Applying machine learning classifiers to dynamic Android malware detection at scale

Amos, Brandon; Turner, Hamilton; White, Jules

doi:10.1109/iwcmc.2013.6583806

Cited by 150 publications

(89 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many of our classifiers exhibit higher values of both precision and recall than theirs. In 2013, Amos et al (2013) leveraged dynamic application profiling in their malware detector. The evaluation metrics of their 10-Fold experiment are slightly lowers than ours.…”

Section: Related Workmentioning

confidence: 99%

Empirical assessment of machine learning-based malware detectors for Android

et al. 2014

View full text Add to dashboard Cite

To address the issue of malware detection through large sets of applications, researchers have recently started to investigate the capabilities of machine-learning techniques for proposing effective approaches. So far, several promising results were recorded in the literature, many approaches being assessed with what we call in the lab validation scenarios. This paper revisits the purpose of malware detection to discuss whether such in the lab validation scenarios provide reliable indications on the performance of malware detectors in real-world settings, aka in the wild.To this end, we have devised several Machine Learning classifiers that rely on a set of features built from applications' CFGs. We use a sizeable dataset of over 50 000 Android applications collected from sources where state-of-the art approaches have selected their data. We show that, in the lab, our approach outperforms existing machine learning-based approaches. However, this high performance does not translate in high performance in the wild. The performance gap we observed-F-measures dropping from over 0.9 in the lab to below 0.1 in the wild -raises one important question: How do state-of-the-art approaches perform in the wild ?

show abstract

Section: Related Workmentioning

confidence: 99%

Empirical assessment of machine learning-based malware detectors for Android

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Using permissions and API calls as features, Wu et al [25] performed their experiments on a dataset of 1 500 goodware and 238 malware. In 2013, Amos et al [26] leveraged dynamic application profiling in their malware detector. Demme et al [27] also used dynamic application analysis to perform malware detection with a dataset of 210 goodware and 503 malware.…”

Section: Malware Detection and Assessmentsmentioning

confidence: 99%

Are Your Training Datasets Yet Relevant?

Allix

Bissyandé

Klein

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In this paper, we consider the relevance of timeline in the construction of datasets, to highlight its impact on the performance of a machine learning-based malware detection scheme. Typically, we show that simply picking a random set of known malware to train a malware detector, as it is done in many assessment scenarios from the literature, yields significantly biased results. In the process of assessing the extent of this impact through various experiments, we were also able to confirm a number of intuitive assumptions about Android malware. For instance, we discuss the existence of Android malware lineages and how they could impact the performance of malware detection in the wild.

show abstract

“…Another framework employing machine learning was introduced in 2013 [15]. This paper evaluates a number of existing classifiers that use machine learning, using a dataset containing thousands of real applications.…”

Section: Previous Workmentioning

confidence: 99%

Android Users Privacy Awareness Survey

Alani

2017

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

Abstract-Having a share of over 80% of the smartphone market, Android has become an important mobile operating system that is used by billions of users on daily basis. With the widespread use of smartphones in general, and Android in specific, privacy concerns grow with that expansion in the user base. With the millions of applications being downloaded by users daily, it is becoming increasingly difficult to differentiate between the good and the bad in terms of security and privacy. In this paper, we present the results of a survey conducted among 4027 Android users worldwide. This survey was conducted to measure the awareness of Android users regarding their privacy. The study measures the users' interaction with the permissions required by different applications they install. The results of the survey show apparent weakness in the awareness of Android users regarding the privacy of their data.

show abstract

Applying machine learning classifiers to dynamic Android malware detection at scale

Cited by 150 publications

References 16 publications

Empirical assessment of machine learning-based malware detectors for Android

Empirical assessment of machine learning-based malware detectors for Android

Are Your Training Datasets Yet Relevant?

Android Users Privacy Awareness Survey

Contact Info

Product

Resources

About