RT Conference Proceedings
T1 Runtime security monitoring by an interplay between rule matching and deep learning-based anomaly detection on logs
A1 Antic, Jan
A1 Costa, Joao Pita
A1 Cernivec, Ales
A1 Cankar, Matija
A1 Martincic, Tomaz
A1 Potocnik, Aljaz
A1 Ratkajec, Hrvoje
A1 Elguezabal, Gorka Benguria
A1 Leligou, Nelly
A1 Lakka, Alexandra
A1 Boigues, Ismael Torres
A1 Morte, Eliseo Villanueva
AB In the era of digital transformation the increasing vulnerability of infrastructure and applications is often tied to the lack of technical capability and the improved intelligence of the attackers. In this paper, we discuss the complementarity between static security monitoring of rule matching and an application of self-supervised machine-learning to cybersecurity. Moreover, we analyse the context and challenges of supply chain resilience and smart logistics. Furthermore, we put this interplay between the two complementary methods in the context of a self-learning and self-healing approach.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9781665475983
YR 2023
FD 2023
LK https://hdl.handle.net/11556/1472
UL https://hdl.handle.net/11556/1472
LA eng
NO Antic , J , Costa , J P , Cernivec , A , Cankar , M , Martincic , T , Potocnik , A , Ratkajec , H , Elguezabal , G B , Leligou , N , Lakka , A , Boigues , I T & Morte , E V 2023 , Runtime security monitoring by an interplay between rule matching and deep learning-based anomaly detection on logs . in 2023 19th International Conference on the Design of Reliable Communication Networks, DRCN 2023 . 2023 19th International Conference on the Design of Reliable Communication Networks, DRCN 2023 , Institute of Electrical and Electronics Engineers Inc. , 19th International Conference on the Design of Reliable Communication Networks, DRCN 2023 , Vilanova i la Geltru , Spain , 17/04/23 . https://doi.org/10.1109/DRCN57075.2023.10108105
NO conference
NO Publisher Copyright: © 2023 IEEE.
NO ACKNOWLEDGMENT This project has received funding from the European Union’s Horizon 2020 research and innovation programmes under Grant Agreements No. 101000162 (PIACERE), 952644 (FISHY) and MEDINA (952633) This project has received funding from the European Union s Horizon 2020 research and innovation programmes under Grant Agreements No. 101000162 (PIACERE), 952644 (FISHY) and MEDINA (952633)
DS TECNALIA Publications
RD 29 jul 2024