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.
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.
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 |