An Antivirus with Extreme Learning Morphological Machine

Abstract

Each second, 8 (eight) new malwares (malicious + software) are launched. Based on this finding, our goal is to propose an antivirus endowed with artificial intelligence, which can identify malware through models based on neural networks of rapid training and high accuracy. The proposed antivirus is equipped with morphological extreme learning machines (mELMs), which are inspired by the image processing theory of Mathematical Morphology. The results shown here are compared with classical approaches and evaluated through widely used classification metrics. On average, the antivirus proposed can distinguish malware from benign applications in 99.80% of cases, with a training time of 9.32 seconds. We found an average ratio between percentage accuracy and training time in reverse order. The proposed antivirus is 96 times better than state-of-the-art Deep Learning.

Keywords

• malwares
• antivirus
• artificial neural networks
• real-time malware detection