AWSCTD research papers

Attack-Caused Windows OS System Calls Traces Dataset

Čeponis, Dainius, and Nikolaj Goranin. "Towards a robust method of dataset generation of malicious activity for anomaly-based HIDS training and presentation of AWSCTD dataset." Baltic Journal of Modern Computing 6, no. 3 (2018): 217-234.
Goranin, Nikolaj, and Dainius Čeponis. "Investigation of AWSCTD dataset applicability for malware type classification." Security & Future 2, no. 2 (2018): 83-86.
Čeponis, Dainius, and Nikolaj Goranin. "Evaluation of Deep Learning Methods Efficiency for Malicious and Benign System Calls Classification on the AWSCTD." Security and Communication Networks 2019 (2019).
Čeponis, Dainius, and Nikolaj Goranin. "Investigation of Dual-Flow Deep Learning Models LSTM-FCN and GRU-FCN Efficiency against Single-Flow CNN Models for the Host-Based Intrusion and Malware Detection Task on Univariate Times Series Data." Applied Sciences 10.7 (2020): 2373.
Vyšniūnas, Tolvinas, et al. "Risk-Based System-Call Sequence Grouping Method for Malware Intrusion Detection." Electronics 13.1 (2024): 206.

Example of how to run training/testing:

python AWSCTD.py //full//path//to//MalwarePlusClean//010_2.csv AWSCTD-CNN-S