Radoglou-Grammatikis, Panagiotis; Liatifis, Athanasios; Grigoriou, Elisavet; Saoulidis, Theocharis; Sarigiannidis, Antonios; Lagkas, Thomas; Sarigiannidis, Panagiotis
IEEE International Conference on Cyber Security and Resilience (CSR), IEEE, 2021.
The rise of the Industrial Internet of Things (IIoT) plays a crucial role in the era of hyper-connected digital economies. Despite the valuable benefits, such as increased resiliency, self-monitoring and pervasive control, IIoT raises severe cybersecurity and privacy risks, allowing cyberattackers to exploit a plethora of vulnerabilities and weaknesses that can lead to disastrous consequences. Although the Intrusion Detection and Prevention Systems (IDPS) constitute valuable solutions, they suffer from several gaps, such as zero-day attacks, unknown anomalies and false positives. Therefore, the presence of supporting mechanisms is necessary. To this end, honeypots can protect the real assets and trap the cyberattackers. In this paper, we provide a web-based platform called TRUSTY , which is capable of aggregating, storing and analysing the detection results of multiple industrial honeypots related to Modbus/Transmission Control Protocol (TCP), IEC 60870-5-104, BACnet, Message Queuing Telemetry Transport (MQTT) and EtherNet/IP. Based on this analysis, we provide a dataset related to honeypot security events. Moreover, this paper provides a Reinforcement Learning (RL) method, which decides about the number of honeypots that can be deployed in an industrial environment in a strategic way. In particular, this decision is converted into a Multi-Armed Bandit (MAB), which is solved with the Thompson Sampling (TS) method. The evaluation analysis demonstrates the efficiency of the proposed method.
Radoglou-Grammatikis, Panagiotis; Rompolos, Konstantinos; Sarigiannidis, Panagiotis; Argyriou, Vasileios; Lagkas, Thomas; Sarigiannidis, Antonios; Goudos, Sotirios; Wan, Shaohua
In: IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 2041–2052, 2021.
The rise of the Internet of Medical Things introduces the healthcare ecosystem in a new digital era with multiple benefits, such as remote medical assistance, real-time monitoring, and pervasive control. However, despite the valuable healthcare services, this progression raises significant cybersecurity and privacy concerns. In this article, we focus our attention on the IEC 60 870-5-104 protocol, which is widely adopted in industrial healthcare systems. First, we investigate and assess the severity of the IEC 60 870-5-104 cyberattacks by providing a quantitative threat model, which relies on Attack Defence Trees and Common Vulnerability Scoring System v3.1. Next, we introduce an intrusion detection and prevention system (IDPS), which is capable of discriminating and mitigating automatically the IEC 60 870-5-104 cyberattacks. The proposed IDPS takes full advantage of the machine learning (ML) and software defined networking (SDN) technologies. ML is used to detect the IEC 60 870-5-104 cyberattacks, utilizing 1) Transmission Control Protocol/Internet Protocol network flow statistics and 2) IEC 60 870-5-104 payload flow statistics. On the other side, the automated mitigation is transformed into a multiarmed bandit problem, which is solved through a reinforcement learning method called Thomson sampling and SDN. The evaluation analysis demonstrates the efficiency of the proposed IDPS in terms of intrusion detection accuracy and automated mitigation performance. The detection accuracy and the F1 score of the proposed IDPS reach 0.831 and 0.8258, respectively, while the mitigation accuracy is calculated at 0.923.
Radoglou-Grammatikis, Panagiotis; Sarigiannidis, Panagiotis; Efstathopoulos, George; Karypidis, Paris-Alexandros; Sarigiannidis, Antonios
In: Proceedings of the 15th International Conference on Availability, Reliability and Security, Association for Computing Machinery, Virtual Event, Ireland, 2020, ISBN: 9781450388337.
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.
Grammatikis, Panagiotis Radoglou; Sarigiannidis, Panagiotis; Iturbe, Eider; Rios, Erkuden; Sarigiannidis, Antonios; Nikolis, Odysseas; Ioannidis, Dimosthenis; Machamint, Vasileios; Tzifas, Michalis; Giannakoulias, Alkiviadis; Angelopoulos, Michail; Papadopoulos, Anastasios; Ramos, Francisco
Secure and Private Smart Grid: The SPEAR Architecture (Inproceedings)
In: 2020 6th IEEE International Conference on Network Softwarization (NetSoft), pp. 450-456, 2020.
Information and Communication Technology (ICT) is an integral part of Critical Infrastructures (CIs), bringing both significant pros and cons. Focusing our attention on the energy sector, ICT converts the conventional electrical grid into a new paradigm called Smart Grid (SG), providing crucial benefits such as pervasive control, better utilisation of the existing resources, self-healing, etc. However, in parallel, ICT increases the attack surface of this domain, generating new potential cyberthreats. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) architecture which constitutes an overall solution aiming at protecting SG, by enhancing situational awareness, detecting timely cyberattacks, collecting appropriate forensic evidence and providing an anonymous cybersecurity information-sharing mechanism. Operational characteristics and technical specifications details are analysed for each component, while also the communication interfaces among them are described in detail.