| A scalable distributed machine learning approach for attack detection in edge computing environments R Kozik, M Choraś, M Ficco, F Palmieri Journal of Parallel and Distributed Computing 119, 18-26, 2018 | 154 | 2018 |
| A deep learning ensemble for network anomaly and cyber-attack detection V Dutta, M Choraś, M Pawlicki, R Kozik Sensors 20 (16), 4583, 2020 | 140 | 2020 |
| Defending network intrusion detection systems against adversarial evasion attacks M Pawlicki, M Choraś, R Kozik Future Generation Computer Systems 110, 148-154, 2020 | 139 | 2020 |
| Contactless palmprint and knuckle biometrics for mobile devices M Choraś, R Kozik Pattern Analysis and Applications 15 (1), 73-85, 2012 | 105 | 2012 |
| 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 | 89 | 2021 |
| Sentiment analysis for fake news detection by means of neural networks S Kula, M Choraś, R Kozik, P Ksieniewicz, M Woźniak Computational Science–ICCS 2020: 20th International Conference, Amsterdam …, 2020 | 78 | 2020 |
| Machine learning techniques applied to detect cyber attacks on web applications M Choraś, R Kozik Logic Journal of IGPL 23 (1), 45-56, 2015 | 74 | 2015 |
| Simulation platform for cyber-security and vulnerability analysis of critical infrastructures M Ficco, M Choraś, R Kozik Journal of computational science 22, 179-186, 2017 | 73 | 2017 |
| 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 | 68 | 2020 |
| 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 | 62 | 2020 |
| Application of the bert-based architecture in fake news detection S Kula, M Choraś, R Kozik 13th International Conference on Computational Intelligence in Security for …, 2021 | 59 | 2021 |
| Measuring and improving agile processes in a small-size software development company M Choraś, T Springer, R Kozik, L Lopez, S Martínez-Fernández, P Ram, ... IEEE access 8, 78452-78466, 2020 | 57 | 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 | 54 | 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 | 53 | 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 | 44 | 2021 |
| Data-driven and tool-supported elicitation of quality requirements in agile companies M Oriol, S Martínez-Fernández, W Behutiye, C Farré, R Kozik, ... Software Quality Journal 28 (3), 931-963, 2020 | 44 | 2020 |
| Cyber threats impacting critical infrastructures M Choraś, R Kozik, A Flizikowski, W Hołubowicz, R Renk Managing the complexity of critical infrastructures: A modelling and …, 2016 | 44 | 2016 |
| 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 | 43 | 2021 |
| Machine learning methods for fake news classification P Ksieniewicz, M Choraś, R Kozik, M Woźniak Intelligent Data Engineering and Automated Learning–IDEAL 2019: 20th …, 2019 | 41 | 2019 |
| A systematic review of recommender systems and their applications in cybersecurity A Pawlicka, M Pawlicki, R Kozik, RS Choraś Sensors 21 (15), 5248, 2021 | 40 | 2021 |
