## 4 Results and Discussion
### Malware Family Classification  
Malware classification encompassed 11 distinct families, each assigned labels and corresponding distribution percentages. This is crucial for understanding the diverse nature of malware and its prevalence. Each of these categories is represented in Table 3, providing an overview of the malware families as observed in this dataset.  
The analysis of malware families provides crucial insights for tailoring cyber-security measures to combat specific threats effectively. The high prevalence of Trojans (27%) suggests a need for enhanced vigilance in software verification processes, as Trojans often masquerade as legitimate applications. The prevalence of ransomware (22%) emphasises the importance of robust backup systems and advanced encryption detection tools. Regular backups can mitigate the impact of data encryption by ransomware, while specialised algorithms can help in the early detection and prevention of ransomware attacks. Downloaders and droppers, each at 11%, highlight the necessity for advanced network monitoring and endpoint security solutions. These types of malware often initiate the downloading and installation of additional malicious software, making it crucial to detect and block them at the earliest stages. The presence of generic malware, also at 11%, highlights the need for versatile and adaptive malware detection systems capable of identifying and responding to a wide range of malicious behaviours that may not fit into specific categories. Backdoors and stealers, each constituting 5%, call for enhanced network security and data protection measures. Backdoors require robust system integrity checks to prevent unauthorised access, while stealers necessitate strong encryption and privacy safeguards to protect sensitive information. Spyware, at 4%, and adware, at 3%, highlight the importance of privacy protection and user consent mechanisms. Effective adware-blocker algorithms can help mitigate the risks associated with these types of malware. Rootkits and worms, each making up 1% of the dataset, demand specialised detection algorithms that use deep system scans to uncover hidden malware, and Worms necessitate network security measures to prevent their spread across systems and networks. These findings emphasise the need for a  
\begin{table}
\begin{tabular}{c c} \hline
**System** & **Specifications** \\ \hline Device & Intel \\ Processor & Core i7-4600U @ 1.90 GHz \\ SSD & 512 GB \\ RAM & 16 GB \\ Operating system & Windows 10 \\ Virtual machine & Virtualbox 7.0 \\ Docker & 23.0.3 \\ Cuckoo Sandbox & 2.0.7 \\ Virustotal API & v3 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental setup.  
\begin{table}
\begin{tabular}{c c} \hline
**Family** & **Percentage** \\ \hline Trojan & 27\% \\ Ransomware & 22\% \\ Downloader & 11\% \\ Dropper & 11\% \\ Generic malware & 11\% \\ Backdoor & 5\% \\ Stealer & 5\% \\ Spyware & 4\% \\ Adware & 3\% \\ Rootkit & 1\% \\ Worm & 1\% \\ \hline \end{tabular}
\end{table}
Table 3: Malware family classification.  
multi-faceted and proactive approach to cyber security, with tailored strategies to address the unique challenges posed by each malware family.