2017 European Intelligence and Security Informatics Conference (EISIC)最新文献_第3页

Behavioural & Tempo-Spatial Knowledge Graph for Crime Matching through Graph Theory 基于图论的犯罪匹配行为与时空知识图

2017 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2017-09-01 DOI: 10.1109/EISIC.2017.29

Nadeem Qazi, W. Wong

引用次数: 9

Text Mining in Unclean, Noisy or Scrambled Datasets for Digital Forensics Analytics 用于数字取证分析的不干净、嘈杂或混乱数据集的文本挖掘

2017 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2017-09-01 DOI: 10.1109/EISIC.2017.19

Konstantinos F. Xylogiannopoulos, P. Karampelas, R. Alhajj

引用次数: 4

Cyberbullying System Detection and Analysis 网络欺凌系统检测与分析

2017 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2017-09-01 DOI: 10.1109/EISIC.2017.43

Yee Jang Foong, M. Oussalah

引用次数: 22

Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense 恶意软件检测中的对抗性机器学习:逃避攻击和防御之间的军备竞赛

2017 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2017-09-01 DOI: 10.1109/EISIC.2017.21

Lingwei Chen, Yanfang Ye, T. Bourlai

{"title":"Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense","authors":"Lingwei Chen, Yanfang Ye, T. Bourlai","doi":"10.1109/EISIC.2017.21","DOIUrl":"https://doi.org/10.1109/EISIC.2017.21","url":null,"abstract":"Since malware has caused serious damages and evolving threats to computer and Internet users, its detection is of great interest to both anti-malware industry and researchers. In recent years, machine learning-based systems have been successfully deployed in malware detection, in which different kinds of classifiers are built based on the training samples using different feature representations. Unfortunately, as classifiers become more widely deployed, the incentive for defeating them increases. In this paper, we explore the adversarial machine learning in malware detection. In particular, on the basis of a learning-based classifier with the input of Windows Application Programming Interface (API) calls extracted from the Portable Executable (PE) files, we present an effective evasion attack model (named EvnAttack) by considering different contributions of the features to the classification problem. To be resilient against the evasion attack, we further propose a secure-learning paradigm for malware detection (named SecDefender), which not only adopts classifier retraining technique but also introduces the security regularization term which considers the evasion cost of feature manipulations by attackers to enhance the system security. Comprehensive experimental results on the real sample collections from Comodo Cloud Security Center demonstrate the effectiveness of our proposed methods.","PeriodicalId":436947,"journal":{"name":"2017 European Intelligence and Security Informatics Conference (EISIC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114571339","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: 79

Whose Hands Are in the Finnish Cookie Jar? 谁的手在芬兰的饼干罐里?

2017 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2017-09-01 DOI: 10.1109/EISIC.2017.25

Jukka Ruohonen, V. Leppänen

引用次数: 7

Are We Really That Close Together? Tracing and Discussing Similarities and Differences between Greek Terrorist Groups Using Cluster Analysis 我们真的那么亲密吗?用聚类分析探讨希腊恐怖组织的异同

2017 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2017-09-01 DOI: 10.1109/EISIC.2017.33

Ioanna K. Lekea, P. Karampelas

{"title":"Are We Really That Close Together? Tracing and Discussing Similarities and Differences between Greek Terrorist Groups Using Cluster Analysis","authors":"Ioanna K. Lekea, P. Karampelas","doi":"10.1109/EISIC.2017.33","DOIUrl":"https://doi.org/10.1109/EISIC.2017.33","url":null,"abstract":"This paper discusses the similarities and differences in both ideology expressed and practices employed by two terrorist groups that operated in Greece between the years of 1975 and 2017: Revolutionary Organization 17 November and Conspiracy of Fire Nuclei. Within this line of thought, we will briefly provide an outline of the political and ideological framework of the groups on focus in an effort to place them within the general historical and political context. We will then focus on the justification and deployment of the terrorist operations as presented in the communiqués published, as well as other announcements and notes distributed in the Social Media by the members of those two terrorist groups. In this context, we elaborate on the tactics of the terrorist groups: their targets, the weapons used and the consequences suffered as a result of their actions are analyzed, in order to evaluate their ideological and - perhaps - ethical standing. To analyze the communiqués of both organizations, two different text mining clustering techniques were applied and the outcomes enabled us to run a comparison between the two terrorist groups and also to examine the possibility of related means, ideology and people behind their different name.","PeriodicalId":436947,"journal":{"name":"2017 European Intelligence and Security Informatics Conference (EISIC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124044855","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: 1

A Statistical Method for Detecting Significant Temporal Hotspots Using LISA Statistics 一种利用LISA统计检测重要时间热点的统计方法

2017 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2017-09-01 DOI: 10.1109/EISIC.2017.24

Martin Boldt, Anton Borg

引用次数: 4

Large Scale Data Collection of Tattoo-Based Biometric Data from Social-Media Websites 基于纹身的社交媒体网站生物特征数据的大规模数据采集

2017 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2017-09-01 DOI: 10.1109/EISIC.2017.27

Michael Martin, J. Dawson, T. Bourlai

{"title":"Large Scale Data Collection of Tattoo-Based Biometric Data from Social-Media Websites","authors":"Michael Martin, J. Dawson, T. Bourlai","doi":"10.1109/EISIC.2017.27","DOIUrl":"https://doi.org/10.1109/EISIC.2017.27","url":null,"abstract":"The use of tattoos as a soft biometric is increasing in popularity among law enforcement communities. There is great need for large scale, publicly available tattoo datasets that can be used to standardize efforts to develop tattoo-based biometric systems. In this work, we introduce a large tattoo dataset (WVU-MediaTatt) collected from a social-media website. Additionally, we provide the source links to the images so that anyone can re-generate this dataset. Our WVU-MediaTatt database contains tattoo sample images from over 1,000 subjects, with two tattoo image samples per subject. To the best of our knowledge, this dataset is significantly bigger than any current released publicly available tattoo dataset, including the recently released NIST Tatt-C dataset. The use of social media in deep learning, data mining, and biometrics has traditionally been a controversial issue in terms of data security and protection of privacy. In this work, we first conduct a full discussion on the issues associated with data collection from social media sources for the use of biometric system development, and provide a framework for data collection. In this study, within the process of creating a new large scale tattoo dataset, we consider the issues and make attempts protect the subject's privacy and information, while ensuring that subjects remain in control of their data in this study and the use of the data adheres to the guidelines proposed by the Heath Care Compliance Association (HCCA) and the U.S. Department of Health & Human Services.","PeriodicalId":436947,"journal":{"name":"2017 European Intelligence and Security Informatics Conference (EISIC)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134435115","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

Detecting Periodic Subsequences in Cyber Security Data 网络安全数据中的周期性子序列检测

2017 European Intelligence and Security Informatics Conference (EISIC) Pub Date : 2017-06-05 DOI: 10.1109/EISIC.2017.40

Matthew Price-Williams, N. Heard, Melissa J. M. Turcotte

引用次数: 9