Assistant ProfessorWayne State University
College of Engineering
Department of Computer Science
Detroit, MI 48202
Office: 14101.30 5057 Woodward Ave
Email: r.jang at wayne.edu
I'm a tenure-track assistant professor in the department of computer science at Wayne State University. I joined Wayne State University in Fall 2020. Before joining Wayne, I was a Ph.D student in the department of computer science at the University of Central Florida. To date, I published as a lead author several peer-reviewed research papers, including papers in top-tier conferences and premier journals, such as IEEE ICDCS, IEEE INFOCOM, and IEEE TMC, etc. I won the Best-in-session Presentation Award (SDN II) at IEEE INFOCOM (2017), the Outstanding Young Researcher Award from KISA (2018).
Recent Research Areas and Interests
- Network Traffic Measurement: I'm interested in designing a cost-efficient data structure and algorithm (i.e., sketch) to realize the fast and accurate traffic measurement in a high-speed and resource-constraint network environment [INFOCOM'17][WiSec'17][ICDCS'19].
- Wireless Security: I'm interested in studying the wireless secuirty inlcuding rogue access point detection [ICDCS'19], SDN-based WLAN intrusion detection system [INFOCOM'17], and channel interference [TMC'20].
- Sampling Algorithm: In this project, we introduce a new concept of per-flow systematic sampling, aiming to provide the same sampling rate across all flows. In addition, we provide a concrete sampling method called SketchFlow, which approximates the idea of the per-flow systematic sampling using a sketch saturation event. We demonstrate SketchFlow’s performance in terms of accuracy, sampling rate, and overhead using real-world datasets. Experimental results show that SketchFlow outperforms SRS (i.e., sFlow) and the non-linear sampling method while requiring a small CPU overhead to measure high-speed traffic in real-time [INFOCOM'20].
- Privacy: In this work, we conduct in-depth analyses of two state-of-the-art WF defense approaches of The Onion Router (Tor). Then, based on our new insights, we propose a novel defense mechanism using a per-burst injection technique, called Deep Fingerprinting Defender (DFD), which is designed to break the inherent patterns preserved in Tor user's traces by carefully injecting dummy packets within every burst [INFOCOM'20].
Experience and Education2020-, Assistant Professor at Wayne State University, CS
2020, Ph.D., CS, University of Central Florida, USA, Advisor: Prof. David Mohaisen
2020, Ph.D., CS, INHA University, South Korea, Advisor: Prof. DaeHun Nyang (Now, Ewha Womans University)
2019, Visiting Research Assistant, USC ISI
ServicesPC member: IEEE MSN 2021, Publicity chair: IEEE ICDCS 2021, Web chair: IEEE/ACM CoNEXT 2019.
- Jeman Park, Rhongho Jang, Manar Mohaisen, David Mohaisen. "A Large-Scale Behavioral Analysis of the Open DNS Resolvers on the Internet," IEEE Transactions on Networking (ToN), 2021
- Ahmed Abusnaina, Mohammed Abuhamad, Hisham Alasmary, Afsah Anwar, Rhongho Jang, Saeed Salem, DaeHun Nyang, David Mohaisen, "Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification," IEEE Transactions on Dependable and Secure Computing (TDSC), 2021.
- Changhun Jung, Jinchun Choi, Rhongho Jang, David Mohaisen, DaeHun Nyang, "A Network-independent Tool-based Usable Authentication System for Internet of Things Devices," Computer and Security (COSE), 2021.
- Rhongho Jang, Changhun Jung, David Mohaisen, Kyunghee Lee, DaeHun Nyang, "A One-Page Text Entry Method Optimized for Rectangle Smartwatches", IEEE Transactions on Mobile Computing (TMC), 2021.
- Rhongho Jang, "Towards Scalable Network Traffic Measurement With Sketches", Dissertation, UCF, Summer 2020. [PDF]
- Rhongho Jang, Daehong Min, Seongkwang Moon, David Mohaisen, and DaeHun Nyang, “SketchFlow: Per-Flow Systematic Sampling Using Sketch Saturation Event”, in Proceedings of the 39th IEEE International Conference on Computer Communications, INFOCOM 2020.
- Ahmed Abusnaina†, Rhongho Jang†, Aminollah Khormali, DeaHun Nyang, and David Mohaisen, “Deep Fingerprinting Defender: Adversarial Learning-based Approach to Defend Against Website Fingerprinting”, in Proceedings of the 39th IEEE International Conference on Computer Communications, INFOCOM 2020. † equivalent contributors
- Hisham Alasmary†, Ahmed Abusnaina†, Rhongho Jang†, Mohammed Abuhamad, Afsah Anwar, DaeHun Nyang, and David Mohaisen “Soteria: Detecting Adversarial Examples in Control Flow Graph-based Malware Classifiers”, in Proceedings of 40th IEEE International Conference on Distributed Computing Systems, ICDCS 2020.
- Rhongho Jang, Jeonil Kang, Aziz Mohaisen, and DaeHun Nyang, “Catch Me If You Can: Rogue Access Point Detection Using Intentional Channel Interference”, IEEE Transactions on Mobile Computing (TMC), 2020.
- Rhongho Jang, Seongkwang Moon, Youngtae Noh, Aziz Mohaisen, and DaeHun Nyang, “InstaMeasure: Instant Per-flow Detection Using Large In-DRAM Working Set of Active Flows”, in Proceedings of 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019.
- Rhongho Jang, Jeonil Kang, Aziz Mohaisen, and DaeHun Nyang, “Rogue Access Point Detector Using Characteristics of Channel Overlapping in 802.11n”, in Proceedings of 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017.
- Rhongho Jang, DongGyu Cho, Youngtae Noh, and DaeHun Nyang, “RFlow+: An SDN-based WLAN Monitoring and Management Framework”, in Proceedings of the 36th IEEE International Conference on Computer Communications, INFOCOM 2017.
- CSC 5290 - Cyber Security Practice (fall 2020, 2021): This course explores board security topics in the areas of network and operating systems. In particular, this course focus on providing hands-on experience leveraging various security tools, aiming to help students understand real-world security threats. It will cover both offensive and defense methods under a laboratory environment. Students are expected to finish lab assignments using real-world malware, exploits, and defense tools.
- CSC 5991 - Network Programmability and Applications (winter 2021): This class covers the foundations and advanced topics in network programmability. By the end of the course, students are expected to know 1) the fundamental knowledge of network programmability, 2) router architecture and working flow, 3) state-of-the-art applications, and 4) opportunities and challenges.