FUMVar: A practical framework for generating Fully-working and Unseen Malware Viants

It is crucial to understand how malware variants are generated to bypass malware detection systems and understand their characteristics to improve the detectors' performances. To achieve this goal, we propose an evolutionary-based framework named FUMVar to generate <u>F</u>ully-working and <u>U</u>nseen <u>M</u>alware <u>Var</u>iants. In particular, we applied FUMVar on portable executable (PE) files that have been used extensively to infect Windows operating systems. Compared to the state-of-the-art approach named AIMED, our experimental results show that FUMVar generated 25% more evasive malware variants while reducing the time taken to generate them by 23%. Furthermore, FUMVar generated malware variants that bypassed commercial anti-malware engines, such as TrendMicro, with an alarming rate of up to 73% false-negative rate. To improve the detection techniques, we evaluate how different perturbations enhance the evasiveness and how different malware categories are affected by those perturbations. The results show that perturbations' effectiveness varies significantly by up to 6 times (e.g., section add v.s. unpack), and more suitable perturbations can be selected for different malware categories due to their varying applications. This information can then be used to develop more robust malware detection systems to detect unseen malware variants more effectively.