@misc {18391,
	title = {A General Practitioner or a Specialist for Your Infected Smartphone?},
	journal = {36th IEEE Symposium on Security and Privacy },
	year = {2015},
	month = {05/2015},
	publisher = {IEEE Computer Society Technical Committee on Security and Privacy},
	address = {San Jose, CA, USA},
	abstract = {With explosive growth in the number of mobile devices, the mobile malware is rapidly spreading as well, and the number of encountered malware families is increasing. Existing solutions, which are mainly based on one malware detector running on the phone or in the cloud, are no longer effective. Main problem lies in the fact that it might be impossible to create a unique mobile malware detector that would be able to detect different malware families with high accuracy, being at the same time lightweight enough not to drain battery quickly and fast enough to give results of detection promptly.

The proposed approach to mobile malware detection is analogous to general practitioner versus specialist approach to dealing with a medical problem. Similarly to a general practitioner that, based on indicative symptoms identifies potential illnesses and sends the patient to an appropriate specialist, our detection system distinguishes among symptoms representing different malware families and, once the symptoms are detected, it triggers specific analyses. A system monitoring application operates in the same way as a general practitioner. It is able to distinguish between different symptoms and trigger appropriate detection mechanisms. As an analogy to different specialists, an ensemble of detectors, each of which specifically trained for a particular malware family, is used. The main challenge of the approach is to define representative symptoms of different malware families and train detectors accordingly to them. The main goal of the poster is to foster discussion on the most representative symptoms of different malware families and to discuss initial results in this area obtained by using Malware Genome project dataset.},
	keywords = {Android, feature selection, malware detection, PCA, security},
	url = {http://www.ieee-security.org/TC/SP2015/posters/paper_16.pdf},
	author = {Milosevic, Jelena and Ferrante, Alberto and Malek, Miroslaw}
}
@conference {127.TaMiFe10,
	title = {Gradual Adaptation of Security for Sensor Networks},
	booktitle = {IEEE WoWMoM 2010: Proceedings of the IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks},
	year = {2010},
	month = {June 13},
	address = {Montreal, Canada},
	abstract = {Wireless sensor networks are composed by nodes with stringent constraints on resources. In particular, a very limited power consumption is often a key factor for this kind of devices. In this paper we describe a method for security self-adaptation tailed for wireless sensor networks. This method allows devices to adapt security of applications gradually with the goal of guaranteeing the maximum possible level of security while satisfying system constraints. A case study is also presented to show how the method works in a real wireless sensor network.},
	keywords = {graceful degradation, gradual adaptation, security, sensors networks},
	doi = {http://dx.doi.org/10.1109/WOWMOM.2010.5534903},
	author = {Taddeo, Antonio Vincenzo and Micconi, Laura and Ferrante, Alberto}
}
