Melez Özelliklerin Modellenmesi ile Zararlı Yazılım Tespiti

Öz

Zararlı yazılımlar sahip oldukları yeteneklerden ötürü bilgisayar ve sistemlere büyük tehlike oluşturmaktadır. Etkin tespit sistemlerinin gelişmesinden aynı şekilde etkilenerek daha tehlikeli ve donanımlı hale gelmektedirler. Otomatik bir tespit sistemi geliştirmek için, zararlı yazılımlar iyi analiz edilmeli ve gelişim meyilleri doğru tespit edilmelidir. Zararlı yazılımların çalıştığı bilgisayarda yarattığı etkiler ve kod yapısı ayrıntılı incelenmeli ve öyle önlem alınmalıdır. Bu çalışmada önerilen tespit sistemi, zararlı yazılımın hem davranış hem kod yapısı bilgisini kullanarak Markov zinciri yöntemi ile istatistiksel bir anlam çıkarmaktadır. Daha sonra derin öğrenme teknikleri ile temellendirilmiş model melez veri kaynağı ile eğitilmiş ve tespit ortamı hazırlanmıştır. Yaptığımız testler sonucunda önerilen tespit yöntemi %96,8’lik doğruluk göstermiştir.

Anahtar Kelimeler

• Markov Zinciri,Derin Öğrenme,Dinamik Analiz,Durağan Analiz

Malware Detection by Using Markov Models of Hybrid Features

Öz

Malware poses a great danger to computers and systems due to their capabilities. They are also affected by the development of effective detection systems and become more dangerous and equipped. In order to develop an automated detection system, malware must be well analyzed, and inclination of their evolution should be accurately understood. The runtime effects of malicious software on the computer and code structure should be examined in detail and precautions should be taken. The detection system proposed in this study makes a statistical meaning with Markov chain method using both behavior and code structure knowledge of malware. Then the model based on deep learning techniques is trained with the hybrid data source and detection environment is prepared. As a result of the tests we performed, the accuracy of the detection method was 96.8%.

Anahtar Kelimeler

• Markov Chain,Malware,Deep learning,Convolutional Network,Dynamic Analysis,Static Analysis

Kaynakça

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