Ijaz, M., Durad, M.H. and Ismail, M. (2019) Static and Dynamic Malware Analysis Using Machine Learning. 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, 8-12 January 2019, 687-691.
https://doi.org/10.1109/IBCAST.2019.8667136

has been cited by the following article:

• TITLE: Malware Detection Using Deep Learning

  AUTHORS: Achi Harrisson Thiziers, Koné Tiémoman, N’guessan Behou Gérard, Traoré Tiémoko Qouddouss Kabir

  KEYWORDS: Neural Network, ANNs, Malicious Code, Malware Analysis, Artificial Intelligence

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.13 No.12, December 29, 2023

  ABSTRACT: Malware represents a real threat to information systems, because of the damage it causes. This threat is growing today, as these programs take on more complex forms. This means they escape traditional malware detection methods. Hence the need for artificial intelligence, more specifically Deep Learning, which could detect malware more effectively. In this article, we’ve proposed a model for malware detection using artificial neural networks. Our approach used data from the characteristics of machines, particularly computers, to train our Deep Learning algorithm. This model demonstrated an accuracy of around 83% in predicting the presence of malware on a machine. Thus, the use of artificial neural networks for malware detection has shown his ability to assimilate complex, non-linear patterns from data.