A Large-Scale Empirical Study on Industrial Fake Apps

Tang, Chongbin; Chen, Sen; Fan, Lingling; Xu, Li-Hua; Liu, Yang; Tang, Zhushou; Li, Dou

doi:10.1109/icse-seip.2019.00028

Cited by 38 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [30] proposed a clustering approach on app names to detect potential fake apps. Tang et al [28] have characterized over 150K fake apps that have same package names or app names with popular apps. Kywe et al [21] and Li et al [22] proposed technique to detect fake apps based on the external features of apps, e.g., icons, app names.…”

Section: Fake Apps/app Clonesmentioning

confidence: 99%

Characterizing Cryptocurrency Exchange Scams

Xia¹,

Zhang²,

Ji³

et al. 2020

Preprint

View full text Add to dashboard Cite

As the indispensable trading platforms of the ecosystem, hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets. While, it also attracts the attentions of attackers. A number of scam attacks were reported targeting cryptocurrency exchanges, leading to a huge mount of financial loss. However, no previous work in our research community has systematically studied this problem. In this paper, we make the first effort to identify and characterize the cryptocurrency exchange scams. We first identify over 1,500 scam domains and over 300 fake apps, by collecting existing reports and using typosquatting generation techniques. Then we investigate the relationship between them, and identify 94 scam domain families and 30 fake app families. We further characterize the impacts of such scams, and reveal that these scams have incurred financial loss of 520k US dollars at least. We further observe that the fake apps have been sneaked to major app markets (including Google Play) to infect unsuspicious users. Our findings demonstrate the urgency to identify and prevent cryptocurrency exchange scams. To facilitate future research, we have publicly released all the identified scam domains and fake apps to the community.

show abstract

Section: Fake Apps/app Clonesmentioning

confidence: 99%

Characterizing Cryptocurrency Exchange Scams

Xia¹,

Zhang²,

Ji³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Prior academic work identifies emerging trends in the Android malware ecosystem [31], [48], [70], [71], [82]. Suarez-Tangil et al [71] conducts a behavioral analysis of 1.2M Android malware samples collected from malware feeds, over a period of eight years (from 2010-2017).…”

Section: Related Workmentioning

confidence: 99%

How Did That Get In My Phone? Unwanted App Distribution on Android Devices

Kotzias¹,

Caballero²,

Bilge³

2020

Preprint

View full text Add to dashboard Cite

Android is the most popular operating system with billions of active devices. Unfortunately, its popularity and openness makes it attractive for unwanted apps, i.e., malware and potentially unwanted programs (PUP). In Android, app installations typically happen via the official and alternative markets, but also via other smaller and less understood alternative distribution vectors such as Web downloads, pay-per-install (PPI) services, backup restoration, bloatware, and IM tools. This work performs a thorough investigation on unwanted app distribution by quantifying and comparing distribution through different vectors. At the core of our measurements are reputation logs of a large security vendor, which include 7.9M apps observed in 12M devices between June and September 2019. As a first step, we measure that between 10% and 24% of users devices encounter at least one unwanted app, and compare the prevalence of malware and PUP. An analysis of the who-installs-who relationships between installers and child apps reveals that the Play market is the main app distribution vector, responsible for 87% of all installs and 67% of unwanted app installs, but it also has the best defenses against unwanted apps. Alternative markets distribute instead 5.7% of all apps, but over 10% of unwanted apps. Bloatware is also a significant unwanted app distribution vector with 6% of those installs. And, backup restoration is an unintentional distribution vector that may even allow unwanted apps to survive users' phone replacement. We estimate unwanted app distribution via PPI to be smaller than on Windows. Finally, we observe that Web downloads are rare, but provide a riskier proposition even compared to alternative markets.

show abstract

“…It is undeniable that these smartphones have yielded many benefits for society, allowing millions of people to stay connected through the Internet. Consequently, it has also drawn the attention of malware authors to disseminate their malware on the application markets (e.g., Google Play Store) [3]. However, unlike the App Store for Apple iOS, the protocol for uploading an application on the Android application market is not that stringent.…”

Section: Introductionmentioning

confidence: 99%

SeqMobile: A Sequence Based Efficient Android Malware Detection System Using RNN on Mobile Devices

Feng¹,

Lim²,

Chen³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

With the proliferation of Android malware, the demand for an effective and efficient malware detection system is on the rise. The existing device-end learning based solutions tend to extract limited syntax features, such as permissions and API calls, to meet a certain time constraint of mobile devices. However, unlike sequence-based features, syntax features lack the semantics which can represent the potential malicious behaviors and further result in more robust model with high accuracy for malware detection.In this paper, we propose an efficient Android malware detection system, named SeqMobile, which adopts behavior-based sequence features and leverages customized deep neural networks on mobile devices instead of the server end. Different from the traditional sequence-based approaches on server end, to meet the performance demand on mobile devices, SeqMobile accepts three effective performance optimization methods to reduce the time of feature extraction and prediction. To evaluate the effectiveness and efficiency of our system, we conduct experiments from the following aspects 1) the detection accuracy of different recurrent neural networks (RNN); 2) the feature extraction performance on different mobile devices, and 3) the detection accuracy and prediction time cost of different sequence lengths. The results unveil that SeqMobile can effectively detect malware with high accuracy. Moreover, our performance optimization methods have proven to improve the performance of training and prediction by at least twofold. Additionally, to discover the potential performance optimization from the state-of-the-art TensorFlow model optimization toolkit for our sequence-based approach, we also provide an evaluation on the toolkit, which can serve as a guidance for other systems leveraging on sequence-based learning approach. Overall, we conclude that our sequence-based approach, together with our performance optimization methods, enable us to efficiently detect malware under the performance demands of mobile devices.

show abstract

A Large-Scale Empirical Study on Industrial Fake Apps

Cited by 38 publications

References 21 publications

Characterizing Cryptocurrency Exchange Scams

Characterizing Cryptocurrency Exchange Scams

How Did That Get In My Phone? Unwanted App Distribution on Android Devices

SeqMobile: A Sequence Based Efficient Android Malware Detection System Using RNN on Mobile Devices

Contact Info

Product

Resources

About