2021 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献

Verbal Deception Cue Training for the Detection of Phishing Emails 网络钓鱼邮件检测的语言欺骗提示训练

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624738

Jaewan Lim, Lina Zhou, Dongsong Zhang

引用次数: 1

Emotional Analysis of Learning Cybersecurity with Games 游戏学习网络安全的情感分析

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624680

Maria Valero, Lei Li, H. Shahriar, Shahriar Sobhan, M. Handlin, Jinghua Zhang

引用次数: 0

Cyber Security Threat Intelligence Monitoring and Classification 网络安全威胁情报监测与分类

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624746

Bo Wang, Jiann-Liang Chen, Chiao-Lin Yu

引用次数: 1

Mining User’s Opinion Towards the Rising and Falling Trends of the Stock Market: A Hybrid Model 挖掘用户对股票市场涨跌趋势的看法:一个混合模型

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624687

Haoda Qian, Liping Chen, Qi-fen Zha

引用次数: 0

Credible Influence Analysis in Mass Media Using Causal Inference 利用因果推理分析大众传媒的可信影响

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624679

Zizhen Deng, Xiaolong Zheng, Zifan Ye, Zhen Cai, D. Zeng

引用次数: 0

Analysis and detection of application-independent slow Denial of Service cyber attacks 独立于应用程序的慢速拒绝服务网络攻击分析与检测

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624789

M. Sikora, R. Fujdiak, J. Misurec

引用次数: 3

Identifying and Categorizing Malicious Content on Paste Sites: A Neural Topic Modeling Approach 在粘贴网站上识别和分类恶意内容:一种神经主题建模方法

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624765

Tala Vahedi, Benjamin Ampel, S. Samtani, Hsinchun Chen

{"title":"Identifying and Categorizing Malicious Content on Paste Sites: A Neural Topic Modeling Approach","authors":"Tala Vahedi, Benjamin Ampel, S. Samtani, Hsinchun Chen","doi":"10.1109/ISI53945.2021.9624765","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624765","url":null,"abstract":"Malicious cyber activities impose substantial costs on the U.S. economy and global markets. Cyber-criminals often use information-sharing social media platforms such as paste sites (e.g., Pastebin) to share vast amounts of plain text content related to Personally Identifiable Information (PII), credit card numbers, exploit code, malware, and other sensitive content. Paste sites can provide targeted Cyber Threat Intelligence (CTI) about potential threats and prior breaches. In this research, we propose a novel Bidirectional Encoder Representation from Transformers (BERT) with Latent Dirichlet Allocation (LDA) model to categorize pastes automatically. Our proposed BERT-LDA model leverages a neural network transformer architecture to capture sequential dependencies when representing each sentence in a paste. BERT-LDA replaces the Bag-of-Words (BoW) approach in the conventional LDA with a Bag-of-Labels (BoL) that encompasses class labels at the sequence level. We compared the performance of the proposed BERT-LDA against the conventional LDA and BERT-LDA variants (e.g., GPT2-LDA) on 4,254,453 pastes from three paste sites. Experiment results indicate that the proposed BERT-LDA outperformed the standard LDA and each BERT-LDA variant in terms of perplexity on each paste site. Results of our BERT-LDA case study suggest that significant content relating to hacker community activities, malicious code, network and website vulnerabilities, and PII are shared on paste sites. The insights provided by this study could be used by organizations to proactively mitigate potential damage on their infrastructure.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"2 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":"123698003","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

Quantifying Use and Abuse of Personal Information 个人信息的量化使用和滥用

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624816

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

引用次数: 1

Evaluating the Impact of Vaccination on COVID-19 Pandemic Used a Hierarchical Weighted Contact Network Model 利用层次加权接触网络模型评价疫苗接种对COVID-19大流行的影响

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624841

Tianyi Luo, Zhidong Cao, Pengfei Zhao, D. Zeng, Qingpeng Zhang

{"title":"Evaluating the Impact of Vaccination on COVID-19 Pandemic Used a Hierarchical Weighted Contact Network Model","authors":"Tianyi Luo, Zhidong Cao, Pengfei Zhao, D. Zeng, Qingpeng Zhang","doi":"10.1109/ISI53945.2021.9624841","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624841","url":null,"abstract":"The 2019 Novel Coronavirus Disease (COVID-19) vaccines have been placed significant expectation to end the COVID-19 pandemic sooner. However, issues related to vaccines still need to be resolved urgently, including the vaccination number and range. In this paper, we proposed an epidemic spread model based on the hierarchical weighted network. This model fully considers the heterogeneity of the community social contact network and the epidemiological characteristics of COVID-19 in China, which enables to evaluate the potential impact of vaccine efficacy, vaccination schemes, and mixed interventions on the epidemic. The results show that a mass vaccination can effectively control the epidemic but cannot completely eliminate it. In the case of limited resources, giving vaccination priority to the individuals with high contact intensity in the community is necessary. Joint implementation with non-pharmacological interventions strengthening the control of virus transmission. The results provide insights for decision-makers with effective vaccination plans and prevention and control programs.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"28 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":"129179540","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

Exploring the Evolution of Exploit-Sharing Hackers: An Unsupervised Graph Embedding Approach 探索漏洞共享黑客的演变:一种无监督图嵌入方法

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624846

Kaeli Otto, Benjamin Ampel, S. Samtani, Hongyi Zhu, Hsinchun Chen

{"title":"Exploring the Evolution of Exploit-Sharing Hackers: An Unsupervised Graph Embedding Approach","authors":"Kaeli Otto, Benjamin Ampel, S. Samtani, Hongyi Zhu, Hsinchun Chen","doi":"10.1109/ISI53945.2021.9624846","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624846","url":null,"abstract":"Cybercrime was estimated to cost the global economy $945 billion in 2020. Increasingly, law enforcement agencies are using social network analysis (SNA) to identify key hackers from Dark Web hacker forums for targeted investigations. However, past approaches have primarily focused on analyzing key hackers at a single point in time and use a hacker’s structural features only. In this study, we propose a novel Hacker Evolution Identification Framework to identify how hackers evolve within hacker forums. The proposed framework has two novelties in its design. First, the framework captures features such as user statistics, node-level metrics, lexical measures, and post style, when representing each hacker with unsupervised graph embedding methods. Second, the framework incorporates mechanisms to align embedding spaces across multiple time-spells of data to facilitate analysis of how hackers evolve over time. Two experiments were conducted to assess the performance of prevailing graph embedding algorithms and nodal feature variations in the task of graph reconstruction in five time-spells. Results of our experiments indicate that Text-Associated Deep-Walk (TADW) with all of the proposed nodal features outperforms methods without nodal features in terms of Mean Average Precision in each time-spell. We illustrate the potential practical utility of the proposed framework with a case study on an English forum with 51,612 posts. The results produced by the framework in this case study identified key hackers posting piracy assets.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"51 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":"117216425","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