Russo, M., Srndic, N., & Laskov, P. (2021). Detection of illicit cryptomining using network metadata. EURASIP Journal on Information Security(11).

Beitrag in wissenschaftlicher Fachzeitschrift

https://dx.doi.org/10.1186/s13635-021-00126-1

Apruzzese, G., Pajola, L., & Conti, M. (2022). The Cross-Evaluation of Machine Learning-based Network Intrusion Detection Systems. IEEE Transactions on Network and Service Management, 19(4).

Beitrag in wissenschaftlicher Fachzeitschrift

https://dx.doi.org/10.1109/tnsm.2022.3157344

Apruzzese, G., Andreolini, M., Ferretti, L., Marchetti, M., & Colajanni, M. (2022). Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems. Digital Threats: Research and Practice, 3(3).

Beitrag in wissenschaftlicher Fachzeitschrift

https://dx.doi.org/10.1145/3469659

Corsini, A., Yang, S. J., & Apruzzese, G. (2021). On the Evaluation of Sequential Machine Learning for Network Intrusion Detection. Paper presented at the 16th International Conference on Availability, Reliability and Security, Vienna, Austria.

Beitrag in Konferenztagungsband

https://dx.doi.org/10.1145/3465481.3470065

Martín-Pérez, M., Rodríguez, R., & Breitinger, F. (2021). Bringing order to approximate matching: Classification and attacks on similarity digest algorithms. Forensic Science International: Digital Investigation, 36(301120).

Beitrag in wissenschaftlicher Fachzeitschrift

https://dx.doi.org/10.1016/j.fsidi.2021.301120

Husák, M., Apruzzese, G., Yang, S. J., & Werner, G. (2021). Towards an Efficient Detection of Pivoting Activity. Paper presented at the 17th IFIP/IEEE International Symposium on Integrated Network Management - GraSec Workshop, Bordeaux, France.

Beitrag in Konferenztagungsband