2019 European Intelligence and Security Informatics Conference (EISIC)最新文献_第2页

Program Committee 项目委员会

2019 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2019-11-01 DOI: 10.1109/eisic49498.2019.9108778

Pengcheng Zhang, Junhua Ding, Xiaobing Sun, Mingyue Jiang

{"title":"Program Committee","authors":"Pengcheng Zhang, Junhua Ding, Xiaobing Sun, Mingyue Jiang","doi":"10.1109/eisic49498.2019.9108778","DOIUrl":"https://doi.org/10.1109/eisic49498.2019.9108778","url":null,"abstract":"Sanghyun Ahn, University of Seoul Amir H. Alavi, University of Missouri Ladjel Bellatreche, LIAS/ENSMA Athman Bouguettaya, The University of Sydney Stephane Bressan, National University of Singapore K. Selcuk Candan, Arizona State University Tru Cao, Ho Chi Minh City University of Technology Songcan Chen, Nanjing University of Aeronautics & Astronautics Hong Chen, Renmin University of China Heeryon Cho, Kookmin University Soo-Mi Choi, Sejong University Mi-Jung Choi, Kangwon National University Hoon Choi, Chungnam National University Jaegul Choo, Korea University Soon Ae Chun, City University of New York Shifei Ding, China University of Mining and Technology Gill Dobbie, The University of Auckland Koji Eguchi, Hiroshima University Sameh Elnikety, Microsoft Young Ik Eom, Sungkyunkwan University Sergio Flesca, University of Calabria Zhipeng Gao, Beijing University of Posts and Telecommunications Wei Gao, Nanjing University Hong Gao, Harbin Institute of Technology Xin Geng, Southeast University Chen Gong, Shanghai Jiao Tong University Hyoil Han, Illinois State University Kenji Hatano, Doshisha University Kazumasa Horie, University of Tsukuba Wen Hua, The University of Queensland Seung-Won Hwang, Yonsei University Eenjun Hwang, Korea University Hyeonseung Im, Kangwon National University Md. Saiful Islam, Griffith University Young-Seob Jeong, SoonChunHyang University Seong-Ho Jeong, Hankuk University of Foreign Studies Xiaolong Jin, Chinese Academy of Sciences","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126176202","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: 0

Continuous Authentication of Smartphone Users via Swipes and Taps Analysis 智能手机用户通过滑动和点击分析的持续身份验证

2019 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2019-11-01 DOI: 10.1109/EISIC49498.2019.9108780

A. Garbuz, A. Epishkina, K. Kogos

{"title":"Continuous Authentication of Smartphone Users via Swipes and Taps Analysis","authors":"A. Garbuz, A. Epishkina, K. Kogos","doi":"10.1109/EISIC49498.2019.9108780","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108780","url":null,"abstract":"Nowadays, smartphones are used for getting access to sensitive and private data. As a result, we need an authentication system that will provide smartphones with additional security and at the same time will not cause annoyance to users. Existing authentication mechanisms provide just a one-time user verification and do not perform re-authentication in the process of further interaction. In this paper, we present a continuous user authentication system based on user's interaction with the touchscreen in conjunction with micromovements, performed by smartphones at the same time. We consider two of the most common types of gestures performed by users (vertical swipes up and down, and taps). The novelty of our approach is that swipes and taps are both analyzed to provide continuous authentication. Swipes are informative gestures, while taps are the most common gestures. This way, we aim to reduce the time of impostors' detection. The proposed scheme collects data from the touchscreen and multiple 3-dimensional sensors integrated in all modern smartphones. We use One-Class Support Vector Machine (OSVM) algorithm to get a model of a legitimate user. The obtained results show that the proposed scheme of continuous authentication can improve smartphone security.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121224815","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: 3

Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks 基于语义神经网络集成的枪械检测与分割

2019 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2019-11-01 DOI: 10.1109/EISIC49498.2019.9108871

Alexander Egiazarov, Vasileios Mavroeidis, Fabio Massimo Zennaro, Kamer Vishi

{"title":"Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks","authors":"Alexander Egiazarov, Vasileios Mavroeidis, Fabio Massimo Zennaro, Kamer Vishi","doi":"10.1109/EISIC49498.2019.9108871","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108871","url":null,"abstract":"In recent years we have seen an upsurge in terror attacks around the world. Such attacks usually happen in public places with large crowds to cause the most damage possible and get the most attention. Even though surveillance cameras are assumed to be a powerful tool, their effect in preventing crime is far from clear due to either limitation in the ability of humans to vigilantly monitor video surveillance or for the simple reason that they are operating passively. In this paper, we present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks that decomposes the problem of detecting and locating a weapon into a set of smaller problems concerned with the individual component parts of a weapon. This approach has computational and practical advantages: a set of simpler neural networks dedicated to specific tasks requires less computational resources and can be trained in parallel; the overall output of the system given by the aggregation of the outputs of individual networks can be tuned by a user to trade-off false positives and false negatives; finally, according to ensemble theory, the output of the overall system will be robust and reliable even in the presence of weak individual models. We evaluated our system running simulations aimed at assessing the accuracy of individual networks and the whole system. The results on synthetic data and real-world data are promising, and they suggest that our approach may have advantages compared to the monolithic approach based on a single deep convolutional neural network.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129205195","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: 11

The Directive 2014/41/UE - The European Investigation Order 指令2014/41/UE -欧洲调查令

2019 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2019-11-01 DOI: 10.1109/eisic49498.2019.9108881

Fabrizia Bemer

引用次数: 0

Timing Covert Channels Detection Cases via Machine Learning 基于机器学习的定时隐蔽通道检测案例

2019 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2019-11-01 DOI: 10.1109/EISIC49498.2019.9108873

A. Epishkina, Mikhail Finoshin, K. Kogos, Aleksandra Yazykova

引用次数: 2

Identifying Deceptive Reviews: Feature Exploration, Model Transferability and Classification Attack 鉴别欺骗性评论:特征探索、模型可转移性和分类攻击

2019 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2019-11-01 DOI: 10.1109/EISIC49498.2019.9108852

Marianela García Lozano, Johan Fernquist

{"title":"Identifying Deceptive Reviews: Feature Exploration, Model Transferability and Classification Attack","authors":"Marianela García Lozano, Johan Fernquist","doi":"10.1109/EISIC49498.2019.9108852","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108852","url":null,"abstract":"The temptation to influence and sway public opinion most certainly increases with the growth of open online forums where anyone anonymously can express their views and opinions. Since online review sites are a popular venue for opinion influencing attacks, there is a need to automatically identify deceptive posts. The main focus of this work is on automatic identification of deceptive reviews, both positive and negative biased. With this objective, we build a deceptive review SVM based classification model and explore the performance impact of using different feature types (TF-IDF, word2vec, PCFG). Moreover, we study the transferability of trained classification models applied to review data sets of other types of products, and, the classifier robustness, i.e., the accuracy impact, against attacks by stylometry obfuscation trough machine translation. Our findings show that i) we achieve an accuracy of over 90% using different feature types, ii) the trained classification models do not perform well when applied on other data sets containing reviews of different products, and iii) machine translation only slightly impacts the results and can not be used as a viable attack method.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117029388","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: 0

Extracting Account Attributes for Analyzing Influence on Twitter 提取账户属性用于分析Twitter上的影响力

2019 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2019-11-01 DOI: 10.1109/EISIC49498.2019.9108896

Johan Fernquist, Ola Svenonius, Lisa Kaati, F. Johansson

{"title":"Extracting Account Attributes for Analyzing Influence on Twitter","authors":"Johan Fernquist, Ola Svenonius, Lisa Kaati, F. Johansson","doi":"10.1109/EISIC49498.2019.9108896","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108896","url":null,"abstract":"The last years has witnessed a surge of auto-generated content on social media. While many uses are legitimate, bots have also been deployed in influence operations to manipulate election results, affect public opinion in a desired direction, or to divert attention from a specific event or phenomenon. Today, many approaches exist to automatically identify bot-like behaviour in order to curb illegitimate influence operations. While progress has been made, existing models are exceedingly complex and nontransparent, rendering validation and model testing difficult. We present a transparent and parsimonious method to study influence operations on Twitter. We define nine different attributes that can be used to describe and reason about different characteristics of a Twitter account. The attributes can be used to group accounts that have similar characteristics and the result can be used to identify accounts that are likely to be used to influence public opinion. The method has been tested on a Twitter data set consisting of 66,000 accounts. Clustering the accounts based on the proposed features show promising results for separating between different groups of reference accounts.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114240993","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

Remote KYC: Attacks and Counter-Measures 远程KYC:攻击与对策

2019 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2019-11-01 DOI: 10.1109/EISIC49498.2019.9108787

M. Pic, Gaël Mahfoudi, Anis Trabelsi

引用次数: 4

Crime Prediction Using Hotel Reviews? 利用酒店评论预测犯罪?

2019 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2019-11-01 DOI: 10.1109/EISIC49498.2019.9108861

Panos Kostakos, Somkiadcharoen Robroo, Bofan Lin, M. Oussalah

引用次数: 1

Evaluation of Deep Learning Models for Ear Recognition Against Image Distortions 针对图像失真的深度学习耳识别模型评价

2019 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2019-11-01 DOI: 10.1109/EISIC49498.2019.9108870

S. El-Naggar, T. Bourlai

{"title":"Evaluation of Deep Learning Models for Ear Recognition Against Image Distortions","authors":"S. El-Naggar, T. Bourlai","doi":"10.1109/EISIC49498.2019.9108870","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108870","url":null,"abstract":"Automated human authentication is becoming increasingly popular on a variety of daily activities, ranging from surveillance to commercial related applications. While there are many biometric modalities that can be used, ear recognition has earned its value if and when available to be captured. Ears demonstrate specific advantages over other competitors in an effort to identify cooperative and non-cooperative individuals in either controlled or challenging environments. The performance of ear recognition systems can be impacted by several factors, including standoff distance, ear pose angle, and ear image quality. While all three factors can degrade ear recognition performance, here we focus on the latter two using real data, and assess the standoff distance factor by synthetically generating blurry and noisy images to simulate longer distance ear images. Thus, in this work we are inspired by various studies in the literature that discuss the how and why challenging biometric images of different modalities impact the associated biometric system recognition. Specifically, we focus on how different ear image distortions and yaw pose angles affect the performance of various deep learning based ear recognition models. Our contributions are threefold. Firstly, we are using challenging ear dataset, with a wide range of yaw pose angles, to evaluate the ear recognition performance of various original ear matching approaches. Secondly, by examining multiple convolutional neural network (CNN) architectures and employing multiple techniques for the learning process, we determine the most efficient CNN - based ear recognition approach. Thirdly, we investigated the impact on performance of a set of ear recognition CNN models in the presence of multiple image degradation factors, including variations of blurriness, additive noise, brightness and contrast.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114806693","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