Publication
Midas: Systematic Kernel TOCTTOU Protection
Related publications (47)
Secure Interface Design Leveraging Hardware/Software Support
Computer systems rely heavily on abstraction to manage the exponential growth of complexity across hardware and software. Due to practical considerations of compatibility between components of these complex systems across generations, developers have favou ...
EPFL2024End-to-end kernel learning via generative random Fourier features
Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...
ELSEVIER SCI LTD2023An Open-Hardware Coarse-Grained Reconfigurable Array for Edge Computing
David Atienza Alonso, Giovanni Ansaloni, José Angel Miranda Calero, Rubén Rodríguez Álvarez, Juan Pablo Sapriza Araujo, Benoît Walter Denkinger, Ruben Rodriguez
In this work, we propose an open-hardware low-power coarse-grained reconfigurable array connected to a lightweight microcontroller and enclosed in an application mapping framework. The latter provides complete support to configure kernels in the reconfigur ...
2023ACTOR: Action-Guided Kernel Fuzzing
Fuzzing reliably and efficiently finds bugs in software, including operating system kernels. In general, higher code coverage leads to the discovery of more bugs. This is why most existing kernel fuzzers adopt strategies to generate a series of inputs that ...
Berkeley2023A RISC-V Extension to Minimize Privileges of Enclave Runtimes
Edouard Bugnion, Neelu Shivprakash Kalani
In confidential computing, the view of the system software is Manichean: the host operating system is untrusted and the TEE runtime system is fully trusted. However, the runtime system is often as complex as a full operating system, and thus is not free fr ...
ACM2023Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data
Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi
Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...
JMLR-JOURNAL MACHINE LEARNING RESEARCH2022NrOS: Effective Replication and Sharing in an Operating System
Sanidhya Kashyap, Ankit Bhardwaj
Writing a correct operating system kernel is notoriously hard. Kernel code requires manual memory management and type-unsafe code and must efficiently handle complex, asynchronous events. In addition, increasing CPU core counts further complicate kernel de ...
USENIX ASSOC2021Multikernel Regression with Sparsity Constraint
Michaël Unser, Shayan Aziznejad
In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised l ...
2021USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation
The Universal Serial Bus (USB) connects external devices to a host. This interface exposes the OS kernels and device drivers to attacks by malicious devices. Unfortunately, kernels and drivers were developed under a security model that implicitly trusts co ...
USENIX ASSOC2020Snap-On User-Space Manager for Dynamically Reconfigurable System-on-Chips
Paolo Ienne, Andrea Guerrieri, Sahand Kashani
Due to increased embedded processing requirements, modern SoCs are becoming heterogeneous computing platforms by combining traditional processing units with custom reconfigurable hardware accelerators (HAs) on an FPGA fabric. However, efficiently managing ...
2019