2022
Vasiliki Kelli; Panagiotis Radoglou-Grammatikis; Thomas Lagkas; Evangelos K Markakis; Panagiotis Sarigiannidis
Risk Analysis of DNP3 Attacks Conference
2022 IEEE International Conference on Cyber Security and Resilience (CSR), 2022, ISBN: 978-1-6654-9952-1.
Περίληψη | BibTeX | Ετικέτες: cyberattacks, Cybersecurity, DNP3, SCADA | Σύνδεσμοι:
@conference{9850291,
title = {Risk Analysis of DNP3 Attacks},
author = {Vasiliki Kelli and Panagiotis Radoglou-Grammatikis and Thomas Lagkas and Evangelos K Markakis and Panagiotis Sarigiannidis},
url = {https://www.researchgate.net/publication/362741509_Risk_Analysis_of_DNP3_Attacks},
doi = {10.1109/CSR54599.2022.9850291},
isbn = {978-1-6654-9952-1},
year = {2022},
date = {2022-07-27},
booktitle = {2022 IEEE International Conference on Cyber Security and Resilience (CSR)},
pages = {351-356},
abstract = {The integration of intelligent devices in the industry allows the automation and control of industrial processes, in an efficient and effective manner. Such systems have contributed to the rapid evolution of production infrastructures, increasing the reliability, reducing production costs, and automating the entire manufacturing operations. However, the utilization of intelligent devices has led to an increased attack surface in critical infrastructures, threatening to compromise regular operations. Attacks against such environments can have disastrous consequences in case their goal is achieved, due to the critical nature of such infrastructures. Thus, the timely identification of vulnerable spots through high-quality risk assessment, is considered highly important for avoiding or mitigating potential risks. In this paper, we focus on Distributed Network Protocol 3 (DNP3), a protocol with high utility in smart grids. Specifically, we investigate, identify and describe the vulnerabilities-by-design of DNP3 through 8 DNP3-centered cyberattacks. In addition, we present a novel method for conducting risk assessment, stemming from the combination of two techniques, namely, Attack Defence Trees (ADTs) and Common Vulnerability Scoring System v3.1 (CVSS). Through our proposed technique, the risk of a cyberattack occurring is calculated, thus contributing in securing the critical infrastructure.},
keywords = {cyberattacks, Cybersecurity, DNP3, SCADA},
pubstate = {published},
tppubtype = {conference}
}
Vasiliki Kelli; Panagiotis Radoglou-Grammatikis; Achilleas Sesis; Thomas Lagkas; Eleftherios Fountoukidis; Emmanouil Kafetzakis; Ioannis Giannoulakis; Panagiotis Sarigiannidis
Attacking and Defending DNP3 ICS/SCADA Systems Conference
2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2022, ISBN: 978-1-6654-9512-7.
Περίληψη | BibTeX | Ετικέτες: cyberattack, DNP3, ICS, Intrusion detection, SCADA | Σύνδεσμοι:
@conference{9881726,
title = {Attacking and Defending DNP3 ICS/SCADA Systems},
author = {Vasiliki Kelli and Panagiotis Radoglou-Grammatikis and Achilleas Sesis and Thomas Lagkas and Eleftherios Fountoukidis and Emmanouil Kafetzakis and Ioannis Giannoulakis and Panagiotis Sarigiannidis},
doi = {10.1109/DCOSS54816.2022.00041},
isbn = {978-1-6654-9512-7},
year = {2022},
date = {2022-05-30},
booktitle = {2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)},
pages = {183-190},
abstract = {The highly beneficial contribution of intelligent systems in the industrial domain is undeniable. Automation, supervision, remote control, and fault reduction are some of the various advantages new technologies offer. A protocol demonstrating high utility in industrial settings, and specifically, in smart grids, is Distributed Network Protocol 3 (DNP3), a multi-tier, application layer protocol. Notably, multiple industrial protocols are not as securely designed as expected, considering the highly critical operations occurring in their application domain. In this paper, we explore the internal vulnerabilities-by-design of DNP3, and proceed with the implementation of the attacks discovered, demonstrated through 8 DNP3 attack scenarios. Finally, we design and demonstrate a Deep Neural Network (DNN)-based, multi-model Intrusion Detection Systems (IDS), trained with our experimental network flow cyberattack dataset, and compare our solution with multiple machine learning algorithms used for classification. Our solution demonstrates a high efficiency in the classification of DNP3 cyberattacks, showing an accuracy of 99.0%.},
keywords = {cyberattack, DNP3, ICS, Intrusion detection, SCADA},
pubstate = {published},
tppubtype = {conference}
}
Διεύθυνση
Internet of Things and Applications Lab
Department of Electrical and Computer Engineering
University of Western Macedonia Campus
ZEP Area, Kozani 50100
Greece
Πληροφορίες Επικοινωνίας
tel: +30 2461 056527
Email: ithaca@uowm.gr