Biblio

The threats from side-channel attacks to modern processors has become a serious problem, especially under the enhancement of the microarchitecture characteristics with multicore and resource sharing. Therefore, the research and measurement of the vulnerability of the side-channel attack of the system is of great significance for computer designers. Most of the current evaluation methods proposed by researchers are only for typical cache side-channel attacks. In this paper, we propose a method to measure systems' vulnerability to side-channel attacks caused by port contention called pcSVF. We collected the traces of the victim and attacker and computed the correlation coefficient between them, thus we can measure the vulnerability of the system against side-channel attack. Then we analyzed the effectiveness of the method through the results under different system defense schemes.

Security has always been one of the main concerns in the field of computer architecture and cloud computing. Cache-based side-channel attacks pose a threat to almost all existing architectures and cloud computing. Especially in the public cloud, the cache is shared among multiple tenants, and cache attacks can make good use of this to extract information. Cache side-channel attacks are a problem to be solved for security, in which how to accurately detect cache side-channel attacks has been a research hotspot. Because the cache side-channel attack does not require the attacker to physically contact the target device and does not need additional devices to obtain the side channel information, the cache-side channel attack is efficient and hidden, which poses a great threat to the security of cryptographic algorithms. Based on the AES algorithm, this paper uses hardware performance counters to obtain the features of different cache events under Flush + Reload, Prime + Probe, and Flush + Flush attacks. Firstly, the random forest algorithm is used to filter the cache features, and then the support vector machine algorithm is used to model the system. Finally, high detection accuracy is achieved under different system loads. The detection accuracy of the system is 99.92% when there is no load, the detection accuracy is 99.85% under the average load, and the detection accuracy under full load is 96.57%.