## 7 Conclusions  
In today's digital age, the threat of malware is ever-present and constantly on the rise. It can wreak havoc on computer systems, compromising personal and sensitive information. Therefore, it is crucial to take necessary precautions to protect assets against such threats. This paper tackled the significant challenge of malware threats through the generation of a novel dataset from the MalwareBazaar repository. The curated dataset included 582 malware and 438 goodware samples from Windows PE files, which served as a foundation for our dynamic analysis and classification framework. For evaluation, a comprehensive five-stage approach was adopted: generating a tailored dataset, labelling malware samples, categorising APIs, extracting and scoring pivotal features using Chi2 and Gini Importance, and applying six cutting-edge ML models. The results highlighted that the RF model demonstrated superior performance, with an impressive precision rate of 99% and an accuracy of 96%. The RF model's AUC stood at 98%, and it achieved an F1-score of 96%, indicating a highly effective balance between precision and recall. These results were \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Research Work** & **ML Models** & **Accuracy** & **Precision** & **F1-Score** & **Recall** & **AUC** & **Specificity** \\ \hline [PERSON] et al. [14] & NA & NA & NA & NA & NA & NA & NA \\ [PERSON] et al. [20] & KNN, RF, SVM, CW, AROW & 98 & 93 & 94 & 97 & NA & NA \\ [PERSON] et al. [41] & NB, RF, RT, SMO, K-Star, LR & 98 & 97 & NA & NA & NA & NA \\ [PERSON] et al. [52] & NLSTM & 95 & 90 & 83 & 77 & NA & NA \\ [PERSON] et al. [27] & Jaccard index & NA & 92 & 89 & 87 & NA & NA \\
**Our Research** & LR, RF, SVM, NB, XGB, KNN & 96 & 99 & 96 & 96 & 98 & 98 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Research comparison in terms of machine learning models and the use of performance metrics. further authenticated by a TPR of 0.93 and an exceptionally low FPR of 0.0098, marking it as the most reliable among the evaluated models. The study revealed that Trojans and ransomware, constituting 27% and 22%, respectively, were predominant among the 11 analysed malware families. This indicates that we need to adopt more sophisticated strategies for these malware. In API categorisation, Windows APIs (22%) and APIs related to the file system (12%) and registry manipulation (8.2%) showcased their importance in detecting malicious activity. The high API usage indicates the need for monitoring to develop targeted security solutions by tracking unauthorized or suspicious changes that can provide early warning signs of malware infection. Our research distinguished itself by adopting a dual approach for feature reduction and scoring, as well as by achieving an improved F1-score (2%) and including AUC and specificity metrics, aspects not comprehensively addressed in previous studies. The curated dataset is now publicly available for further research and lays a solid groundwork for future studies on malware evolution prediction. D.Z.S. : Conceptualisation, Data Curation, Validation, Experimentation, and Writing. M.N.A. : Editing, Reviewing, Validation, and Supervision. All authors have read and agreed to the published version of the manuscript. This research is funded by the Hardiman PhD Scholarship 2023 in the School of Computer Science, University of Galway, Ireland. Publicly available dataset is accessible on GitHub [[https://github.com/drzehra14/Windows-PE-Malware-API-dataset/](https://github.com/drzehra14/Windows-PE-Malware-API-dataset/)]([https://github.com/drzehra14/Windows-PE-Malware-API-dataset/](https://github.com/drzehra14/Windows-PE-Malware-API-dataset/)) (accessed on 9 March2023). By providing access to this comprehensive collection of data, the goal is to facilitate a wide range of experiments, especially those new to the field of dynamic malware analysis. By offering a pre-processed and structured dataset, it eliminates the initial barriers associated with data collection and preparation.