A Strategy For Spotting Irregularities in Unix Shell  Activities Using a Pre-trained DistilBERT Model in  Computer Security
Zefang Liu, John F. Buford
Information Security Education Journal , Vol. 11, No. 1, 2024.
DOI: https://doi.org/10.6025/isej/2024/11/1/13-28
Link: https://www.dline.info/isej/fulltext/v11n1/isejv11n1_3.pdf
Abstract:
Identifying irregularities in command shell activities is crucial to maintaining computer security. Significant progress has been made in deep learning and natural language understanding, especially with transformer-based models, which have shown considerable potential in tackling intricate security issues. In this study, we present a thorough strategy for spotting irregularities in Unix shell activities using a pre-trained DistilBERT model, combining both unsupervised and supervised learning methods to spot unusual activities while reducing the need for manual labeling of data. The unsupervised technique focuses on understanding Unix shell commands basic structure and grammar, allowing for the recognition of deviations from typical behavior. Trials on a large-scale dataset from real-world systems have proven the effectiveness of our strategy in identifying unusual activities in Unix shell sessions. This research underscores the possibility of using recent breakthroughs in transformer technology to tackle significant security issues in computing.