CAREER: Securing Cyberspace: Gaining Deep Insights into the Online Underground Ecosystem
CAREER: Securing Cyberspace: Gaining Deep Insights into the Online Underground Ecosystem
批准号:
1940859
负责人:
Yanfang Ye
金额:
$50.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-06-05 至 2022-03-31
中文摘要
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英文摘要
As the Internet becomes increasingly ubiquitous, it offers a low-risk harbor for cybercrime -- illegal activities such as hacking and online scams. Cybercrime is increasingly enabled by an online underground ecosystem, within which are anonymous forums and so-called dark web platforms for cybercriminals to exchange knowledge and trade in illicit products and services. To better protect the trust of legitimate institutions and users within cyberspace, and to disrupt or mitigate the harm of illegal markets, there is an urgent need to gain deep insights into the online underground ecosystem and understand its intricate relationships. The goal of this project is to design and develop an integrated computational framework for in-depth investigation of the online underground ecosystem and thus to help secure cyberspace by producing data-driven interventions of cybercrimes. The research will have three main parts. First, the research team will design methods and develop algorithmic and scalable techniques to automate the analysis of online underground markets. Second, the team will develop a novel framework for cross-market user identification and profiling. Third, the team will design an innovative model and propose algorithms for structural analysis of cybercriminal social networks to gain deep insights into the organization and operation of cybercriminals in the ecosystem. The broader impacts of this work include benefits to scientific communities and society as a whole by developing interventions into online crime to secure cyberspace for its users. The establishment of a cybersecurity lab through this project will enhance education and workforce training in cybersecurity. The project integrates research with education through curriculum development, the participation of underrepresented groups, and student mentoring activities for a newly-established cybersecurity degree program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples: Principles, Framework, and Application to AICS’2019 Challenge
增强深度神经网络针对对抗性恶意软件样本的鲁棒性:原则、框架和 AICS 2019 挑战赛的应用
DOI:
--
发表时间:
2019
期刊:
2019
影响因子:
--
作者:
[Li, Deqiang, Li, Qianmu, Ye, Yanfang, Xu, Shouhuai]
通讯作者:
Xu, Shouhuai
DOI:
10.1145/3357384.3357876
发表时间:
2019-11
期刊:
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
影响因子:
--
作者:
[Yiming Zhang;Yujie Fan;Yanfang Ye;Liang Zhao;C. Shi]
通讯作者:
Yiming Zhang;Yujie Fan;Yanfang Ye;Liang Zhao;C. Shi
Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior
用于受控实验的对比特征依赖模式挖掘及其在驾驶行为中的应用
DOI:
10.1109/icdm.2019.00146
发表时间:
2019
期刊:
19th IEEE International Conference on Data Mining (ICDM
影响因子:
--
作者:
[Li, Qingzhe, Zhao, Liang, Lee, Yi-Ching, Ye, Yanfang, Lin, Jessica, Wu, Lingfei]
通讯作者:
Wu, Lingfei
DOI:
10.1609/aaai.v35i5.16600
发表时间:
2021-05
期刊:
影响因子:
--
作者:
[Jianan Zhao;Xiao Wang;C. Shi;Binbin Hu;Guojie Song;Yanfang Ye]
通讯作者:
Jianan Zhao;Xiao Wang;C. Shi;Binbin Hu;Guojie Song;Yanfang Ye
DOI:
10.1609/aaai.v35i9.16947
发表时间:
2021-05
期刊:
影响因子:
--
作者:
[Shifu Hou;Yujie Fan;Mingxuan Ju;Yanfang Ye;Wenqiang Wan;Kui Wang;Y. Mei;Qi Xiong;Fudong Shao]
通讯作者:
Shifu Hou;Yujie Fan;Mingxuan Ju;Yanfang Ye;Wenqiang Wan;Kui Wang;Y. Mei;Qi Xiong;Fudong Shao
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