Abstract
The use of malicious software (malware) as an instrument for carrying out different criminal activities both organised and non-organised have become the major threat faced by today’s world of connectivity. Frequency and complexity of such cyberattacks makes it difficult for computer antivirus companies to efficiently handle the high value of the new malwares released using traditional approaches that depends mainly on signature. As a result, machine learning approaches are now the best home for this problem, and have demonstrate a great success. One of the challenges now is finding a method that is reasonably fast, and can practically adopted. In this work, we try some of the best machine learning models Convolutional Neural Network (CNN) in one of the new computer generation language namely Julia.
Minjibir, J (2021). Malware Classification Into Families Based On File Contents And Characteristics. Afribary. Retrieved from https://tracking.afribary.com/works/malware-classification-into-families-based-on-file-contents-and-characteristics
Minjibir, Jabir "Malware Classification Into Families Based On File Contents And Characteristics" Afribary. Afribary, 13 Apr. 2021, https://tracking.afribary.com/works/malware-classification-into-families-based-on-file-contents-and-characteristics. Accessed 24 Nov. 2024.
Minjibir, Jabir . "Malware Classification Into Families Based On File Contents And Characteristics". Afribary, Afribary, 13 Apr. 2021. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/malware-classification-into-families-based-on-file-contents-and-characteristics >.
Minjibir, Jabir . "Malware Classification Into Families Based On File Contents And Characteristics" Afribary (2021). Accessed November 24, 2024. https://tracking.afribary.com/works/malware-classification-into-families-based-on-file-contents-and-characteristics