Data Security

Deguang Kong

Expected to graduate in Dec. **** and use OPT to start working. Looking for Full-time Research

Working

or Engineering or R & D jobs.

Information

Strength

• Designing (implementing) statistical machine learning models to analyze data in different con-

texts (e.g., image/video data, web/text data, biology data, network data, etc) for sensing envi-

ronment and understanding human behaviors.

• Cyber security/privacy (including defense/attack) using statistical data analysis.

Current address: 308 College Street Apt A, Arlington, TX, 76013 Mobile: 817-***-****

Contact

E-mail: ********@*****.***

Information

University of Texas at Arlington Aug. 2010 – now

Education

• Ph.D Candidate, Dept. of CSE, Supervisor: Chris Ding, GPA: 3.85/4.0

Pennsylvania State University Aug. 2008 – Aug.2010

• Ph.D Training, College of Information Science and Technology, Supervisor: Peng Liu

University of Science and Technology of China Aug.2005 – Aug.2008

• Master Student, Dept. of Electrical Engineering, GPA: 3.83/4.0

Shandong University, P. R. China Sep.2001 – Jun.2005

• Undergraduate, Dept. of CSE, GPA: 3.75/4.0

Internships

• Los Alamos National Lab, Los Alamos, NM, Jun–Dec. 2012

• NEC Lab America, Cupertino, CA, May–Aug, 2013

Published 16 papers, 10 appeared in top conferences.

Selected

1. Deguang Kong and Chris Ding. Iterative Re-weighted Methods for General Group Lasso

Publications

Problem on Arbitrary Structure, IEEE International Conference on Data Mining (ICDM’13)

(94/809=11.62%)

2. Deguang Kong and Chris Ding. Minimal Shrinkage for Noisy Data Recovery using Schatten-

p norm, The 23rd European Conference on Machine Learning and Principles and Practice of

Knowledge Discovery in Databases (ECML/PKDD’13)(111/443=25%).

3. Deguang Kong and Guanhua Yan. Discriminant Malware Distance Learning on Structural

Information for Automated Malware Classification, 19th ACM SIGKDD Conference on Knowl-

edge Discovery and Data Mining (KDD’13)

4. Deguang Kong and Guanhua Yan. Discriminant Malware Distance Learning on Structural

Information for Automated Malware Classification, ACM sigmetrics/performance Joint Inter-

national Conference on Measurement and Modeling of Computer Systems, SIGMETRICS’13,

poster, ((27+33)/196=30.61%)

5. Guanhua Yan, Nathan Brown, and Deguang Kong, Exploring Discriminatory Features for

Automated Malware Classification, Proceedings of the 10th Conference on Detection of Intru-

sions and Malware & Vulnerability Assessment (DIMVA’13). (12/38 = 31.58%)

6. Miao Zhang and Chris Ding and Deguang Kong. Collective Kernel Construction in Noisy

Environment, SIAM International Conference on Data Mining (SDM’13) (89/348=25.5%)

7. Deguang Kong and Chris Ding. A Semi-Definite Positive Linear Discriminant Analysis and

its Applications, IEEE International Conference on Data Mining (ICDM’12)(151/756=19.97%)

8. Deguang Kong and Chris Ding. Maximum Consistency Preferential Random Walks, The 22th

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery

in Databases (ECML/PKDD’12)(105/443=23.7%)

9. Deguang Kong, Chris Ding, Heng Huang and Feiping Nie. An iterative locally linear embed-

ding algorithm, The 29th International Conference on Machine Learning(ICML’12)(243/890=27.3%)

10. Deguang Kong, Chris Ding, Heng Huang and Haifeng Zhao. Multi-label ReliefF and F-

statistic Feature Selections for Image Annotation, IEEE International Conference on Computer

Vision and Pattern Recognition(CVPR’12)(465/1933 = 24%)

11. Chris Ding and Deguang Kong. Nonnegative Matrix Factorization using a robust error func-

tion, 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP’12)

12. Deguang Kong, Chris Ding and Heng Huang. Robust Nonnegative Matrix Factorization

using L21-norm, 20th ACM International Conference on Information and Knowledge Manage-

ment(CIKM’11), (137/917=15%)

13. Deguang Kong, Donghai Tian, Peng Liu and Dinghao Wu. Semantic Aware Attribution

Analysis of Remote Exploits, Wiley Journal of Security and Communication Networks, 2012.

14. Deguang Kong, Donghai Tian, Peng Liu and Dinghao Wu. SA3: Automatic Semantic Aware

Attribution Analysis of Remote Exploits, Proceedings of 7th International ICST Conference on

Security and Privacy in Communication Networks (SecureComm’11)(23/95= 24.2%)

15. Deguang Kong, Yoonchan Jhi, Tao Gong, Sencun Zhu, Peng Liu and Hongsheng Xi . SAS:

Semantic Aware Signature Generation for Polymorphic Worm Detection, International Journal

of Information Security(Invited submission as one of three best papers of SecureComm’10)

16. Deguang Kong, Yoonchan Jhi, Tao Gong, Sencun Zhu, Peng Liu and Hongsheng Xi . SAS:

Semantic Aware Signature Generation for Polymorphic Worm Detection, Proceedings of 6th

International ICST Conference on Security and Privacy in Communication Networks (Se-

cureComm’10)(28/112= 25%)

Research Assistant, Dept. of CSE, University of Texas at Arlington, Aug 2010 – May 2013

Research

• Mentor: Chris Ding

Experience

• Machine learning algorithms and applications in computer vision, text mining, and bioinfor-

matics: dimension reduction (e.g., semi definite positive Linear Discriminant Analysis, locally

linear embedding), feature selection (e.g., multi-label ReliefF, Fstatistic), semi-supervised learn-

ing (e.g., preferential random walk), clustering (e.g., Laplacian embedding, non-negative matrix

factorization), etc

Research Intern, Media analytics department, NEC Lab LA, America, May 2013 – Aug 2013

• Mentor: Ryohei Fujimaki

• Structure sparsity (e.g., lasso, group lasso, exclusive lasso) for feature learning and parameter

learning.

Research Intern, CCS3 (Information Science) group, Los Alamos National Lab, Jun 2012 – Dec

2012

• Mentor: Stephan Eidenbenz, Guanhua Yan

• Malware Phylogenetic classification using distance metric learning and maximum consistency

semi-supervised learning algorithms, based on attributes (e.g, register, memory, opcode, etc)

extracted from static analysis of malware.

Research Student, College of IST, Pennsylvania State University, Aug 2008 – Aug 2010

• Mentor: Peng Liu, Dinghao Wu

• Developed statistical algorithms (e.g., Hidden markov model, mixture of Markov model) for

exploit code signature generation, malware attribution analysis by extracting the exploit code

using static analysis.

Languages: C/C++/STL, Java, Python/Perl, Matlab, Assembly, SQL, HTML/XML,

Computer

Javascript, Shell script

Skills

Tools and IDE: Eclipse, MS Visual Studio, GDB, Netbeans, NS2, Powerbuilder, IDA pro, Hadoop

Other Projects

• Software Plagiarism detection

• Viral marketing in social networks.

Dr. Chris H.Q. Ding Dr. Ryohei Fujimaki Dr. Guanhua Yan

Referees

Professor, Dept. of Computer Science& Researcher, Media Analytic Depart- Research Staff member,

Engineering ment Science Group

University of Texas at Arlington NEC Lab, America Los Alamos National Lab

phone: 817-***-**** phone: 408-***-**** phone: 505-***-****

E-mail: *******@***.*** E-mail: *********@***-****.*** E-mail: *****@****.***

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