EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detection avoidance techniques in order to hide their malicious activities from analysis tools. These include a wide range of anti-emulator techniques, where the malware programs attempt to hide their malicious activities by detecting the emulator. For this reason, countermeasures against anti-emulation are becoming increasingly important in Android malware detection. Analysis and detection based on real devices can alleviate the problems of anti-emulation as well as improve the effectiveness of dynamic analysis. Hence, in this paper we present an investigation of machine learning based malware detection using dynamic analysis on real devices. A tool is implemented to automatically extract dynamic features from Android phones and through several experiments, a comparative analysis of emulator based vs. device based detection by means of several machine learning algorithms is undertaken. Our study shows that several features could be extracted more effectively from the on-device dynamic analysis compared to emulators. It was also found that approximately 24% more apps were successfully analysed on the phone. Furthermore, all of the studied machine learning based detection performed better when applied to features extracted from the on-device dynamic analysis

Global distribution and interannual variations of mesospheric and lower thermospheric neutral wind diurnal tide: 2. Nonmigrating tide

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94716/1/jgra18976.pd

The geospace response to variable inputs from the lower atmosphere:a review of the progress made by Task Group 4 of CAWSES-II

The advent of new satellite missions, ground-based instrumentation networks, and the development of whole atmosphere models over the past decade resulted in a paradigm shift in understanding the variability of geospace, that is, the region of the atmosphere between the stratosphere and several thousand kilometers above ground where atmosphere-ionosphere-magnetosphere interactions occur. It has now been realized that conditions in geospace are linked strongly to terrestrial weather and climate below, contradicting previous textbook knowledge that the space weather of Earth's near space environment is driven by energy injections at high latitudes connected with magnetosphere-ionosphere coupling and solar radiation variation at extreme ultraviolet wavelengths alone. The primary mechanism through which energy and momentum are transferred from the lower atmosphere is through the generation, propagation, and dissipation of atmospheric waves over a wide range of spatial and temporal scales including electrodynamic coupling through dynamo processes and plasma bubble seeding. The main task of Task Group 4 of SCOSTEP's CAWSES-II program, 2009 to 2013, was to study the geospace response to waves generated by meteorological events, their interaction with the mean flow, and their impact on the ionosphere and their relation to competing thermospheric disturbances generated by energy inputs from above, such as auroral processes at high latitudes. This paper reviews the progress made during the CAWSES-II time period, emphasizing the role of gravity waves, planetary waves and tides, and their ionospheric impacts. Specific campaign contributions from Task Group 4 are highlighted, and future research directions are discussed

Global distribution and interannual variations of mesospheric and lower thermospheric neutral wind diurnal tide: 1. Migrating tide

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95689/1/jgra18975.pd

An empirical model of the Earth's horizontal wind fields: HWM07

The new Horizontal Wind Model (HWM07) provides a statistical representation of the horizontal wind fields of the Earth's atmosphere from the ground to the exosphere (0-500 km). It represents over 50 years of satellite, rocket, and ground-based wind measurements via a compact Fortran 90 subroutine. The computer model is a function of geographic location, altitude, day of the year, solar local time, and geomagnetic activity. It includes representations of the zonal mean circulation, stationary planetary waves, migrating tides, and the seasonal modulation thereof. HWM07 is composed of two components, a quiet time component for the background state described in this paper and a geomagnetic storm time component (DWM07) described in a companion paper

TIMED Doppler Interferometer: Overview and recent results

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94617/1/jgra18230.pd