Tommy Chin
Rochester Institute of Technology
17 Papers
68 Citations
Tommy Chin is an academic researcher from Rochester Institute of Technology. The author has contributed to research in topics: Software-defined networking & Computer science. The author has an hindex of 8, co-authored 17 publications.
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Papers
Selective Packet Inspection to Detect DoS Flooding Using Software Defined Networking (SDN)
Tommy Chin,Xenia Mountrouidou,Xiangyang Li,Kaiqi Xiong +3 more
- 29 Jun 2015
TL;DR: A novel attack detection approach that coordinates monitors distributed over a network and controllers centralized on an SDN Open Virtual Switch (OVS), selectively inspecting network packets on demand, able to quickly issue an alert against potential threats followed by careful verification for high accuracy, while balancing the workload on the OVS is discussed.
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A Machine Learning Framework for Domain Generation Algorithm-Based Malware Detection
TL;DR: This paper collects real-time threat data from the real-life traffic over a one-year period and builds a deep neural network model to enhance the proposed machine learning framework by handling the huge dataset it gradually collected.
Phishlimiter: A Phishing Detection and Mitigation Approach Using Software-Defined Networking
TL;DR: This paper proposes a new technique for deep packet inspection (DPI) and then leverage it with software-defined networking (SDN) to identify phishing activities through e-mail and web-based communication and shows that PhishLimiter provides an effective and efficient solution to deter malicious activities.
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An SDN-supported collaborative approach for DDoS flooding detection and containment
Tommy Chin,Xenia Mountrouidou,Xiangyang Li,Kaiqi Xiong +3 more
- 01 Oct 2015
TL;DR: An innovative approach that coordinates distributed network traffic Monitors and attack Correlators supported by Open Virtual Switches that is able to not only quickly raise an alert against potential threats, but also follow it up with careful verification to reduce false alarms is presented.
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A Machine Learning Framework for Studying Domain Generation Algorithm (DGA)-Based Malware
Tommy Chin,Kaiqi Xiong,Chengbin Hu,Yi Li +3 more
- 08 Aug 2018
TL;DR: A machine learning framework for identifying and clustering domain names to circumvent threats from a DGA is proposed and achieves accuracies of 95.14% and 92.45% for the first-level classification and second-level clustering, respectively.
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