## 6 Conclusions and Future Work  
In this paper, we have proposed a lightweight, quick, and accurate machine-learning-based approach for malware detection and categorization, namely MalwD&C. We tested our proposed scheme in two scenarios: (i) malware detection (benign vs. malware) and (ii) malware categorization (multi-class classification).  
The proposed MalwD&C approach uses machine learning to detect and classify malware. The approach was tested on the BODMAS [14] dataset, and the random forest classifier was found to be the most effective. In the malware detection scenario, the approach achieved an accuracy of 99.38% on full features and 99.56% on a selected subset of 25 features, with a reduced training time of 4.269 s. In the malware categorization scenario, the approach achieved an accuracy of 97.59% on full features and 97.69% on selected features, with a reduced training time of 1.25 s. Overall, the experiments show the effectiveness of the proposed approach in detecting and classifying malware with high accuracy and relatively fast training and testing times.  
In summary, our proposed MalwD&C approach demonstrates promising malware detection and categorization results. Using a RF classifier and feature selection enables an accurate and efficient classification of malware samples. Our approach can potentially be used in real-world applications to quickly and accurately identify and classify malware samples. Future work includes exploring other advance classifiers, such as Convolutional Neural Networks (CNNs), on the best feature subset. Since the RF classifier achieves a higher accuracy and has comparatively better training and testing times, we pick this  
Figure 8: ROC curves for difference classifiers for malware detection problem. (**a**) Full Features; (**b**) Selected Features.  
classifier for our final proof-of-the-concept application. We leave the implementation of the proof-of-the-concept application and its evaluation for future work. Additionally, another avenue for future work is to expand the dataset and include more diverse malware samples. This will increase the robustness of the proposed approach and ensure that it can handle a wide range of malware variants. Furthermore, incorporating other feature extraction techniques, such as graph-based representation or embeddings, could also be interesting. Finally, investigating the potential to use MalwD&C in a real-time setting, and evaluating its performance on large-scale datasets can be a promising direction. In conclusion, the results of our proposed MalwD&C approach are promising and offer new opportunities for further research in the field of malware detection and categorization.  
A.B.--Conceptualization, Data curation, Software, Writing--original draft, Investigation, Validation and Visualization; A.B.B.--Methodology, Formal Analysis, Resources, Writing--original draft, Validation, and Investigation; T.A.--Writing--original draft, Writing--review and editing, Funding acquisition, Validation, and Visualization; S.B.--Methodology, Supervision, Writing--original draft, Project Administration, and Visualization; S.U.--Methodology, Software, Resources, Writing--original draft, and Visualization. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.