Detection of Malicious Tools by Monitoring DLL Using Deep Learning

Author:

Matsuda Wataru1,Fujimoto Mariko1,Mitsunaga Takuho2

Affiliation:

1. The University of Tokyo

2. Toyo University

Publisher

Information Processing Society of Japan

Subject

General Computer Science

Reference32 articles.

1. [1] CISA: Publicly available tools seen in cyber incidents worldwide (2018).

2. [2] Sjarif, N.N.A., Chuprat, S., Mahrin, M.N., Ahmad, N.A., Ariffin, A., Senan, F.M., Zamani, N.A. and Saupi, A.: Endpoint detection and response: Why use machine learning? 2019 International Conference on Information and Communication Technology Convergence (ICTC), pp.283-288 (Oct. 2019).

3. [3] Ghafir, I. and Prenosil, V.: Malicious file hash detection and drive-by download attacks, Proc. Second International Conference on Computer and Communication Technologies, pp.661-669, Springer (2016).

4. [4] Mulder, J.: The sans institute: Mimikatz overview, defenses and detection (2016), available from <https://www.sans.org/reading-room/whitepapers/detection/mimikatz-overview-defenses-detection-36780>.

5. [5] RenditionSec: Antivirus isn't dead, but you need monitoring too (2017), available from <https://blog.renditioninfosec.com/2017/11/antivirus-isnt-dead-but-you-need-monitoring-too/>.

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