Endorsed Trans. Security Safety Pub Date : 2017-12-07 DOI: 10.4108/EAI.7-12-2017.153393

Zheng Tan, Cheng Wang, Xiaoling Fu, Jipeng Cui, Changjun Jiang, Weili Han

引用次数: 2

Bootstrapping trust in software defined networks 在软件定义网络中引导信任

EAI Endorsed Trans. Security Safety Pub Date : 2017-12-07 DOI: 10.4108/EAI.7-12-2017.153397

Nicolae Paladi, C. Gehrmann

引用次数: 6

An On-Demand Defense Scheme Against DNS Cache Poisoning Attacks DNS缓存投毒攻击的按需防御方案

EAI Endorsed Trans. Security Safety Pub Date : 2017-10-22 DOI: 10.1007/978-3-319-78813-5_43

Zheng Wang, Shui Yu, S. Rose

引用次数: 4

Identifying forensically uninteresting files in a large corpus 在大型语料库中识别法医上不感兴趣的文件

EAI Endorsed Trans. Security Safety Pub Date : 2016-12-08 DOI: 10.4108/eai.8-12-2016.151725

N. Rowe

{"title":"Identifying forensically uninteresting files in a large corpus","authors":"N. Rowe","doi":"10.4108/eai.8-12-2016.151725","DOIUrl":"https://doi.org/10.4108/eai.8-12-2016.151725","url":null,"abstract":"For digital forensics, eliminating the uninteresting is often more critical than finding the interesting since there is so much more of it. Published software-file hash values like those of the National Software Reference Library (NSRL) have limited scope. We discuss methods based on analysis of file context using the metadata of a large corpus. Tests were done with an international corpus of 262.7 million files obtained from 4018 drives. For malware investigations, we identify clues to malware in context, and show that using a Bayesian ranking formula on metadata can increase recall by 5.1 while increasing precision by 1.7 times over inspecting executables alone. For more general investigations, we show that using together two of nine criteria for uninteresting files, with exceptions for some special interesting files, can exclude 77.4% of our corpus instead of the 23.8% that were excluded by NSRL. For a test set of 19,784 randomly selected files from our corpus that were manually inspected, false positives after file exclusion (interesting files identified as uninteresting) were 0.18% and false negatives (uninteresting files identified as interesting) were 29.31% using our methods. The generality of the methods was confirmed by separately testing two halves of our corpus. Few of our excluded files were matched in two commercial hash sets. This work provides both new uninteresting hash values and programs for finding more.","PeriodicalId":335727,"journal":{"name":"EAI Endorsed Trans. Security Safety","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130305831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Prevention of crime in B2C E-Commerce: How E-Retailers/Banks protect themselves from Criminal Sctivities B2C电子商务中的犯罪预防:电子零售商/银行如何防范犯罪活动

EAI Endorsed Trans. Security Safety Pub Date : 2016-12-08 DOI: 10.4108/eai.8-12-2016.151727

Najlaa Almajed, L. Maglaras, F. Siewe, H. Janicke, P. B. Zadeh

引用次数: 2

Security, Privacy and Trust in Cyber Physical Systems 网络物理系统中的安全、隐私和信任

EAI Endorsed Trans. Security Safety Pub Date : 2016-12-08 DOI: 10.4108/eai.8-12-2016.151724

H. Janicke, Kevin I. Jones, L. Maglaras

引用次数: 0

Filtering Nonlinear Feedback Shift Registers using Welch-Gong Transformations for Securing RFID Applications 使用Welch-Gong变换滤波非线性反馈移位寄存器以保护RFID应用

EAI Endorsed Trans. Security Safety Pub Date : 2016-12-08 DOI: 10.4108/eai.8-12-2016.151726

K. Mandal, G. Gong

{"title":"Filtering Nonlinear Feedback Shift Registers using Welch-Gong Transformations for Securing RFID Applications","authors":"K. Mandal, G. Gong","doi":"10.4108/eai.8-12-2016.151726","DOIUrl":"https://doi.org/10.4108/eai.8-12-2016.151726","url":null,"abstract":"Pseudorandom number generators play an important role to provide security and privacy on radio frequency identification (RFID) tags. In particular, the EPC Class 1 Generation 2 (EPC C1 Gen2) standard uses a pseudorandom number generator in the tag identification protocol. In this paper, we first present a pseudorandom number generator, named the filtering nonlinear feedback shift register using Welch-Gong (WG) transformations (filtering WG-NLFSR) and the filtering WG7-NLFSR for EPC C1 Gen2 RFID tags. We then investigate the periodicity of a sequence generated by the filtering WG-NLFSR by considering the model, named nonlinear feedback shift registers using Welch-Gong (WG) transformations (WG-NLFSR). The periodicity of WG-NLFSR sequences is investigated in two ways. Firstly, we perform the cycle decomposition of WG-NLFSR recurrence relations over different finite fields by computer simulations where the nonlinear recurrence relation is composed of a characteristic polynomial and a WG transformation module. Secondly, we conduct an empirical study on the period distribution of the sequences generated by the WG-NLFSR. The empirical study states that a sequence with period bounded below by the square root of the maximum period can be generated by the WG-NLFSR with high probability for any initial state.","PeriodicalId":335727,"journal":{"name":"EAI Endorsed Trans. Security Safety","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128056788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Assessing Security, Capacity and Reachability of a Heterogeneous Industrial Network during Planning Phase 规划阶段异构工业网络的安全性、容量和可达性评估

EAI Endorsed Trans. Security Safety Pub Date : 2016-12-08 DOI: 10.4108/eai.8-12-2016.151728

Apala Ray, J. Åkerberg, M. Björkman, M. Gidlund

引用次数: 1

A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN) 基于深度学习的软件定义网络(SDN) DDoS检测系统

EAI Endorsed Trans. Security Safety Pub Date : 2016-11-22 DOI: 10.4108/eai.28-12-2017.153515

Quamar Niyaz, Weiqing Sun, A. Javaid

引用次数: 241