| Defending network intrusion detection systems against adversarial evasion attacks M Pawlicki, M Choraś, R Kozik Future Generation Computer Systems 110, 148-154, 2020 | 127 | 2020 |
| A deep learning ensemble for network anomaly and cyber-attack detection V Dutta, M Choraś, M Pawlicki, R Kozik Sensors 20 (16), 4583, 2020 | 115 | 2020 |
| Intrusion detection approach based on optimised artificial neural network M Choraś, M Pawlicki Neurocomputing 452, 705-715, 2021 | 97 | 2021 |
| Tight arms race: Overview of current malware threats and trends in their detection L Caviglione, M Choraś, I Corona, A Janicki, W Mazurczyk, M Pawlicki, ... IEEE Access 9, 5371-5396, 2020 | 88 | 2020 |
| New explainability method for BERT-based model in fake news detection M Szczepański, M Pawlicki, R Kozik, M Choraś Scientific reports 11 (1), 23705, 2021 | 70 | 2021 |
| Achieving explainability of intrusion detection system by hybrid oracle-explainer approach M Szczepański, M Choraś, M Pawlicki, R Kozik 2020 International Joint Conference on neural networks (IJCNN), 1-8, 2020 | 60 | 2020 |
| Machine Learning–the results are not the only thing that matters! What about security, explainability and fairness? M Choraś, M Pawlicki, D Puchalski, R Kozik Computational Science–ICCS 2020: 20th International Conference, Amsterdam …, 2020 | 60 | 2020 |
| A new method of hybrid time window embedding with transformer-based traffic data classification in IoT-networked environment R Kozik, M Pawlicki, M Choraś Pattern Analysis and Applications 24 (4), 1441-1449, 2021 | 53 | 2021 |
| A survey on neural networks for (cyber-) security and (cyber-) security of neural networks M Pawlicki, R Kozik, M Choraś Neurocomputing 500, 1075-1087, 2022 | 43 | 2022 |
| Hybrid model for improving the classification effectiveness of network intrusion detection V Dutta, M Choraś, R Kozik, M Pawlicki 13th International Conference on Computational Intelligence in Security for …, 2021 | 43 | 2021 |
| Machine Learning Based Approach to Anomaly and Cyberattack Detection in Streamed Network Traffic Data. M Komisarek, M Pawlicki, R Kozik, M Choras J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl. 12 (1), 3-19, 2021 | 37 | 2021 |
| A systematic review of recommender systems and their applications in cybersecurity A Pawlicka, M Pawlicki, R Kozik, RS Choraś Sensors 21 (15), 5248, 2021 | 30 | 2021 |
| On the impact of network data balancing in cybersecurity applications M Pawlicki, M Choraś, R Kozik, W Hołubowicz Computational Science–ICCS 2020: 20th International Conference, Amsterdam …, 2020 | 28 | 2020 |
| A $10 million question and other cybersecurity-related ethical dilemmas amid the COVID-19 pandemic A Pawlicka, M Choraś, M Pawlicki, R Kozik Business Horizons 64 (6), 729-734, 2021 | 27 | 2021 |
| Detection of Cyberattacks Traces in IoT Data. V Dutta, M Choras, M Pawlicki, R Kozik J. Univers. Comput. Sci. 26 (11), 1422-1434, 2020 | 27 | 2020 |
| The proposition and evaluation of the roedunet-simargl2021 network intrusion detection dataset ME Mihailescu, D Mihai, M Carabas, M Komisarek, M Pawlicki, ... Sensors 21 (13), 4319, 2021 | 26 | 2021 |
| Guidelines for stego/malware detection tools: Achieving GDPR compliance A Pawlicka, D Jaroszewska-Choras, M Choras, M Pawlicki IEEE Technology and Society Magazine 39 (4), 60-70, 2020 | 26 | 2020 |
| Socialtruth project approach to online disinformation (fake news) detection and mitigation M Choraś, M Pawlicki, R Kozik, K Demestichas, P Kosmides, M Gupta Proceedings of the 14th International Conference on Availability …, 2019 | 24 | 2019 |
| Cost-sensitive distributed machine learning for netflow-based botnet activity detection R Kozik, M Pawlicki, M Choraś Security and Communication Networks 2018, 1-8, 2018 | 24 | 2018 |
| Innovative machine learning approach and evaluation campaign for predicting the subjective feeling of work-life balance among employees A Pawlicka, M Pawlicki, R Tomaszewska, M Choraś, R Gerlach PloS one 15 (5), e0232771, 2020 | 23 | 2020 |
