Malware detection using machine and deep learning algorithms for computer devices
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Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Malicious software, sometimes known as malware, poses a serious threat to computer security.
Due to the quantity and diversity of variations, traditional security measures are inadequate, and
there are a lot of places on the internet has malware infection such as Computer Virus, internet
worms, and trojan horses are examples of malware. Despite the fact that malware multiformity
and obstruction create it difficult to discover files, during runtime, dynamic malware-binary
analysis is performed. Provides way to identify & protect towards the attack posed with malware
software in this paper, we offer an approach for automated malware behavior analysis using
machine learning. Using such technology, for a short time ago there is a new undiscovered
malware families for the classes (classification) that have been found, as well as new malware
classes with a similar behavior, have been uncovered (clustering) may be dynamically recognized.
Both clustering and classification imply incremental analysis, which may monitor the daily
activities of thousands of malwares and analyses their behavior. The innovative research ensures
that new malware types are properly identified and discriminated against while greatly reducing
the overhead of existing test procedures.
Açıklama
Anahtar Kelimeler
Computer Virus Detection, Machine Learning, Classification Behaviour, Clustering Behaviour, Malware Detection
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Altaiy, Mohammed Thair Abdulsattar. (2022). Malware detection using machine and deep learning algorithms for computer devices. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
