Analysis of Bayesian classification‐based approaches for Android malware detection

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

doi:10.1049/iet-ifs.2013.0095

Cited by 141 publications

(79 citation statements)

References 24 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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

Self Cite

158

View full text Add to dashboard Cite

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.

show abstract

Section: Results Comparison With Existing Workmentioning

confidence: 99%

High accuracy android malware detection using ensemble learning

Yerima

Sezer

Muttik³

2015

IET Information Security

Self Cite

158

View full text Add to dashboard Cite

show abstract

“…Şekil 3. APK Analyser tarafından yapılan otomatikleştirilmiş tersine mühendislik ve veri madenciliği [11] Şekil 4. Kötücül yazılım tespit çatısı [12] Yerima ve arkadaşları statik özellikler kullanılarak paralel makine öğrenmesi sınıflandırıcıları vasıtasıyla kötücül yazılımın erken tespiti için bir metot önermişlerdir.…”

Section: Mobi̇l Kötücül Yazilim (Mobile Malware)unclassified

Android Kötücül Yazilim Tespi̇ti̇ Yaklaşimlari

Doğru¹,

Dinçer²

2017

Uluslararasi Bi̇lgi̇ Güvenli̇ği̇ Mühendi̇sli̇ği̇ Dergi̇si̇

View full text Add to dashboard Cite

ÖZETMobil cihazlar, mobil uygulamaların işlevselliklerinin gelişmesiyle birlikte hem iş hem günlük hayatta vazgeçilmez cihazlar olmaya başlamıştır. Kendi cihazını getir iş modeli ile beraber bu cihazlar iş ve kamu kurumlarının ağlarına bağlanarak, beraberinde kötücül yazılımların tüm risklerini organizasyona taşımaktadır. Kötücül davranış, bilgiye ve cihaza yetkisiz erişim dolayısıyla hem kurum hem kişiye karşı ciddi ölçüde tehdit oluşturmaya başlamıştır. Android, açık kaynak çekirdek politikası sebebiyle bu tehditlere çok daha fazla açık bir platformdur. Bu kötücül yazılımları tespit edip önlem almak için tespit mekanizmaları geliştirilmekte, buna karşılık olarak kötücül yazılım geliştiricileri dönüşüm gibi güçlü tekniklerle bu tespit tekniklerinden kaçmayı amaçlamaktadır. Bu çalışmada, Android kötücül yazılım tespiti yaklaşımları sunan farklı çalışmalar incelenmiştir ve bu çalışmalar çeşitli ölçütler bakımından karşılaştırılmıştır.Anahtar Kelimeler: Android malware, kötücül yazılım, kötücül yazılım tespiti, dinamik yaklaşım, statik yaklaşım, imza tabanlı yaklaşım, hibrit yaklaşım ANDROID MALWARE DETECTION APPROACHES ABSTRACTWith the development of the functionality of mobile applications, mobile devices have become essential in both business and daily life. Bring your own device business model permits the employees to connect their own devices to the networks of business and public institutions and carry all the risks of malware together with the organization. Malicious behavior via unprivileged access to information and device has been a serious threat to both organizations and individuals. Android has open source kernel policy, so it is more vulnerable to these threats. Detection approaches have been developed for the mitigation and detection of malware by time. In response to this, malware developers aim to hide from these detection techniques by developing complicated techniques like obfuscation. In this study, various studies presenting Android malware detection approaches have been reviewed and compared in terms of various criteria.

show abstract

“…The authors use Mutual Information to assert important factors to be used as independent features in Bayesian algorithm. The authors later extend their work to improve the accuracy with detection by performing paralleled analysis [15] [16] 17]. Our work, on the other hand, targets 3 more machine learning models to have better view of each model, as well as realize their own advantages and application.…”

Section: Related Workmentioning

confidence: 99%

Practical Application of Machine Learning Algorithms to Android Malware Analysis

2017

Nov. 20-22, 2017 Kuala Lumpur (Malaysia) SETH-17, CAENS-17, BDMCE-17, FEWCMB-17, ANPMS-17, ELBIS-17 &Amp; LHSRS-17

View full text Add to dashboard Cite

show abstract

Analysis of Bayesian classification‐based approaches for Android malware detection

Cited by 141 publications

References 24 publications

High accuracy android malware detection using ensemble learning

High accuracy android malware detection using ensemble learning

Android Kötücül Yazilim Tespi̇ti̇ Yaklaşimlari

Practical Application of Machine Learning Algorithms to Android Malware Analysis

Contact Info

Product

Resources

About