2021 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献_第2页

Generating Optimal Attack Paths in Generative Adversarial Phishing 生成式对抗网络钓鱼中最优攻击路径的生成

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624751

Rayah Al-Qurashi, Ahmed Aleroud, A. Saifan, Mohammad Alsmadi, I. Alsmadi

引用次数: 4

Identifying Corporate Political Trends Online 在线识别企业政治趋势

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624686

Lauren Maunder, Joshua Lyons, Lauren Anderson, Joe Harrison, Brian Timana-Gomez, Paul O'Donnell, Kiernan B. George, Alan J. Michaels

引用次数: 1

User Role Identification in Software Vulnerability Discussions over Social Networks 基于社交网络的软件漏洞讨论中的用户角色识别

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624857

Rebecca Jones, Daniel Fortin, S. Chatterjee, Dennis G. Thomas, Lisa Newburn

{"title":"User Role Identification in Software Vulnerability Discussions over Social Networks","authors":"Rebecca Jones, Daniel Fortin, S. Chatterjee, Dennis G. Thomas, Lisa Newburn","doi":"10.1109/ISI53945.2021.9624857","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624857","url":null,"abstract":"Understanding and early awareness of software vulnerabilities is vital for preventing and mitigating potential impacts from cybersecurity events. One step toward early characterization of software vulnerabilities may involve analyzing discussion and spread of information in online social networks. Prior work has used information from such discussions over multiple online forums to develop dynamic networks among users followed by analysis of structure, spread, and information evolution. In this work, we advance the state-of-the-art by focusing on data-driven learning of types, roles, and transition of roles exhibited by users over time. In social networks, users take on particular roles based on their actions and structure of the network. Identifying “meaningful” roles can help separate potential users of interest from the larger community, and identify patterns in a network relevant for generating early insights into the extent of software vulnerabilities. We identify and compare roles found in online forums (e.g., Twitter) using feature-based Non-negative Matrix Factorization coupled with topological and influence-based measures of centrality. Since users’ activities change over time, we also analyze role evolution in dynamic networks.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116114834","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

Domain-oriented News Recommendation in Security Applications 安全应用中面向领域的新闻推荐

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624817

Ying Sun, Qingchao Kong, Luwen Huangfu, Jin Pan

{"title":"Domain-oriented News Recommendation in Security Applications","authors":"Ying Sun, Qingchao Kong, Luwen Huangfu, Jin Pan","doi":"10.1109/ISI53945.2021.9624817","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624817","url":null,"abstract":"The unprecedented growth of information on the Internet has brought about the problem of information overload. To alleviate this problem, news recommendation aims to select news articles for users according to their personal interests. In security applications such as intelligence collection and public opinion monitoring, it is of great importance to obtain valuable information quickly from massive news resources. Different from other application settings, users in security-related scenarios tend to browse news with a domain-oriented purpose. In contrast to the existing news recommendation methods which focus on general-purpose solutions, news recommendation in security applications needs domain-oriented solutions to incorporate users’ interests in a specific domain. To this end, in this paper, we propose the problem of domain-oriented news recommendation and develop a specific news recommendation model for security applications. Specifically, our proposed Domain-oriented News Recommendation (DNR) model extracts both general and specific preferences of the user, and performs matching between the user and the candidate news from the above two aspects to combine into the final result. We construct three security-related datasets using a large-scale real-world dataset and validate the effectiveness of our method.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122720225","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

Behaviors of Unwarranted Password Identification via Shoulder-Surfing during Mobile Authentication 移动认证中肩部冲浪的非法密码识别行为

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624730

Lina Zhou, Kanlun Wang, Jianwei Lai, Dongsong Zhang

引用次数: 4

Differences in Geographic Profiles When Using Street Routing Versus Manhattan Distances in Buffer Zone Radii Calculations 在缓冲区半径计算中使用街道路线与曼哈顿距离时地理概况的差异

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624736

Stefano Z. Stamato, Andrew J. Park, Brian Eng, Valerie Spicer, Herbert H. Tsang, D. Rossmo

{"title":"Differences in Geographic Profiles When Using Street Routing Versus Manhattan Distances in Buffer Zone Radii Calculations","authors":"Stefano Z. Stamato, Andrew J. Park, Brian Eng, Valerie Spicer, Herbert H. Tsang, D. Rossmo","doi":"10.1109/ISI53945.2021.9624736","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624736","url":null,"abstract":"Geographic profiling (GP) is a technique used to uncover probable areas where an offender might be anchored based on a set of interconnected crime locations. GP applies a distance-decay function modulated by a buffer zone radius to the area under investigation to produce a probability surface indicating areas where an offender’s anchor point is likely located. Distance approximations are often utilized to create profiles, such as Euclidean and Manhattan distance. Though distance approximations have worked well in most applications of GP, existing research falls short in exploring the costs and benefits associated with a more accurate distance metric, such as street routing distances. This study examines the costs and benefits of street routing distance metrics by applying GP to 19 crime series data sets. Two profiles are generated for each series: one using Manhattan distances and the other using street routing distances in the buffer zone radius calculations with costs recorded at each step. The resulting probability surfaces are compared, and the results obtained conform with previous literature in determining that Manhattan distance approximations often underestimate the offender’s travel distances. The results indicate that street routing distances may be a viable alternative to distance approximations particularly in areas with low road density.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127789758","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

A Systematic Evaluation of EM and Power Side-Channel Analysis Attacks on AES Implementations 对AES实现的电磁攻击和功率侧信道分析攻击的系统评估

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624778

Vishnuvardhan V. Iyer, Meizhi Wang, J. Kulkarni, Ali E. Yılmaz

{"title":"A Systematic Evaluation of EM and Power Side-Channel Analysis Attacks on AES Implementations","authors":"Vishnuvardhan V. Iyer, Meizhi Wang, J. Kulkarni, Ali E. Yılmaz","doi":"10.1109/ISI53945.2021.9624778","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624778","url":null,"abstract":"The effectiveness of coarse- and fine-grained electromagnetic (EM) side-channel analysis (SCA) attacks, as well as power SCA attacks, are empirically evaluated on implementations of the Advanced Encryption Standard (AES) algorithm. Coarse-grained EM and power SCA attacks use a single sensor configuration to measure the aggregated EM emanation or power consumption for a large set of encryptions, and then analyze this set of signals to recover all encryption key bytes. In contrast, fine-grained EM SCA attacks first perform high-resolution scans with relatively small probes in multiple orientations to localize on-chip information leakage, and then use a specific probe configuration for each key byte to collect and analyze signals. The fine-grained EM SCA attacks are found to be up to >70× more effective than coarse-grained EM and power SCA attacks when extracting the key from 3 implementations of 128-bit AES. They are constrained, however, by the potentially prohibitive cost of the initial search to identify effective probe configurations. Search protocols, categorized according to the threat model, to reduce this one-time acquisition cost are presented and are found to require ~8–15× fewer measurements compared to an exhaustive search.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116122925","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: 5

Exploring Differences Among Darknet and Surface Internet Hacking Communities 探索暗网和表面网络黑客社区之间的差异

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624681

Zhiyuan Ding, Victor A. Benjamin, Weifeng Li, Xueyan Yin

{"title":"Exploring Differences Among Darknet and Surface Internet Hacking Communities","authors":"Zhiyuan Ding, Victor A. Benjamin, Weifeng Li, Xueyan Yin","doi":"10.1109/ISI53945.2021.9624681","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624681","url":null,"abstract":"Cyber-threat intelligence (CTI) has matured into its own industry within recent years. CTI efforts frequently involve scrutinizing data within Darknet communities to understand emerging threats. Many hackers within the Darknet share knowledge and other information through a variety of formats, including video. At the same time, many hackers are also making use of the “surface” Internet and traditional video-sharing platforms to disseminate hacking knowledge. Gleaning intelligence from the Darknet can be a very laborious and costly task, raising the question of how meaningful and valuable are the hacker patterns that can be observed on the surface Internet. Extant research contains no studies that compare and contrast hacking videos uploaded to the Darknet versus those uploaded to traditional Internet communities. In this research-in-progress, a testbed of hacking videos is constructed by sourcing videos from a popular video-sharing website, as well as several Darknet forums. The testbed is scrutinized to understand differences in how the populations of users watching such videos respond to them, and whether there are any unique engagement patterns that emerge within the Darknet and surface Internet populations. The results of this work serve to justify further investigations into the hacker knowledge gap between the Darknet and the traditional Internet.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134263283","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

Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach 从社交媒体中自动提取个人身份信息以提高隐私意识:一种深度迁移学习方法

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624678

Yizhi Liu, Fangyu Lin, Mohammadreza Ebrahimi, Weifeng Li, Hsinchun Chen

{"title":"Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach","authors":"Yizhi Liu, Fangyu Lin, Mohammadreza Ebrahimi, Weifeng Li, Hsinchun Chen","doi":"10.1109/ISI53945.2021.9624678","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624678","url":null,"abstract":"Internet users have been exposing an increasing amount of Personally Identifiable Information (PII) on social media. Such exposed PII can be exploited by cybercriminals and cause severe losses to the users. Informing users of their PII exposure in social media is crucial to raise their privacy awareness and encourage them to take protective measures. To this end, advanced techniques are needed to extract users’ exposed PII in social media automatically, whereas most existing studies remain manual. While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency. However, DL-based IE models often require large-scale labeled data for training, but PII-labeled social media posts are difficult to obtain due to privacy concerns. Also, these models rely heavily on pre-trained word embeddings, while PII in social media often varies in forms and thus has no fixed representations in pre-trained word embeddings. In this study, we propose the Deep Transfer Learning for PII Extraction (DTL-PIIE) framework to address these two limitations. DTL-PIIE transfers knowledge learned from publicly available PII data to social media in order to address the problem of rare PII-labeled data. Moreover, our framework leverages Graph Convolutional Networks (GCNs) to incorporate syntactic patterns to guide PIIE without relying on pre-trained word embeddings. Evaluation against benchmark IE models indicates that our approach outperforms state-of-the-art DL-based IE models. An ablation analysis further confirms the efficacy of each component in our model. Our proposed framework can facilitate various applications, such as PII misuse prediction and privacy risk assessment, thereby protecting the privacy of internet users.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134312953","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

Tracing Relevant Twitter Accounts Active in Cyber Threat Intelligence Domain by Exploiting Content and Structure of Twitter Network 利用Twitter网络的内容和结构追踪活跃在网络威胁情报领域的相关Twitter账号

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624754

Avishek Bose, Shreya Gopal Sundari, Vahid Behzadan, W. Hsu

引用次数: 1