▬ The 2nd Inha-UCF GRL (Global Research Lab) Group Joint Workshop

  • Organizers: Prof. DaeHun Nyang and Prof. Aziz Mohaisen
  • Date: 8th Feb 2018, Thursday, AM 10:00 ~ PM 18:30
  • University of Central Florida
  • Programs
    • TBA

▬ The 1st Inha-UB GRL (Global Research Lab) Group Joint Workshop

  • Organizers: DaeHun Nyang and Aziz Mohaisen
  • Date: 12th May 2017, Friday, AM 10:00 ~ PM 05:00
  • Place: 60th Anniversary Hall #108, Inha University, Incheon, South Korea (Google Maps)
  • Programs
    10:00 ~ 11:00 - MyungKeun Yoon (Kookmin University)
    • Title: Packet-level De-duplication
    • Abstract:┬áPacket recording or capturing is one of the most useful tools for network forensics and surveillance. Since a storage system is of a limited size, de-duplication can be used to save disk space. We present a new scalable de-duplication engine for packet recording that can eliminate redundant contents over multiple packets. Unlike previous work, our proposed scheme is designed for packet-level de-duplication to support any kinds of network from the current Internet to emerging networks. We also present a new fast chunking method and a new indexing scheme that enable multiple engine instances to execute in parallel. We implement the de-duplication engine, and experimental results show that our proposed scheme can remove up to 65 percent of the packet contents in a real campus network. We also confirm that its throughput scalably increases with the number of CPU cores, which means that the proposed scheme can be implemented in a wide range of computing devices from small home gateways to high-end servers.
    11:00 ~ 12:00 - JoongHeon Kim (ChungAng University)
    • Title: GPU-Driven Security
    • Abstract: This presentation is dedicated to the introduction to general purpose GPU (GPGPU) computing for artificial neural networks and GPU-specific security issues. For the GPGPU-based artificial neural networks, the Lyapunov-optimization based time-average energy efficiency maximization algorithm under queue-stabilization will be presented for GPGPU-capable parallelized artificial neural network computation. In addition, security-related industry and academia issues in GPGPU-based data-intensive computation will be discussed.
    12:00 ~ 01:30 - Lunch
    01:30 ~ 02:30 - Seungwon Shin (KAIST)
    • Title: A Framework for Scalable Anomaly Detection in Software-Defined Networks
    • Abstract: Recently, Software-Defined Networking (SDN) technology has been developed and used in many places. SDN provides a centralized management mechanism for the network, helping to easily monitor and manage the state of the network in the control plane. Therefore, researches and products that detect network attacks such as DDoS using these functions are proposed. In this presentation, we will look at a brief trend of these studies and how to detect DDoS attacks using SDN.
    02:30 ~ 03:30 - Kui Ren (University at Buffalo)
    • Title: Exploring New Attacking Surfaces for Smart Manufacturing Systems: A Case Study of 3D Printing
    • Abstract: Additive manufacturing, also known as 3D printing, has been increasingly applied to fabricate highly intellectual-property (IP) sensitive products. However, the related IP protection issues in 3D printers are still largely underexplored. On the other hand, smartphones are equipped with rich onboard sensors and have been applied to pervasive mobile surveillance in many applications. These facts raise one critical question: is it possible that smartphones access the side-channel signals of 3D printer and then hack the IP information? In this talk, we answer this by performing an end-to-end study on exploring smartphone-based side-channel attacks against 3D printers. Specifically, we formulate the problem of the IP side-channel attack in 3D printing. Then, we investigate the possible acoustic and magnetic side-channel attacks using built-in sensors of the smartphone. Moreover, we explore a magnetic-enhanced side-channel attack model to accurately deduce the vital directional operations of 3D printer. Experimental results show that by exploiting the side-channel signals collected by smartphones, we can successfully reconstruct the physical prints and their G-code with Mean Tendency Error of 5.87% on regular designs and 9.67% on complex designs, respectively.
    03:30 ~ 04:00 - Coffee Break
    04:00 ~ 05:00 - DaeHun Nyang (Inha University) and Aziz Mohaisen (University at Buffalo): GRL group talk session and progress report