Quantifying Cache Side-Channel Leakage by Refining Set-Based Abstractions
We propose an improved abstract interpretation based method for quantifying cache side-channel leakage by addressing two key components of precision loss in existing set-based cache abstractions.
Our method targets two key sources of imprecision: (1) imprecision in the abstract transfer function used to update the abstract cache state when interpreting a memory access and (2) imprecision due to the incompleteness of the set-based domain.
At the center of our method are two key improvements: (1) the introduction of a new transfer function for updating the abstract cache state which carefully leverages information in the abstract state to prevent the spurious aging of memory blocks and (2) and a refinement of the set-based domain based on the finite powerset construction.
We show that both the new abstract transformer and the domain refinement enjoy certain optimality properties.
We have implemented the method and compared it against the state-of-the-art technique on a suite of benchmark programs implementing both sorting algorithms and cryptographic algorithms.
The experimental results show that our method is effective in improving the precision of cache side-channel leakage quantification.
Tue 1 JulDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:15 - 17:39 | |||
16:15 21mTalk | Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories Technical Papers Tianyu Chen Microsoft Research Asia, Zeyu Wang Huawei Cloud Computing Technologies Co., Ltd., Lin Li Huawei Cloud Computing Technologies Co., Ltd., Ding Li Peking University, Zongyang Li Peking University, Xiaoning Chang Huawei Cloud Computing Technologies Co., Ltd., Pan Bian Huawei Technologies CO., LTD., China, Guangtai Liang Huawei Cloud Computing Technologies, Qianxiang Wang Huawei Technologies Co., Ltd, Tao Xie Peking University | ||
16:36 21mTalk | Quantifying Cache Side-Channel Leakage by Refining Set-Based Abstractions Technical Papers | ||
16:57 21mTalk | Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification Technical Papers Francisco Costa University of Brasília, Brazil, Ismael Medeiros Computer Science Department / University of Brasília, Leandro Oliveira Computer Science Department / University of Brasília, João Clássio Computer Science Department / University of Brasília, Rodrigo Bonifácio UNB, Krishna Narasimhan F1RE, Mira Mezini TU Darmstadt; hessian.AI; National Research Center for Applied Cybersecurity ATHENE, Márcio Ribeiro Federal University of Alagoas, Brazil DOI Pre-print | ||
17:18 21mTalk | Ensuring Convergence and Invariants Without Coordination Technical Papers Dina Borrego NOVA LINCS, FCT, Universidade NOVA de Lisboa, Carla Ferreira NOVA University Lisbon, Elisa Gonzalez Boix Vrije Universiteit Brussel, Nuno Preguica Universidade Nova de Lisboa |