Benjamin Zi Hao Zhao (University of New South Wales and Data61 CSIRO), Hassan Jameel Asghar (Macquarie University and Data61 CSIRO), Mohamed Ali Kaafar (Macquarie University and Data61 CSIRO)

We assess the security of machine learning based biometric authentication systems against an attacker who submits uniform random inputs, either as feature vectors or raw inputs, in order to find an emph{accepting sample} of a target user. The average false positive rate (FPR) of the system, i.e., the rate at which an impostor is incorrectly accepted as the legitimate user, may be interpreted as a measure of the success probability of such an attack. However, we show that the success rate is often higher than the FPR. In particular, for one reconstructed biometric system with an average FPR of 0.03, the success rate was as high as 0.78. This has implications for the security of the system, as an attacker with only the knowledge of the length of the feature space can impersonate the user with less than 2 attempts on average. We provide detailed analysis of why the attack is successful, and validate our results using four different biometric modalities and four different machine learning classifiers. Finally, we propose mitigation techniques that render such attacks ineffective, with little to no effect on the accuracy of the system.

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CDN Judo: Breaking the CDN DoS Protection with Itself

Run Guo (Tsinghua University), Weizhong Li (Tsinghua University), Baojun Liu (Tsinghua University), Shuang Hao (University of Texas at Dallas), Jia Zhang (Tsinghua University), Haixin Duan (Tsinghua University), Kaiwen Sheng (Tsinghua University), Jianjun Chen (ICSI), Ying Liu (Tsinghua University)

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SPEECHMINER: A Framework for Investigating and Measuring Speculative Execution...

Yuan Xiao (The Ohio State University), Yinqian Zhang (The Ohio State University), Radu Teodorescu (The Ohio State University)

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Melting Pot of Origins: Compromising the Intermediary Web Services...

Takuya Watanabe (NTT), Eitaro Shioji (NTT), Mitsuaki Akiyama (NTT), Tatsuya Mori (Waseda University, NICT, and RIKEN AIP)

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OmegaLog: High-Fidelity Attack Investigation via Transparent Multi-layer Log Analysis

Wajih Ul Hassan (University of Illinois Urbana-Champaign), Mohammad A. Noureddine (University of Illinois Urbana-Champaign), Pubali Datta (University of Illinois Urbana-Champaign), Adam Bates (University of Illinois Urbana-Champaign)

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