@conference{Radoglou-Grammatikis2020b,

title = {DIDEROT: An intrusion detection and prevention system for DNP3-based SCADA systems},

author = { P. Radoglou-Grammatikis and P. Sarigiannidis and G. Efstathopoulos and P.-A. Karypidis and A. Sarigiannidis},

url = {https://www.researchgate.net/publication/343853580_DIDEROT_an_intrusion_detection_and_prevention_system_for_DNP3-based_SCADA_systems},

doi = {10.1145/3407023.3409314},

year  = {2020},

date = {2020-01-01},

journal = {ACM International Conference Proceeding Series},

abstract = {In this paper, an Intrusion Detection and Prevention System (IDPS) for the Distributed Network Protocol 3 (DNP3) Supervisory Control and Data Acquisition (SCADA) systems is presented. The proposed IDPS is called DIDEROT (Dnp3 Intrusion DetEction pReventiOn sysTem) and relies on both supervised Machine Learning (ML) and unsupervised/outlier ML detection models capable of discriminating whether a DNP3 network flow is related to a particular DNP3 cyberattack or anomaly. First, the supervised ML detection model is applied, trying to identify whether a DNP3 network flow is related to a specific DNP3 cyberattack. If the corresponding network flow is detected as normal, then the unsupervised/outlier ML anomaly detection model is activated, seeking to recognise the presence of a possible anomaly. Based on the DIDEROT detection results, the Software Defined Networking (SDN) technology is adopted in order to mitigate timely the corresponding DNP3 cyberattacks and anomalies. The performance of DIDEROT is demonstrated using real data originating from a substation environment. © 2020 ACM.},

keywords = {Anomaly Detection, Autonencoder, Intrusion detection, machine learning, SCADA, SDN, Smart Grid},

pubstate = {published},

tppubtype = {conference}

}

@conference{Al-Saadi20192025,

title = {A novel approach for performance-based clustering and anagement of network traffic flows},

author = { M. Al-Saadi and B.V. Ghita and S. Shiaeles and P. Sarigiannidis},

url = {https://www.researchgate.net/publication/334634109_A_novel_approach_for_performance-based_clustering_and_anagement_of_network_traffic_flows},

doi = {10.1109/IWCMC.2019.8766728},

year  = {2019},

date = {2019-01-01},

journal = {2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019},

pages = {2025-2030},

abstract = {Management of network performance comprises numerous functions such as measuring, modelling, planning and optimising networks to ensure that they transmit traffic with the speed, capacity and reliability expected by the applications, each with different requirements for bandwidth and delay. Overall, the objective of this paper is to propose a novel mechanism to optimise the network resource allocation through supporting the routing of individual flows, by clustering them based on performance and integrating the respective clusters with an SDN scheme. In this paper we have employed a particular set of traffic features then applied data reduction and unsupervised machine learning techniques, to derive an Internet traffic performance-based clustering model. Finally, the resulting data clusters are integrated within a unified SDN architectural solution, which improves network management by finding nearly optimal flow routing, to be evaluated against a number of traffic data sources. © 2019 IEEE.},

keywords = {Clustering, Network performance, SDN, Unsupervised algorithm},

pubstate = {published},

tppubtype = {conference}

}