Machine learning is rapidly becoming one of the most important technology for malware traffic detection, since the continuous evolution of malware requires a constant adaptation and the ability to generalize . However, network traffic datasets are usually oversized and contain redundant and irrelevant information, and this may dramatically increase the computational cost and decrease the accuracy of most classifiers, with the risk to introduce further noise.
|Titolo:||New Optimization Approaches in Malware Traffic Analysis|
LETTERI, IVAN (Corresponding)
|Data di pubblicazione:||2022|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|