About Me
Hi! My name is DongInn Kim. I am a Ph.D. student at the Computer Science Department of Indiana University-Bloomington under the guidance of Prof. L Jean Camp. I work for the IU CSI CTF (Capture The Flag) team and I am also working on the Open Source Cluster Application Resources (OSCAR) as a core developer. My research interests include software engineering, clustering computing, and systems security.
- IU CSI CTF: Capture The Flag team of Indiana University
Research Interest
My current research focuses on the reverse engineering for the security of IoT devices and building an intelligent dynamic analysis tool on the top of existing static/dynamic analysis security tools.
Current Work
- detect malicious domains from the CH registry database: planning to apply machine learning
- binary analysis to investigate any possible penetrations in Intel SGX: working with SGX reading group
- practice the CTF exercises / problems with some various reverse engineering tools (e.g. radare2, unicorn, capstone, keystone, …)
Past Work (Summer in 2017)
- renovating the security toolbar, which is an extension of Firefox to make web browsing more secure.
- work on the “CUTS CertProbe and CertWarehouse” for collecting and compiling the Public key certificates. <= [Previous work: PDF]
Publications
- DongInn Kim, Jeffrey M. Squyres, and Andrew Lumsdaine: The Introduction of the OSCAR Database API (ODA) => PDF
- DongInn Kim, Jeffrey M. Squyres, and Andrew Lumsdaine: Revamping the OSCAR database: A Flexible Approach to Cluster Configuration Data Management. => PDF
Technical Skills
Machine learning (ML) models that I have implmented with R:
| Name | What does it do |
|---|---|
| K-Means | Unsupervised, non-parametric ML to classify data with the given K value |
| E-M | Unsupervised, parametric ML to find maximum likelihood or maximum a posteriori |
| Linear Regression | Supervised, parametric ML to find the linear model between given data sets (X,…, Y) |
| Logistic Regression | Supervised, parametric ML to find the categorical model between dependent variables |
| KNN | Unsupervised, non-parametric ML for classification and regression |
| Naive Bayes | Semi-supervised, parametric ML for the probabilistic classifier with the assumption of IID |
Here is the list of the tools that I am currently learning:
| Name | What does it do |
|---|---|
| Frida | Dynamic instrumentation toolkit for the binary analysis |
| Scapy | Python version of tshark and more |
| Radare2 | Reverse engineering framework |
| Unicorn | CPU emulator (U) |
| Capstone | Disassembly framework |
| Keystone | Assembler framework |
| Pwndbg | GDB plug-in to make debugging with GDB much easier |
Research Colleagues
- L Jean Camp: Professor, School of Informatics and Computing, Indiana University
- Andrew Lumsdaine: UW-PNNL Distinguished Faculty Fellow and Affiliate Professor of Computer Science, University of Washington
Copyright ©2017 The Trustees of