picture   Yanfang (Fanny) Ye, Ph.D.

T. and D. Schroeder Associate Professor
Department of Computer and Data Sciences
Case School of Engineering
Case Western Reserve University

Email: yanfang.ye (at) case (dot) edu



Research Interests

"Innovation, research and education - for a better world!"

I am currently the Theodore L. and Dana J. Schroeder Associate Professor in the department of Computer and Data Sciences (CDS) at Case Western Reserve University (CWRU). My research areas mainly include Cybersecurity, Data Mining, Machine Learning, and Health Intelligence. With long-term and strong collaboration with industry partners, I have proposed and developed cloud-based solutions for mining big data in the area of cybersecurity, especially for malware detection and adversarial machine learning. I have had over eighty publications in my fields (e.g., ACM CSUR, IEEE TNNLS, IEEE TSMC, KAIS, JCV, SIGKDD, ICDM, CIKM, WWW, AAAI, IJCAI, ACSAC). My proposed techniques have significantly reduced the time needed to detect new malicious software - from weeks to seconds, which have been incorporated into popular commercial cybersecurity products including Comodo and Kingsoft Antivirus that protect millions of users worldwide. In recent years, I have expanded my research on health intelligence with the focus on combating opioid crisis and epidemic. I recently received received the MetroLab Innovation of the Month (2020), the NSF Career Award (2019), the IJCAI Early Career Spotlight (2019), the AICS 2019 Challenge Problem Winner, the SIGKDD 2017 Best Paper Award and SIGKDD 2017 Best Student Paper Award (Applied Data Science Track), the IEEE EISIC 2017 Best Paper Award, and the New Researcher of the Year Award (2016-2017) at WVU. I have received multiple prestigious awards from the NSF and DoJ/NIJ in support of my research. All these awards are highly competitive.


Position Openings: To Perspective Students and Post-docs

  • I am currently looking for multiple Ph.D. students and Post-docs doing supervised research or independent study with me. If you are a well motivated and dedicated student pursuing a Ph.D. degree or post-doc related to the areas of Cybersecurity, Data Mining, Machine Learning, and Health Intelligence, please send me an email with your CV.
  • NSF REU Fellowship Opportunity at CWRU: During the period September 1, 2020 to April 30, 2021, there is an opportunity for THREE CWRU undergraduate students (juniors/seniors in but not limited to computer and data sciences) to be involved with the NSF funded project entitled "RAPID: AI- and Data-driven Integrated Framework for Hierarchical Community-level Risk Assessment" and work collaboratively with the research team on developing AI and data driven methods for COVID-19 relief. Each student will receive an $8,000 fellowship for their participation in the REU experience. Detailed information and application process can be accessed here.


alpha-Satellite: Help Combat COVID-19 in the U.S.

An AI-driven System for Real-time COVID-19 Risk Assessment

  • The novel coronavirus and its deadly outbreak have posed grand challenges to human society; the World Health Organization (WHO) characterized coronavirus disease (COVID-19) a global pandemic. A growing number of areas reporting local sub-national community transmission would represent a significant turn for the worse in the battle against the novel coronavirus, which points to an urgent need for expanded surveillance so we can better understand the spread of COVID-19 and thus better respond with actionable strategies for community mitigation. By advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and real-time data generated from heterogeneous sources, as an initial offering, we have proposed and developed an AI-driven system (named alpha-Satellite), to provide hierarchical community-level risk assessment to assist with the development of strategies for combating the fast evolving COVID-19 pandemic. More specifically, given a specific location (either user input or automatic positioning), the developed system will automatically provide risk indices associated with it in a hierarchical manner (e.g., state, county, specific location) to enable individuals to select appropriate actions for protection while minimizing disruptions to daily life to the extent possible. After we launched our system to the public for beta test on April 20, it had attracted 42,546 users in the first week. The large number of its users indicate the high demand from the public for effective computational tools to assist people with actionable strategies. The system has receiving a lot of good feedback from the media and users on the ease of use as well as the utility of the relative risk estimation. The developed system, paper and the generated benchmark datasets have been made publicly accessible through our website.

    [System] [Paper] [Bib] [Benchmark Datasets]

    Yanfang Ye (), Shifu Hou, Yujie Fan, Yiyue Qian, Yiming Zhang, Shiyu Sun, Qian Peng, Kenneth Laparo. "alpha-Satellite: An AI-driven System and Benchmark Datasets for Hierarchical Community-level Risk Assessment to Help Combat COVID-19", arXiv preprint arXiv:2003.12232, 2020.
    [System] [Paper] [Bib]
    [Benchmark Datasets]


Latest News

  • To help combat COVID-19, we have proposed and developed an AI-driven system, named alpha-Satellite, to provide hierarchical community-level risk assessment to assist with the development of strategies for community mitigation. The developed system, paper and the generated benchmark datasets have been made publicly accessible through our website. The work has recently received the MetroLab Innovation of the Month (May 2020).
  • Our paper entitiled "Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and Application to AICS'2019 Challenge" recently received the AAAI Workshop on Artificial Intelligence for Cyber Security Challenge Problem Winner. Thank my collabrators! Congratulations to our team!
  • Our paper entitiled "Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense" recently received the IEEE EISIC 2017 Best Paper Award. Congratulations to our team! Congratulations to my student Lingwei Chen!
  • Our paper recently received the SIGKDD 2017 Best Paper Award and the SIGKDD 2017 Best Student Paper Award (Applied Data Science Track). Congratulations to our team for the SIGKDD 2017 Best Paper Award! Congratulations to my student Shifu Hou for the SIGKDD 2017 Best Student Paper Award! Our video won SIGKDD 2017 Audience Appreication Award Finalist (26,033 views on YouTube).

    Shifu Hou, Yanfang Ye (), Yangqiu Song, Melih Abdulhayoglu. "HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network", Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD) , 2017. (Download paper here; Download slides here; and Click this link to watch the video)


Selected Publications

* indicates that the author is my student; indicates the corresponding author.

Book Chapters

  • Yanfang Ye. "Intelligent Malware Detection by Applying Data Mining Techniques", In T. Li eds., Data Mining Where Theory Meets Practice, Xiamen University Press, 2013, ISBN 978-7-5615-4294-1.

Journal Publications

  • Yanfang Ye (), Yujie Fan*, Shifu Hou*, Yiming Zhang*, Yiyue Qian*, Shiyu Sun*, Qian Peng*, Mingxuan Ju*, Wei Song, Kenneth Loparo. "a-Satellite: An AI-driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States", IEEE Journal of Biomedical and Health Informatics (J-BHI), 2020. MetroLab Innovation of the Month.
  • Jianfei Zhang*, Lifei Chen, Yanfang Ye, Gongde Guo, Rongbo Chen, Alain Vanasse, Shengrui Wang. "Survival Neural Networks for Time-to-event Prediction in Longitudinal Study", Knowledge and Information Systems (KAIS), 2020.
  • Liang Zhao, Feng Chen, Yanfang Ye. "Efficient Learning with Exponentially-Many Conjunctive Precursors for Interpretable Spatial Event Forecasting", IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.
  • Qingzhe Li, Amir Alipour-Fanid, Martin Slawski, Yanfang Ye, Lingfei Wu, Kai Zeng, Liang Zhao. "Large-scale Cost-aware Classification Using Feature Computational Dependencies", IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.
  • Junxiang Wang, Liang Zhao, Yanfang Ye, Yuji Zhang. "Adverse Event Detection by Integrating Twitter Data and VAERS", Journal of Biomedical Semantics, 9(19), 2018.
  • Yanfang Ye, Tao Li, Donald Adjeroh, S. Sitharama Iyengar. "A Survey on Malware Detection Using Data Mining Techniques", ACM Computing Surveys (ACM CSUR), Vol. 50, Issue 3, Article No. 41, 2017.
  • Yanfang Ye (), Lingwei Chen*, Shifu Hou*, William Hardy*, Xin Li. "DeepAM: A Heterogeneous Deep Learning Framework for Intelligent Malware Detection", Knowledge and Information Systems (KAIS), Vol. PP (52): 1~21, 2017.
  • Gongde Guo, Lifei Chen, Yanfang Ye, Qingshan Jiang. "Cluster Validation Method for Determining the Number of Clusters in Categorical Sequences", IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), Vol. PP (99): 1-13, 2016.
  • Ming Ni, Tao Li, Qianmu Li, Hong Zhang, Yanfang Ye, Qingshan Jiang. "FindMal: A File-to-file Social Network Based Malware Detection Framework", Knowledge-Based Systems (KBS), 112: 142-151, 2016.
  • Yujie Fan*, Yanfang Ye, Lifei Chen. "Malicious Sequential Pattern Mining for Automatic Malware Detection", Expert Systems with Applications (ESWA), Vol. 52, pp. 16~25, 2016.
  • Yanfang Ye (), Tao Li, Haiyin Shen. "Soter: Smart Bracelets for Children's Safety", ACM Transactions on Intelligent Systems and Technology (ACM TIST), Vol.6, No. 4, Article 46, 2015.
  • Lifei Chen, Yanfang Ye, Gongde Guo, Jianping Zhu. "Kernel-based linear classification on categorical data", Soft Computing, pp. 1~13, 2015.
  • Weiwei Zhuang, Yanfang Ye, Yong Chen, Tao Li. "Ensemble Clustering for Internet Security Applications", IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, Vol 42, pp. 1784~1796, 2012.
  • Weiwei Zhuang, Yanfang Ye, Tao Li, Qingshan Jiang. "Intelligent phishing website detection using classification ensemble", Systems Engineering - Theory & Practice, Vol. 31, issue (10): 2008-2020, 2011.
  • Yanfang Ye, Tao Li, Qingshan Jiang, Youyu Wang. "CIMDS: Adapting post-processing techniques of associative classification for malware detection system", IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, Vol 40, pp. 298~307, 2010.
  • Yanfang Ye, Lifei Chen, Dingding Wang, Tao Li, Qingshan Jiang, Min Zhao. "SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging", Journal in Computer Virology, Vol 5, pp. 283~293, 2009.
  • Yanfang Ye, Tao Li, Kai Huang, Qingshan Jiang, Yong Chen. "Hierarchical Associative Classifier (HAC) for Malware Detection from the Large and Imbalanced Gray List", Journal of Intelligent Information Systems, Vol 35, pp. 1~20, 2009.
  • Weiwei Zhuang, Yanfang Ye, Qingshan Jiang, Zhixue Han. "Application of Incremental Associative Classification Method in Malware Detection", Computer Engineering, 35 (4): 159-161, 2009.
  • Yanfang Ye, Dingding Wang, Tao Li, Dongyi Ye, Qingshan Jiang. "An Intelligent PE-Malware Detection System Based on Association Mining", Journal in Computer Virology, Vol 4, pp. 323~334, 2008.

Conference Publications

  • Jianan Zhao, Xiao Wang, Chuan Shi, Zekuan Liu, Yanfang Ye. "Network Schema Preserved Heterogeneous Information Network Embedding", 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020. (12.6% acceptance rate)
  • Yanfang Ye (), Shifu Hou*, Lingwei Chen*, Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong, Fudong Shao. "Out-of-sample Node Representation Learning for Heterogeneous Graph in Real-time Android Malware Detection", 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. (17.9% acceptance rate)
  • Yujie Fan*, Yiming Zhang*, Shifu Hou*, Lingwei Chen*, Yanfang Ye (), Chuan Shi, Liang Zhao, Shouhuai Xu. "iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow", 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. (17.9% acceptance rate)
  • Yiming Zhang*, Yujie Fan*, Wei Song, Shifu Hou*, Yanfang Ye (), Xin Li, Liang Zhao, Chuan Shi, Jiabin Wang, Qi Xiong. "Your Style Your Identity: LeveragingWriting and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network", The Web Conference (WWW), 2019. (20% acceptance rate for short paper)
  • Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, Yanfang Ye. "Heterogeneous Graph Attention Network", The Web Conference (WWW), 2019. (18% acceptance rate for regular paper)
  • Qingzhe Li, Liang Zhao, Yi-Ching Lee, Yanfang Ye, Jessica Lin, Lingfei Wu. "Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior", 19th IEEE International Conference on Data Mining (ICDM), 2019. (18.5% acceptance rate)
  • Shifu Hou*, Yujie Fan*, Yiming Zhang*, Yanfang Ye (), Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong and Fudong Shao. "aCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model", International Conference on Information and Knowledge Management (CIKM), 2019. (19.4% acceptance rate)
  • Yiming Zhang*, Yujie Fan*, Yanfang Ye (), Liang Zhao, Chuan Shi. "Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework", International Conference on Information and Knowledge Management (CIKM), 2019. (19.4% acceptance rate)
  • Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang. "Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting", P33rd AAAI Conference on Artificial Intelligence (AAAI), 2019. (16.7% acceptance rate)
  • Deqiang Li, Qianmu Li, Yanfang Ye, Shouhuai Xu. "Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and Application to AICS'2019 Challenge", AAAI Workshop on Artificial Intelligence for Cyber Security (AICS), 2019. AICS 2019 Challenge Problem Winner.
  • Yujie Fan*, Shifu Hou*, Yiming Zhang*, Yanfang Ye (), Melih Abdulhayoglu. "Gotcha - Sly Malware! Scorpion: A Metagraph2vec Based Malware Detection System", Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), 2018. (22.5% acceptance rate)
  • Yujie Fan*, Yiming Zhang*, Yanfang Ye (), Xin Li. "Automatic Opioid User Detection from Twitter: Transductive Ensemble Built on Different Meta-graph Based Similarities over Heterogeneous Information Network", 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018. (20.5% acceptance rate)
  • Junxiang Wang, Liang Zhao, Yanfang Ye. "Semi-supervised Multi-instance Learning for Flu Shot Adverse Event Detection", IEEE international conference on Big Data (BigData), 2018. (18.9% acceptance rate)
  • Yanfang Ye (), Shifu Hou*, Lingwei Chen*, Xin Li, Liang Zhao, Shouhuai Xu, Jiabin Wang, Qi Xiong. "ICSD: An Automatic System for Insecure Code Snippet Detection in Stack Overflow over Heterogeneous Information Network", Annual Computer Security Applications Conference (ACSAC), 2018. (20.1% acceptance rate)
  • Shifu Hou*, Yanfang Ye (), Yangqiu Song, Melih Abdulhayoglu. "HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network", Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), 2017. SIGKDD 2017 Best Paper Award and SIGKDD 2017 Best Student Paper Award (Applied Data Science Track). (9.2% acceptance rate for oral)
    SIGKDD 2017 Audience Appreciation Award Finalist: 26,033 views on YouTube.
  • Lingwei Chen*, Yanfang Ye (), Thirimachos Bourlai. "Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense", IEEE European Intelligence and Security Informatics Conference (EISIC), 2017. IEEE EISIC 2017 Best Paper Award. (~25% acceptance rate)
  • Lingwei Chen*, Shifu Hou*, Yanfang Ye (). "SecureDroid: Enhancing Security of Machine Learning-based Detection against Adversarial Android Malware Attacks", Annual Computer Security Applications Conference (ACSAC), 2017. (19.7% acceptance rate)
  • Yujie Fan*, Yiming Zhang*, Yanfang Ye (), Xin Li, Wanhong Zheng. "Social Media for Opioid Addiction Epidemiology: Automatic Detection of Opioid Addicts from Twitter and Case Studies", ACM International Conference on Information and Knowledge Management (CIKM), 2017. (~20% acceptance rate)
  • Yanfang Ye, Tao Li, Shenghuo Zhu, Weiwei Zhuang, Egemen Tas, Umesh Gupta, Melih Abdulhayoglu. "Combining File Content and File Relations for Cloud Based Malware Detection", Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD) , pp. 222~230, 2011. (8% acceptance rate for oral)
  • Yanfang Ye, Tao Li, Yongchen, Qingshan Jiang. "Automatic Malware Categorization Using Cluster Ensemble", Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD) , pp. 95~104, 2010. (10.9% acceptance rate for oral)
  • Yanfang Ye, Tao Li, Qingshan Jiang, Zhixue Han, Li Wan. "Intelligent File Scoring System for Malware Detection from the Gray List", Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD) , pp. 1385~1394, 2009. (9.8% acceptance rate for oral)
  • Yanfang Ye, Dingding Wang, Tao Li, Dongyi Ye. "IMDS: Intelligent Malware Detection System", Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD) , pp. 1043~1047, 2007. (17.9% acceptance rate)


Post-docs

  • Jianfei Zhang (Post-doc, Fall 2019 -- )

Current Students

  • Shifu Hou (Ph.D. Student, Fall 2016 -- )
  • Aaron Saas (Ph.D. Student, Spring 2016 -- )
  • Yujie Fan (Ph.D. Student, Fall 2016 -- )
  • Yiming Zhang (Ph.D. Student, Fall 2016 -- )
  • Yiyue Qian (Ph.D. Student, Fall 2019 -- )
  • Hadeel Almaimani (Ph.D. Student, Fall 2019 -- )
  • Mingxuan Ju (Ph.D. Student, Summer 2020 -- )
  • Shiyu Sun (Ph.D. Student, Summer 2020 -- )
  • Qianlong Wen (Ph.D. Student, Fall 2020 -- )
  • Jianan Zhao (Ph.D. Student, Fall 2020 -- )
  • Qian Peng (MS Student, Fall 2019 -- )

Graduated Students

  • Lingwei Chen (Ph.D., May 2019)
  • Shifu Hou (MS, June 2019)
  • Yiming Zhang (MS, December 2018)
  • Jian Liu (MS, June 2018)
  • Sai Venkata Akhil Thammineni (MS, November 2017)
  • Srinivas Garapati, MS (MS, October 2017)
  • Utsav Kirtikumar Upadhyay (MS, September 2017)
  • Madhusudhan Reddy Boddu (MS, March 2017)
  • Sai Ram Nellutla (MS, December 2015)
  • Dominique Amos (BS, December 2015)
  • Alex Finkelstein (BS, May 2015)
  • Kevin Hao (BS, May 2015)
  • Michael Hite (BS, May 2015)
  • Joshua Suess (BS, May 2015)
  • Jacob Sutton (BS, May 2015)
  • Sam Wood (BS, May 2015)
  • Reem AL Alshikh (BS, May 2015)
  • Zainab Alamri (BS, May 2015)

Former Students

  • Madhuri Siddula (Ph.D. Student, Spring 2015 - Summer 2016)
  • William B. Hardy (MS Student, Spring 2015 - Summer 2017)


Teaching

  • EECS 600: CyberAI: AI in Cybersecurity [Spring 2020]
    Lectures: R 5:30pm -- 8:00pm in White-324
    Office Hours: Thursday 2:30pm -- 4:30pm, or by appointment, in Olin-610
  • EECS 349/444: Computer Security [Fall 2019]
  • CS 573: Advanced Data Mining [Fall 2018, Spring 2017, Spring 2016, Spring 2015]
  • CS 467: Practicing Cybersecurity: Attacks and Countermeasures [Spring 2019, Spring 2018]
  • CS 569: Cybersecurity and Big Data Analytics [Fall 2017, Fall 2016, Fall 2015, Fall 2014]
  • CS 426: Discrete Mathematics [Spring 2014]