INNOVATION IN CYBER THREAT DETECTION: TRANSFORMER-BASED APPROACH

Malware poses a major threat to cyber security. In fact, its increasing sophistication and rapid spread over the internet poses increasingly complex challenges. Modern malware uses advanced evasion strategies, often rendering traditional detection systems ineffective, especially against zero-day attacks. These challenges are amplified by complex obfuscation techniques, as well as the diversity of malicious behaviors, fueled by the daily creation of new malware. In the face of these threats, our study proposes an innovative approach using BERT and GPT-2 to improve malware detection. The main innovation of our method lies in the application of Transformers to analyze and identify complex behavioral signatures of malware, which improves the detection capability, particularly in terms of accuracy and generalization to new threats. The evaluation of our model on the CICMalDroid2020 dataset, as well as the comparison of the results obtained with similar works, demonstrate that BERT and GPT-2 offer significant advantages in terms of accuracy, robustness and generalization capacity against modern threats.