## 5 Results
### Integrity Analysis of Pre-Trained MalConv and LGBM  
Table 3 reports the evasion metric measured for the pre-trained MalConv and LGBM models. The evasion metric was computed on the Windows PE malware files of the study dataset for which we were able to generate realistic adversarial Windows PE malware by attacking MalConv with the attack methods: Extend, Full DOS, Shift, FGSM padding+slack and GAMMA. Notably, the evasion metric measured on the LGBM model allowed us to verify the transferability of the evasion ability of the adversarial Windows PE malware files produced by fooling the pre-trained MalConv model versus the pre-trained LGBM model. In fact, the higher the evasion measured on the LGBM model, the higher the number of adversarial Windows PE malware files that were generated to fool the MalConv model but also evaded the LGBM model. In addition, we note that a negative value of evasion means that the Windows PE malware files generated with the considered attack method were all correctly detected in the \"malware\" class using the LGBM model, even when the original malware counterparts from which the adversarial malware files were generated were wrongly classified as goodware according to the same model. So, based upon the considerations reported above, the results of the evasion metric achieved using the pre-trained MalConv model show that Extend is the attack method that was able to generate the higher number of Windows PE malware files that evaded the MalConv model (i.e., they were wrongly classified as goodware), while their original counterparts (i.e., the malware files used for adversarial file generation) were correctly classified as malware according to the MalConv model. GAMMA and Full DOS were the runner-up attack methods for the pre-trained MalConv model. On the other hand, the analysis of the transferability of the realistic adversarial malware files, which fooled both the MalConv and LGBM models, shows that GAMMA achieved the highest transferability using the produced adversarial Windows PE malware files with Extend as the runner-up. In fact, GAMMA measured the highest value of evasion using the LGBM model, as it generated \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **oa** & **prec** & **recall** & **F** & **fnr** & **fpr** \\ \hline MalConv & 0.8034 & 0.9766 & 0.6224 & 0.7603 & 0.3776 & 0.0150 \\ LGBM & 0.9294 & 0.9857 & 0.8717 & 0.9252 & 0.1283 & 0.0127 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy performance analysis of the pre-trained MalConv and LGBM models. The accuracy metrics (oa—overall accuracy, prec—precision, recall—recall, F—Fscore, frr— the false negative rate and fpr—the false positive rate) were measured on the Windows PE dataset prepared for this evaluation study. The best results are underlined. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attack Method** & **MalConv** & **LGBM** \\ \hline Extend & 7951 & 306 \\ Full DOS & 6115 & \(-10\) \\ Shift & 3423 & 41 \\ FGSM & 4384 & \(-157\) \\ GAMMA & 6857 & 1266 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The integrity performance (measured through the evasion metric) of the pre-trained MalConv and LGBM models and computed with respect to the following attack methods: Extend, Full DOS, Shift, FGSM padding + slack and GAMMA. The attack methods were used with the Windows PE malware of the study dataset to attack the pre-trained MalConv model. the highest number of adversarial samples fooling the LGBM model (in addition to the MalConv model). These results support conclusions already drawn in [5] by processing a smaller dataset of older Windows PE malware files. Notably, Full DOS, which generated 6115 adversarial malware files to fool the MalConv model, achieved a negative evasion value with LGBM. This means that 10 malware samples of the original dataset were wrongly classified according to the LGBM model as goodware, while their adversarial counterparts generated to fool the MalConv model with the Full DOS method were correctly detected as malware according to the LGBM model.