Android Platformunda Kötücül Yazılım Tespiti: Literatür İncelemesi

Year 2020, , 65 - 76, 31.01.2020

Gökçer Peynirci Mete Eminağaoğlu

https://doi.org/10.17671/gazibtd.524408

Abstract

Kötücül yazılımların tespiti Windows, Mac, Linux gibi geniş kitlelerin kullandığı işletim sistemleri de dahil olmak üzere, platformdan bağımsız bir biçimde karşımıza çıkmaktadır. Android işletim sistemi, akıllı telefonlarda pazar payı liderliği ve açık kaynaklı yapısıyla kötücül yazılımların birincil hedefi haline gelmiştir. Bunun sonucu olarak da bilişim suçlularının günümüzde öncelikli hedefi olan bu platform, aynı zamanda yeni güvenlik yöntemleri ve teknikleri tasarlayıp yeterliliklerini ölçmek isteyen araştırmacılar için de en öncelikli ortamlardan birini oluşturmaktadır. Olabildiğince fazla sayıda ve güncel çalışmaların incelenmesinin amaçlandığı bu literatür taramasında, Android işletim sistemini hedef alan kötücül yazılımların, yapay öğrenme teknikleri ve yaklaşımları kullanılarak tespit edilmesi konuları kapsanmıştır. Bu alandaki bilimsel araştırmalar mevcut literatürden derlenerek ilgili çalışmalarda kullanılan veya kullanılması önerilen tasarım, yöntem ve uygulamalar özet bir biçimde anlatılmıştır.

Keywords

Android, kötücül yazılım tespiti, yapay öğrenme, veri madenciliği, öznitelik seçimi

Malware Detection on Android Platform: A Literature Review

Abstract

The problem of malware detection, irrespective of the platform of choice, affects nearly all operating systems, including the ones with large user bases such as Windows, Mac, and Linux. A significantly larger market share in the smartphone market compared to even its greatest rival and its open source architecture has made Android operating system the prime target for malware-related threats and cyber-attacks. Therefore, Android became the primary platform for designing and measuring the effectiveness of new approaches and methodologies for malware detection. This literature review focuses on the topic of detection of malware on Android platform by utilizing machine learning techniques and approaches. An extensive collection of the scientific studies on the given topic was collected and the design, the methodology and the real-world applications proposed or implemented by them are described in a short and concise manner.

Keywords

Android, malware detection, machine learning, data mining, feature selection

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