BD-MDLC: Behavior description-based enhanced malware detection for windows environment using longformer classifier

Author:

Babu SarathORCID,Singh Virendra

Publisher

Elsevier BV

Reference29 articles.

1. Alrawi, O., Lever, C., Valakuzhy, K., Snow, K., Monrose, F., Antonakakis, M., et al., 2021. The Circle of life: A large-scale study of the IoT malware lifecycle. In: 30th USENIX Security Symposium. USENIX Security 21, pp. 3505–3522.

2. A dynamic Windows malware detection and prediction method based on contextual understanding of API call sequence;Amer;Comput. Secur.,2020

3. EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models;Anderson,2018

4. Avllazagaj, E., Zhu, Z., Bilge, L., Balzarotti, D., Dumitraş, T., 2021. When malware changed its mind: An empirical study of variable program behaviors in the real world. In: 30th USENIX Security Symposium. USENIX Security 21, pp. 3487–3504.

5. Longformer: The long-document transformer;Beltagy,2020

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