International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Cybersecurity Firmware for Malware Detection and Prevention Using Transformer Learning |
|---|---|
| ABSTRACT | Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Malicious software applications, or malware, are the primary source of many security problems. These intentionally manipulative malicious applications intend to perform unauthorized activities on behalf of their originators on the host machines for various reasons such as stealing advanced technologies and intellectual properties, governmental acts of revenge, and tampering sensitive information, to name a few. More efficient mitigation methods are needed due to the fast expansion of malicious software on the internet and their self-modifying abilities, as in polymorphic and metamorphic malware. This project proposes to develop the MalFree Sandbox with stacked bidirectional long short-term memory (Stacked BiLSTM) and generative pre-trained transformer based (GPT-2) deep learning language models for detecting malicious code offline. The proposed algorithms, namely the bidirectional long short-term memory (BiLSTM) model and the generative pre-trained transformer 2(GPT-2) detect malicious code pieces by examining assembly instructions obtained from static analysis results of Portable Executable (PE) Files. To understand malwares through MalFree Sandbox, care must be taken to sandbox the malwares in an environment that allows for a detailed and comprehensive analysis while also preventing it from being able to further spread. |
| AUTHOR | S. NIJAR MOHAMED, M. VIJAYALAKSHMI PG Scholar, Department of Master of Computer Applications, RVS College of Engineering, Dindigul, Tamil Nadu, India Assistant Professor, Department of Master of Computer Applications, RVS College of Engineering, Dindigul, Tamil Nadu, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406083 |
| pdf/83_Cybersecurity Firmware for Malware Detection and Prevention Using Transformer Learning.pdf | |
| KEYWORDS | |
| References | [1] Microsoft Malware Classification Challenge (BIG 2015), Kaggle Dataset, 2015. [2] Abadi, M., Agarwal, A., Barham, P., et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems”, 2016. [3] Pedregosa, F., Varoquaux, G., Gramfort, A., et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, Vol. 12, pp. 2825–2830, 2011. [4] Python Software Foundation, Python Documentation, 2026. [5] Pallets Project, Flask Documentation, 2026. [6] Devlin, J., Chang, M.W., Lee, K., and Toutanova, K., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, 2018. [7] Radford, A., Wu, J., Child, R., et al., “Language Models are Unsupervised Multitask Learners (GPT-2)”, OpenAI, 2019. [8] S. Vinayakumar, K. P. Soman, and P. Poornachandran, “Applying Deep Learning Approaches for Network Traffic Prediction and Malware Detection,” Journal of Cyber Security Technology, 2020. |
