Next generation antivirus endowed with bitwise morphological extreme learning machines

Abstract Background and Objective Every second, on average, 8 (eight) new malware are created. So, our goal is to propose an antivirus, endowed with artificial intelligence, able of identifying malwares through models based on fast training and high-performance neural networks. Methods Our NGAV (Next Generation Antivirus) is equipped with an authorial ELM (Extreme Learning Morphological) machine. Our bmELMs (Bitwise-Morphological ELMs) are inspired by the image processing theory of Mathematical Morphology. We claim that bmELMs are able to adapt in any machine learning dataset. Inspired by Mathematical Morphology, our bmELMs are capable of modeling any form present at the decisions boundaries of neural networks. Results Our bmELMs results are compared with classical ELMs and evaluated through widely used classification metrics. Our antivirus, provided with Bitwise-Morphology, achieves an average accuracy of 97.88%, 93.07%, 93.07% and 91.74% in malware detection of PE (Portable Executable), Java, JavaScript and PHP, respectively. Conclusions Our NGAV enables high performance, large capacity of parallelism, and simple, low-power architecture with low power consumption. We concluded that our Bitwise-Morphology assists to the main requirements for the proper operation and confection of antivirus in hardware.