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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Android Malware Application Detection using Multi-layer Perceptron

Çok Katmanlı Algılayıcı Kullanarak Android Kötü Amaçlı Yazılım Uygulama Tespiti

How to cite: Altan G, Paşalıoğlu F. Android malware application detection using multi-layer perceptron. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2022; 5(2): 95-99. DOI: 10.54856/jiswa.202212221

Full Text: PDF, in English.

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Title: Android Malware Application Detection using Multi-layer Perceptron

Abstract: Cyber-attacks are one of the most critical problems that seriously threaten society. Whereas there are various presentations and ways of carrying out cyber-attacks, numerous mechanisms and techniques exist to defend applications. Many malware creators have chosen the Android operating system as a target due to its popularity. Thousands of new malware samples, aiming to infect new devices daily, are trying to circumvent the security measures implemented by Android app stores. This study experiments with a multi-layer perceptron model for Android malware detection. This proposed system is based on static analysis techniques on Android. We analyzed popular machine learning algorithms with a total number of 129013 applications (5560 malicious and 123453 harmless software). We achieved higher malware-detection rates of 97.60% in the iterations.

Keywords: Classification; Multi-layer perceptron; Malware detection; Cyber-attack; Cyber-security; Android


Başlık: Çok Katmanlı Algılayıcı Kullanarak Android Kötü Amaçlı Yazılım Uygulama Tespiti

Özet: Siber saldırılar, toplumu ciddi şekilde tehdit eden son zamanların en kritik sorunlarından biridir. Siber saldırıları gerçekleştirmenin çeşitli sunumları ve yolları olsa da, uygulamaları savunmak için çok sayıda mekanizma ve yöntem mevcuttur. Birçok kötü amaçlı yazılımcı, popülaritesi nedeniyle Android işletim sistemini hedef olarak seçmektedir. Her gün yeni cihazlara erişmeyi amaçlayan binlerce yeni kötü amaçlı yazılım örneği, Android uygulama mağazalarının uyguladığı güvenlik önlemlerini atlatmaya çalışmaktadır. Bu çalışma, Android tabanlı çalışan kötü amaçlı yazılım tespiti için çok katmanlı algılayıcı modeli ile deneyler gerçekleştirmektedir. Önerilen bu sistem Android üzerinde kullanılan statik analiz tekniklerine dayanmaktadır. Popüler makine öğrenimi algoritmalarını toplam 129013 uygulama (5560 kötü amaçlı ve 123453 zararsız yazılım) ile analiz edilmiştir. İterasyonlarda %97,60'dan daha yüksek kötü amaçlı yazılım algılama oranları elde edilmiştir.

Anahtar kelimeler: Sınıflama; Çok katmanlı algılayıcılar; Kötü amaçlı yazılım tespiti; Siber atak; Siber güvenlik; Android


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