MalwareBako – An Automated Malware Analysis and Interactive Sandbox System

This study highlights the limitations of traditional malware detection techniques and demonstrates how integrating multiple analysis approaches significantly enhances detection capability and resilience against modern malware evasion strategies. This paper draws attention to the drawbacks of the conventional methods of malware detection and shows how a combination of various methods of analysis can greatly increase detection rates and better resistance to the contemporary malware evasion methods. This paper introduced MalwareBako, a full and modular malware analysis platform incorporating both static analysis and dynamic sandbox execution and interactive investigation into a safe and isolated environment. The system can efficiently meet the requirements of scalable and effective malware analysis by integrating automatic detection tools with manual exploration tools. Sandbox execution facilitates the platform to attain low-latency static processing, highprecision extraction of IOCs, and effective behavioural monitoring, and ensures complete isolation with virtualisation technologies. The platform can be used as a practical cybersecurity research, incident response, and educational applications besides its technical capabilities. The combination of automated pipelines and interactive sandbox environments makes it more analytical and usable and can be applied to real-life security processes. The next step in the work will be to expand the platform to accept Android APKs and respond to the fast-growing threat environment in the mobile world. Moreover, the adaptive learning mechanisms will also be examined to update the detection rules and threat scoring parameters dynamically in response to the newly observed samples to enhance the detection performance in the long-term. The system will be made more applicable to an enterprise security setting through the integration of structured threat intelligence frameworks, including mapping behavioural indicators to standardised attack techniques. These improvements will help to turn MalwareBako into more intelligent, adaptive, and userfriendly cybersecurity platform based on technical analysis architecture.

[1]      Perera, J., Feng, X., Biswas, M. I., & Migacz, L. (2025, August). Virtual Sandbox for Malware Analysis and Threat Detection. In 2025 IEEE International Conference on High Performance Computing and Communications (HPCC) (pp. 1619-1624). IEEE. [2]      S. Jamalpur, Y. S. Navya, P. Raja, G. Tagore and G. R. K. Rao, "Dynamic Malware Analysis Using Cuckoo Sandbox," 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 2018, pp. 1056-1060, doi: 10.1109/ICICCT.2018.8473346.Dynam ic Malware Analysis Using Cuckoo Sandbox | IEEE Conference Publication | IEEE Xplore [3]      MalwareBazaar. (n.d.). Free automated malware analysis      platform. Retrieved from https://malwarebazaar.com/ [4]      M. Vasilescu, L. Gheorghe and N. Tapus, "Practical malware analysis based on sandboxing," 2014 RoEduNet Conference 13th Edition: Networking in Education and Research Joint Event RENAM 8th Conference, Chisinau, Moldova, 2014, pp. 1-6, doi: 10.1109/RoEduNet  RENAM.2014.6955304. Practical malware analysis based on sandboxing | IEEE Conference Publication | IEEE Xplore [5]      S. Jain, T. Choudhury, V. Kumar and P. Kumar, "Detecting Malware & Analysing Using Sandbox Evasion," 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India, 2018, pp. 111-116, doi: 10.1109/IC3IoT.2018.8668149. Detecting Malware & Analysing Using Sandbox Evasion | IEEE Conference Publication | IEEE Xplore [6]      Greamo, C., & Ghosh, A. (2011). Sandboxing and virtualization: Modern tools for combating malware. IEEE Security & Privacy, 9(2), 79-82. [7]      Miwa, S., Miyachi, T., Eto, M., Yoshizumi, M., & Shinoda, Y. (2007, August). Design and Implementation of an Isolated Sandbox with Mimetic Internet Used to Analyze Malwares. In DETER. [8]      Tumkur, S. D., Eswarakrishnan, V., Wairagade, A., Aslam, M. A., Bilal, M., & Cheema, A. M. (2025). Optimizing Malware Detection in Virtual Cloud Environments Using Hybrid Machine Learning Approach. Arabian Journal for Science and Engineering, 50(23), 19463-19494. [9]      Chauhan, A., Ailawalia, P., & Mehra, C. (2024). Advancements in Automated Malware Analysis Techniques: A Comprehensive Study. Grenze International Journal of Engineering & Technology (GIJET), 10. [10]  Shalaginov, A., Banin, S., Dehghantanha, A., & Franke, K. (2018). Machine learning aided static malware analysis: A survey and tutorial. Cyber threat intelligence, 7-45.