SPECWANDS: An Efficient Priority-based Scheduler Against Speculation Contention Attacks
Bowen Tang, Chenggang Wu, Pen-Chung Yew, Yinqian Zhang, Mengyao Xie,, Yuanming Lai, Yan Kang, Wei Wang, Qiang Wei, Zhe Wang

TL;DR
This paper introduces SPECWANDS, a priority-based scheduler that mitigates volatile-type speculative execution attacks by preventing resource contention-based covert channels, achieving effective security with minimal performance overhead.
Contribution
It proposes a novel scheduling scheme, SPECWANDS, with three priority policies to defend against contention-based attacks while maintaining high performance.
Findings
Significant reduction in covert channel transmission.
Lower overhead compared to existing mitigation schemes.
Maintains most benefits of speculative execution.
Abstract
Transient Execution Attacks (TEAs) have gradually become a major security threat to modern high-performance processors. They exploit the vulnerability of speculative execution to illegally access private data, and transmit them through timing-based covert channels. While new vulnerabilities are discovered continuously, the covert channels can be categorised to two types: 1) Persistent Type, in which covert channels are based on the layout changes of buffering, e.g. through caches or TLBs; 2) Volatile Type, in which covert channels are based on the contention of sharing resources, e.g. through execution units or issuing ports. The defenses against the persistent-type covert channels have been well addressed, while those for the volatile-type are still rather inadequate. Existing mitigation schemes for the volatile type such as Speculative Compression and Time-Division-Multiplexing will…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSecurity and Verification in Computing · Cryptography and Data Security · Adversarial Robustness in Machine Learning
