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This work introduces the CHEx86 processor architecture for securing applications, including legacy binaries, against a wide array of security exploits that target temporal and spatial memory safety vulnerabilities such as out-of-bounds accesses, use-after-free, double-free, and uninitialized reads, by instrumenting the code at the microcode-level, completely under-the-hood, with only limited access to source-level symbol information. In addition, this work presents a novel scheme for speculatively tracking pointer arithmetic and pointer movement, including the detection of pointer aliases in memory, at the machine code-level using a configurable set of automatically constructed rules. This architecture outperforms the address sanitizer, a state-of-the-art software-based mitigation by 59%, while eliminating porting, deployment, and verification costs that are invariably associated with recompilation. 

This paper presents Packet Chasing, an attack on the network that does not require access to the network, and works regardless of the privilege level of the process receiving the packets. A spy process can easily probe and discover the exact cache location of each buffer used by the network driver. Even more useful, it can discover the exact sequence in which those buffers are used to receive packets. This then enables packet frequency and packet sizes to be monitored through cache side channels. This allows both covert channels between a sender and a remote spy with no access to the network, as well as direct attacks that can identify, among other things, the web page access patterns of a victim on the network. In addition to identifying the potential attack, this work proposes a software-based short-term mitigation as well as a light-weight, adaptive, cache partitioning mitigation that blocks the interference of I/O and CPU requests in the last-level cache. 

Heterogeneous architectures have become increasingly common. From co-packaging small and large cores, to GPUs alongside CPUs, to general-purpose heterogeneous-ISA architectures with cores implementing different ISAs. As diversity of execution cores grows, predictive models become of paramount importance for scheduling and resource allocation. In this paper, we investigate the capabilities of performance predictors in a heterogeneous-ISA setting, as well as the predictors’ effects on scheduler quality. We follow an unbiased feature selection methodology to identify the optimal set of features for this task, instead of pre-selecting features before training. Finally, we incorporate our findings in ML-based schedulers and evaluate their sensitivity to the underlying system’s level of heterogeneity. We show our schedulers to perform within 2-11% of an oracular scheduler across a variety of underlying heterogeneous-ISA multicore systems without modification.