Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Coupled Effects of a Perturbation in a Complex Structure Observed with SDO/AIA, SDO/HMI and ROSA/HARDcam

We study properties of intensity fluctuations in NOAA Active Region 11250 observed on 13 July 2011 starting at UT 13:32. Included are data obtained in the EUV bands of the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory (SDO/AIA) as well as nearly simultaneous observations of the chromosphere made, at much higher spatial and temporal resolution, with the Rapid Oscillations in the Solar Atmosphere (ROSA) and Hydrogen-Alpha Rapid Dynamics camera (HARDcam) systems at the Dunn Solar Telescope. A complex structure seen in both the ROSA/HARDcam and SDO data sets comprises a system of loops extending outward from near the boundary of the leading sunspot umbra. It is visible in the ROSA Ca II K and HARDcam Hα images, as well as the SDO 304 Å, 171 Å and 193 Å channels, and it thus couples the chromosphere, transition region and corona. In the ground-based images the loop structure is 4.1 Mm long. Some 17.5 Mm, can be traced in the SDO/AIA data. The chromospheric emissions observed by ROSA and HARDcam appear to occupy the inner, and apparently cooler and lower, quarter of the loop. We compare the intensity fluctuations of two points within the structure. From alignment with SDO/HMI images we identify a point "A" near the loop structure, which sits directly above a bipolar magnetic feature in the photosphere. Point "B" is characteristic of locations within the loops that are visible in both the ROSA/HARDcam and the SDO/AIA data. The intensity traces for point A are quiet during the first part of the data string. At time ~ 19 min they suddenly begin a series of impulsive brightenings. In the 171 Å and 193 Å coronal lines the brightenings are localized impulses in time, but in the transition region line at 304 Å they are more extended in time. The intensity traces in the 304 Å line for point B shows a quasi-periodic signal that changes properties at about 19 min. The wavelet power spectra are characterized by two periodicities. A 6.7 min period extends from the beginning of the series until about 25 minutes, and another signal with period ~3 min starts at about 20 min. The 193 Å power spectrum has a characteristic period of 5 min, before the 20 min transition and a 2.5 min periodicity afterward. In the case of HARDcam Hα data a localized 4 min periodicity can be found until about 7 min, followed by a quiet regime. After ~20 min a 2.3 min periodicity appears. Interestingly a coronal loop visible in the 94 Å line that is centrally located in the AR, running from the leading umbra to the following polarity, at about time 20 min undergoes a strong brightening beginning at the same moment all along 15 Mm of its length. The fact that these different signals all experience a clear-cut change at time about 20 min suggests an underlying organizing mechanism. Given that point A has a direct connection to the photospheric magnetic bipole, we conjecture that the whole extended structure is connected in a complex manner to the underlying magnetic field. The periodicities in these features may favor the wave nature rather than upflows and interpretations will be discussed.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Android Malware Detection: an Eigenspace Analysis Approach

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.

Towards automated Android app collusion detection

Android OS supports multiple communication methods between apps. This opens the possibility to carry out threats in a collaborative fashion, c.f. the Soundcomber example from 2011. In this paper we provide a concise definition of collusion and report on a number of automated detection approaches, developed in co-operation with Intel Security.

N-opcode analysis for android malware classification and categorization

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.

Dynalog: an automated dynamic analysis framework for characterizing android applications

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.

Failed filament eruption inside a coronal mass ejection in active region 11121

Aims. We study the formation and evolution of a failed filament eruption observed in NOAA active region 11121 near the southeast
limb on November 6, 2010.
Methods. We used a time series of SDO/AIA 304, 171, 131, 193, 335, and 94 Å images, SDO/HMI magnetograms, as well as ROSA
and ISOON Hα images to study the erupting active region.
Results. We identify coronal loop arcades associated with a quadrupolar magnetic configuration, and show that the expansion and
cancellation of the central loop arcade system over the filament is followed by the eruption of the filament. The erupting filament
reveals a clear helical twist and develops the same sign of writhe in the form of inverse γ-shape.
Conclusions. The observations support the “magnetic breakout” process in which the eruption is triggered by quadrupolar reconnection
in the corona. We propose that the formation mechanism of the inverse γ-shape flux rope is the magnetohydrodynamic helical
kink instability. The eruption has failed because of the large-scale, closed, overlying magnetic loop arcade that encloses the active
region

A review of ADP processing software

Data processing is an essential part of Acoustic Doppler Profiler (ADP) surveys, which have become the standard tool in assessing flow characteristics at tidal power development sites. In most cases, further processing beyond the capabilities of the manufacturer provided software tools is required. These additional tasks are often implemented by every user in mathematical toolboxes like MATLAB, Octave or Python. This requires the transfer of the data from one system to another and thus increases the possibility of errors. The application of dedicated tools for visualisation of flow or geographic data is also often beneficial and a wide range of tools are freely available, though again problems arise from the necessity of transferring the data. Furthermore, almost exclusively PCs are supported directly by the ADP manufacturers, whereas small computing solutions like tablet computers, often running Android or Linux operating systems, seem better suited for online monitoring or data acquisition in field conditions. While many manufacturers offer support for developers, any solution is limited to a single device of a single manufacturer. A common data format for all ADP data would allow development of applications and quicker distribution of new post processing methodologies across the industry.