@article {kazdagli2016emma,
	title = {EMMA: A New Platform to Evaluate Hardware-based Mobile Malware Analyses},
	journal = {arXiv preprint arXiv:1603.03086},
	year = {2016},
	abstract = {Hardware-based malware detectors (HMDs) are a key emerging technology to build trustworthy computing platforms, especially mobile platforms. Quantifying the efficacy of HMDs against malicious adversaries is thus an important problem. The challenge lies in that real-world malware typically adapts to defenses, evades being run in experimental settings, and hides behind benign applications. Thus, realizing the potential of HMDs as a line of defense {\textendash} that has a small and battery-efficient code base {\textendash} requires a rigorous foundation for evaluating HMDs. To this end, we introduce EMMA{\textemdash}a platform to evaluate the efficacy of HMDs for mobile platforms. EMMA deconstructs malware into atomic, orthogonal actions and introduces a systematic way of pitting different HMDs against a diverse subset of malware hidden inside benign applications. EMMA drives both malware and benign programs with real user-inputs to yield an HMD{\textquoteright}s effective operating range{\textemdash} i.e., the malware actions a particular HMD is capable of detecting. We show that small atomic actions, such as stealing a Contact or SMS, have surprisingly large hardware footprints, and use this insight to design HMD algorithms that are less intrusive than prior work and yet perform 24.7\% better. Finally, EMMA brings up a surprising new result{\textemdash} obfuscation techniques used by malware to evade static analyses makes them more detectable using HMDs.},
	author = {Kazdagli, Mikhail and Huang, Ling and Reddi, Vijay Janapa and Tiwari, Mohit}
}
