Milos Doroslovacki

2papers

2 Papers

CRFeb 13, 2019
Towards a Better Indicator for Cache Timing Channels

Fan Yao, Hongyu Fang, Milos Doroslovacki et al.

Recent studies highlighting the vulnerability of computer architecture to information leakage attacks have been a cause of significant concern. Among the various classes of microarchitectural attacks, cache timing channels are especially worrisome since they have the potential to compromise users' private data at high bit rates. Prior works have demonstrated the use of cache miss patterns to detect these attacks. We find that cache miss traces can be easily spoofed and thus they may not be able to identify smarter adversaries. In this work, we show that \emph{cache occupancy}, which records the number of cache blocks owned by a specific process, can be leveraged as a stronger indicator for the presence of cache timing channels. We observe that the modulation of cache access latency in timing channels can be recognized through analyzing pairwise cache occupancy patterns. Our experimental results show that cache occupancy patterns cannot be easily obfuscated even by advanced adversaries that successfully evade cache miss-based detection.

ITMay 17, 2017
Supervised Machine Learning for Signals Having RRC Shaped Pulses

Mohammad Bari, Hussain Taher, Syed Saad Sherazi et al.

Classification performances of the supervised machine learning techniques such as support vector machines, neural networks and logistic regression are compared for modulation recognition purposes. The simple and robust features are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals having root-raised-cosine shaped pulses are simulated in extreme noisy conditions having joint impurities of block fading, lack of symbol and sampling synchronization, carrier offset, and additive white Gaussian noise. The features are based on sample mean and sample variance of the imaginary part of the product of two consecutive complex signal values.