Binghui Wang (Iowa State University), Jinyuan Jia (Iowa State University), Neil Zhenqiang Gong (Iowa State University)

Many security and privacy problems can be modeled as a graph classification problem, where nodes in the graph are classified by collective classification simultaneously. State- of-the-art collective classification methods for such graph-based security and privacy analytics follow the following paradigm: assign weights to edges of the graph, iteratively propagate reputation scores of nodes among the weighted graph, and use the final reputation scores to classify nodes in the graph. The key challenge is to assign edge weights such that an edge has a large weight if the two corresponding nodes have the same label, and a small weight otherwise. Although collective classification has been studied and applied for security and privacy problems for more than a decade, how to address this challenge is still an open question. For instance, most existing methods simply set a constant weight to all edges.

In this work, we propose a novel collective classification framework to address this long-standing challenge. We first formulate learning edge weights as an optimization problem, which quantifies the goals about the final reputation scores that we aim to achieve. However, it is computationally hard to solve the optimization problem because the final reputation scores depend on the edge weights in a very complex way. To address the computational challenge, we propose to jointly learn the edge weights and propagate the reputation scores, which is essentially an approximate solution to the optimization problem. We compare our framework with state-of-the-art methods for graph-based security and privacy analytics using four large-scale real-world datasets from various application scenarios such as Sybil detection in social networks, fake review detection in Yelp, and attribute inference attacks. Our results demonstrate that our framework achieves higher accuracies than state-of-the-art methods with an acceptable computational overhead.

View More Papers

YODA: Enabling computationally intensive contracts on blockchains with Byzantine...

Sourav Das (Department of Computer Science and Engineering, Indian Institute of Technology Delhi), Vinay Joseph Ribeiro (Department of Computer Science and Engineering, Indian Institute of Technology Delhi), Abhijeet Anand (Department of Computer Science and Engineering, Indian Institute of Technology Delhi)

Read More

Understanding Open Ports in Android Applications: Discovery, Diagnosis, and...

Daoyuan Wu (Singapore Management University), Debin Gao (Singapore Management University), Rocky K. C. Chang (The Hong Kong Polytechnic University), En He (China Electronic Technology Cyber Security Co., Ltd.), Eric K. T. Cheng (The Hong Kong Polytechnic University), Robert H. Deng (Singapore Management University)

Read More

Robust Performance Metrics for Authentication Systems

Shridatt Sugrim (Rutgers University), Can Liu (Rutgers University), Meghan McLean (Rutgers University), Janne Lindqvist (Rutgers University)

Read More

DIAT: Data Integrity Attestation for Resilient Collaboration of Autonomous...

Tigist Abera (Technische Universität Darmstadt), Raad Bahmani (Technische Universität Darmstadt), Ferdinand Brasser (Technische Universität Darmstadt), Ahmad Ibrahim (Technische Universität Darmstadt), Ahmad-Reza Sadeghi (Technische Universität Darmstadt), Matthias Schunter (Intel Labs)

Read More