GPUGuard

Abstract

Graphics processing units (GPUs) are moving towards supporting concurrent kernel execution where multiple kernels may be co-executed on the same GPU and even on the same streaming multiprocessor (SM) core. While concurrent kernel execution improves hardware resource utilization, it opens up vulnerabilities to covert-channel and side-channel attacks. These attacks exploit information leakage across kernels that results from contention on shared resources; they have been shown to be a dangerous threat on CPUs, and are starting to be demonstrated on GPUs. The unique micro-architectural features of GPUs, such as specialized cache structures and massive parallel thread support, create opportunities for GPU-speciic channels to be formed. In this paper, we propose GPUGuard, a decision tree based detection and a hierarchical defense framework that can reliably close the covert channels. Our results show that GPUGuard can detect contention with 100% sensitivity and a small (8.5%) false positive rate. The timing channels are mitigated through Tangram, a GPU-speciic contention channel elimination scheme, with only 8% to 23% overhead when there is an attack and zero performance overhead when no attacks are detected. Compared to temporal partitioning, GPUGuard is 69%-96% faster in various architectures even when active, showing that it is possible to gain substantial performance from executing concurrent kernels on a single SM while securing GPUs against these attacks. CCS CONCEPTS • Computer systems organization → Single instruction, multiple data; • Security and privacy → Artiicial immune systems; Side-channel analysis and countermeasures.

Systematic Analysis on the Effectiveness of Covert Channel Data Transmission

“…The covert channel-using table to transmit secure data that allows two entities to agree on a secret key using encryption to prevent an attacker from getting any information. Qiumin Xu et al [20] a Trojan application can cause resource contention by altering the contents of a cache set to encode '1' and leaving the resource idle to encode '0'. On the other hand, the Spy application visits the cache and measures its access time in order to decode the transferred bit.…”