A New Android Malware Detection Approach Using Bayesian Classification

Yerima, Suleiman Y.; Sezer, Sakir; McWilliams, Gavin; Muttik, Igor

doi:10.1109/aina.2013.88

Cited by 173 publications

(96 citation statements)

References 19 publications

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“…They present experimental results based on 238 Android samples from 34 families together with 1500 benign apps. Our work differs from [20]- [25], as our static analysis driven approach leverages ensemble learning driven by a more extensive feature set comprising 179 feature attributes (from API calls, commands, and permissions). Additionally, our study utilizes a larger malware dataset than the previous works.…”

Section: Related Workmentioning

confidence: 99%

“…Investigation of machine learning based detection for Android platform is gaining attention recently with the growing availability of malware samples. Related work that apply machine learning with static analysis to detect Android malware can be found in [20] - [24] for instance.…”

Section: Related Workmentioning

confidence: 99%

“…In [20], Bayesian classification was applied to categorize apps into 'benign' or 'suspicious' using 58 code-based feature attributes. The training and classification employed 1000 Android malware samples from 49 families and 1000 benign applications.…”

Section: Related Workmentioning

confidence: 99%

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High accuracy android malware detection using ensemble learning

Yerima

Sezer

Muttik³

2015

IET Information Security

157

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Abstract-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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

High accuracy android malware detection using ensemble learning

Yerima

Sezer

Muttik³

2015

IET Information Security

157

View full text Add to dashboard Cite

show abstract

“…Demme et al [27] also used dynamic application analysis to perform malware detection with a dataset of 210 goodware and 503 malware. Yerima et al [28] built malware classifiers based on API calls, external program execution and permissions. Their dataset consists of 1 000 goodware and 1 000 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

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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.

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“…Many of our in the lab classifiers achieved higher performance than their best classifier. Yerima et al (2013) built malware classifiers based on API calls, external program execution and permissions. Their dataset consists in 1 000 goodware and 1 000 malware.…”

Section: Related Workmentioning

confidence: 99%

Empirical assessment of machine learning-based malware detectors for Android

et al. 2014

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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 ?

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A New Android Malware Detection Approach Using Bayesian Classification

Cited by 173 publications

References 19 publications

High accuracy android malware detection using ensemble learning

High accuracy android malware detection using ensemble learning

Are Your Training Datasets Yet Relevant?

Empirical assessment of machine learning-based malware detectors for Android

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