Illinois Science of Security (SoS) Lablet

Extensions of Network Reliability Analysis  slides | video
Hoang Hai Nguyen, Graduate Research Assistant, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
April 02, 2019, 3:00 p.m., CSL Auditorium (B02)

Abstract: Network reliability studies properties of networks subjected to random failures of their components. It has been widely adopted to modeling and analyzing real-world problems across different domains. Two practical situations that usually arise from such problems are (i) the correlation between component failures and (ii) the uncertainty in the failure probabilities, both of which are often overlooked from the literature. In this seminar, I will talk about recent developments in the theory of network reliability that aims at addressing both problems. For the first problem, we assign components with random variables while allowing the variables to be jointly distributed; for the second, we model component failure probabilities using Beta distributions. We study properties of the resulting reliability polynomials as polynomials of Beta random variables and demonstrate the use of model on two real-world systems.

Classifying Malware Represented as Control Flow Graphs using Deep Graph Convolution Neural Network  slides | video
Jiaqi Yan, Graduate Research Assistant, Illinois Institute of Technology
April 30, 2019, 3:00 p.m., CSL Auditorium (B02)

Abstract: Malware have been one of the biggest cyber threats in the digital world for a long time. Existing machine learning-based malware classification methods rely on handcrafted features extracted from raw binary files or disassembled code. The diversity of such features created has made it hard to build generic malware classification systems that work effectively across different operational environments. To strike a balance between generality and performance, we explore new machine learning techniques to classify malware programs represented as their control flow graphs (CFGs). To overcome the drawbacks of existing malware analysis methods using inefficient and non-adaptive graph matching techniques, in this work, we build a new system that uses deep graph convolutional neural network to embed structural information inherent in CFGs for effective yet efficient malware classification. We use two large independent datasets that contain more than 20K malware samples to evaluate our proposed system and the experimental results show that it can classify CFG-represented malware programs with performance comparable to those of the state-of-the-art methods applied on handcrafted malware features.