About Me

Hi! My name is DongInn Kim. I am a PhD candidate in Computer Science at Indiana University. 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. My PhD thesis under the supervision of Prof. Jean Camp is based on developing an intrusion detection system by fingerprinting the accessing edge services and IoT devices with machine learning processes of the collected features. 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.

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

Past Work

Publications

  1. Vafa Andalibi, Jayati Dev, DongInn Kim, Eliot Lear, and Jean Camp. Making Access Control Easy in IoT. In IFIP International Symposium on Human Aspects of Information Security & Assurance, June 2021.
  2. Vafa Andalibi, Eliot Lear, DongInn Kim, and Jean Camp. On the Analysis of MUD-Files’ Interactions, Conflicts, and Configuration Requirements Before Deployment. In 5th EAI International Conference on Safety and Security in Internet of Things, SaSeIoT, May 2021. Springer.
  3. Shakthidhar Gopavaram, Jayati Dev, Marthie Grobler, DongInn Kim, Sanchari Das, and L Jean Camp. Cross-National Study on Phishing Resilience. In Proceedings of the Workshop on Usable Security and Privacy (USEC), May 2021. => PDF
  4. DongInn Kim, Vafa Andalibi, and L Jean Camp. Protecting IoT Devices through Localized Detection of BGP Hijacks for Individual Things. In SafeThings 2021, Oakland, May 2021. IEEE Workshop on the Internet of Safe Things. => PDF
  5. DongInn Kim, Vafa Andalibi, and L Jean Camp. Fingerprinting Edge and Cloud Services in IoT. In Systematic Approaches to Digital Forensic Engineering, City University of New York (CUNY), New York City, May 2020. IEEE Computer Society. => PDF
  6. Vafa Andalibi, DongInn Kim, and L. Jean Camp. Throwing MUD into the FOG: Defending IoT and Fog by expanding MUD to Fog network. In 2nd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 19), Renton, WA, July 2019. USENIX Association. => PDF
  7. DongInn Kim, Jeffrey M. Squyres, and Andrew Lumsdaine: The Introduction of the OSCAR Database API (ODA) => PDF
  8. 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