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.