Using probabilistic generative models for ranking risks of Android apps

Peng, Hao; Gates, Chris; Sarma, Bhaskar; Li, Ninghui; Qi, Yuan; Potharaju, Rahul; Nita-Rotaru, Cristina; Molloy, Ian

doi:10.1145/2382196.2382224

Cited by 303 publications

(228 citation statements)

References 20 publications

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“…Table 8 shows how the results in this paper measures against the best results of [1], [20], [22], [23], [24], [33], and [41] respectively (using the available metrics). These comparative results show that our approach in this paper outperforms previous similar efforts.…”

Section: Results Comparison With Existing Workmentioning

confidence: 99%

High accuracy android malware detection using ensemble learning

Yerima

Sezer

Muttik³

2015

IET Information Security

158

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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: Results Comparison With Existing Workmentioning

confidence: 99%

High accuracy android malware detection using ensemble learning

Yerima

Sezer

Muttik³

2015

IET Information Security

158

View full text Add to dashboard Cite

show abstract

“…With regards to state-of-the-art literature tackled in this work, a significant number of Machine Learning approaches for malware detection [6,29,[36][37][38][39] have been presented to the research community. The feature set that we use in this paper was evaluated in [23] and achieved better performance than those approaches.…”

Section: Related Workmentioning

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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“…Thus, any app can be classified even if coming from unknown marketplaces. Another app classification system is presented in [32], where apps are classified in comparison with formerly analyzed apps. The methodology exploits probabilistic generative models to analyze apps on different criteria including permissions.…”

Section: Related Workmentioning

confidence: 99%

Risk analysis of Android applications: A user-centric solution

Dini

Martinelli

Matteucci

et al. 2018

Future Generation Computer Systems

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Android applications (apps) pose many risks to their users, e.g., by including code that may threaten user privacy or system integrity. Most of the current security countermeasures for detecting dangerous apps show some weaknesses, mainly related to users' understanding and acceptance. Hence, users would benefit from an effective but simple technique that indicates whether an app is safe or risky to be installed. In this paper, we present MAETROID (Multi-criteria App Evaluator of TRust for AndrOID), a framework to evaluate the trustworthiness of Android apps, i.e., the amount of risk they pose to users, e.g., in terms of confidentiality and integrity. MAETROID performs a multi-criteria analysis of an app at deploy-time and returns a single easyto-understand evaluation of the app's risk level (i.e., Trusted, Medium Risk, and High Risk), aimed at driving the user decision on whether or not installing a new app. The criteria include the set of requested permissions and a set of metadata retrieved from the marketplace, denoting the app quality and popularity. We have tested MAETROID on a set of 11,000 apps both coming from Google Play and from a database of known malicious apps. The results show a good accuracy in both identifying the malicious apps and in terms of false positive rate.

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Using probabilistic generative models for ranking risks of Android apps

Cited by 303 publications

References 20 publications

High accuracy android malware detection using ensemble learning

High accuracy android malware detection using ensemble learning

Are Your Training Datasets Yet Relevant?

Risk analysis of Android applications: A user-centric solution

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