Towards a Robust Method of Dataset Generation of Malicious Activity on a Windows-Based Operating System for Anomaly-Based HIDS Training. - Dainius Ceponis; Nikolaj Goranin (Doctoral Consortium/Forum@DB&IS, 2018)

Towards a Robust Method of Dataset Generation of Malicious Activity for Anomaly-Based HIDS Training and Presentation of AWSCTD Dataset. - Dainius Ceponis; Nikolaj Goranin (Balt. J. Mod. Comput., 2018)

Evaluation of Deep Learning Methods Efficiency for Malicious and Benign System Calls Classification on the AWSCTD. - Dainius Ceponis; Nikolaj Goranin (Secur. Commun. Networks, 2019)

Automated System-Level Anomaly Detection and Classification Using Modified Random Forest. - Nana Kwame Gyamfi; Dainius Ceponis; Nikolaj Goranin (ICAIC, 2022)

Malware Detection Using Convolutional Neural Network, A Deep Learning Framework: Comparative Analysis. - Nana Kwame Gyamfi; Nikolaj Goranin; Dainius Ceponis; Antanas Cenys (J. Internet Serv. Inf. Secur., 2022)

An Empirical Determination of Optimum Artificial Intelligence Algorithm: Detection Using Signal-to-Noise Ratio Approach. - Nana Kwame Gyamfi; Nikolaj Goranin; Dainius Ceponis (IEEE Access, 2024)