@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 {18379,
	title = {Proactive Failure Management in Smart Grids for Improved Resilience (A Methodology for Failure Prediction and Mitigation)},
	booktitle = {IEEE GLOBECOM SmartGrid Resilience (SGR) Workshop},
	year = {2015},
	month = {12/2015},
	address = {San Diego, CA, USA},
	abstract = {A gradual move in the electric power industry towards Smart Grids brings several challenges to the system operation such as preserving its resilience and ensuring security. As the system complexity grows and a number of failures increases, the need for grid management paradigm shift from reactive to proactive is apparent and can be realized by employing advanced monitoring instruments, data analytics and prediction methods. In order to improve resilience of the Smart Grid and to contribute to efficient system operation, we present a blueprint of a comprehensive methodology for proactive failure management that may also be applied to manage other types of disturbances and undesirable changes. The methodology is composed of three main steps: (i) continuous monitoring of the most indicative features, (ii) prediction of failures and (iii) their mitigation. The approach is complementary to the existing ones that are mainly based on fast detection and localization of grid disturbances, and reactive corrective actions.},
	keywords = {Failure Prediction, Proactive Management, Resilience, security, smart grid, Synchrophasor},
	author = {Kaitovi{\'c}, Igor and Lukovi{\'c}, Slobodan and Malek, Miroslaw}
}
