JCOP Coordination, Action Management and Technical Contributions from Technical University of Crete
The goal of the Action is to create a Joint Cybersecurity Operations Platform (JCOP) that is specifically designed to enhance the cyber capabilities of the EU Member State authorities, by providing assets regarding (i) Cyber Threat Intelligence (CTI), (ii) Incident Response and (iii) Cybersecurity Operations Training (COT). In addition, the Action's positive impact will be enhanced by extensive cross-border cooperation activities for cyber defense competence augmentation, which is essential for strengthening trust among the Member States. The delivered platform will promote and integrate concepts such as shared situational awareness, joint preparedness and coordinated incident response.
Figure 1: Outline of the JCOP pillars
Technical University of Crete - Management
TUC is the project coordinator of JCOP and as such it is responsible for the successful implementation of the Action, which translates into tasks including: administration, monitoring, and quality assurance. In particular, the activity that TUC leads is constituted by project management reporting, cost claim preparation, administrative checks, as well as providing a clear vision for the project's goals and overall requirements. In close coordination with the European Commission (EC) and the European Health and Digital Executive Agency (HaDEA), TUC is obliged to submit all required documentation, including financial statements. Regarding the status of the progress of all activities, reports are submitted to HaDEA on a semi-annual basis. In case of detecting deviations from the work plan, the coordinator must take preemptive, or immediate action in a responsible manner, to subdue emerging problems and risks. Last but not least, TUC was the organizer of the kick-off meeting that took place in August 2021, in addition to six plenary meetings: (1) November 2021, (2) January 2022, (3) July 2022 (all physical in Cyprus), and (4) October 2022 (physical in Greece, Chania), (5) February 2023 (physical in Greece, Athens) and (6) June 2023 (physical in Norway, Oslo).
In addition to management, monitoring, quality assurance, and reporting, TUC is in charge of defining the Architecture of the platform, Key Performance Indicators (KPIs), and requirements of the Action. The architecture refers to the platform interfaces and functionalities of the core modules. Specific requirements concerning the architecture and the core modules have already been defined, incorporating input from the authorities, who provided expertise through formulating requirements specification from an end-user perspective. This was enriched with further input from the rest of the consortium regarding the specification of the requirements collection methodology, the technical requirements, the KPIs, and the architecture itself.
The overall methodology was defined following the steps below, in sequential order:
- Identification of end user requirements
- Definition of logical (high-level) architecture
- Components specification
- Definition of detailed (development) architecture
- Definition of the full set of requirements
- Definition of KPIs and evaluation methodology
By the end of the Action, the goal is to provide a platform that is in line with the recommendations provided in the European Commission Recommendation on Coordinated Response to Large Scale Cybersecurity Incidents and Crises1 (also known as “Cyber Blueprint”), and the proposal for a Joint Cyber Unit, which demonstrates cross-border cooperation for enhancing joint cybersecurity operations, if the result is in line with relevant Security Operations Centers (SOCs) that operate around the EU Cybersecurity Strategy.
On the technical side of the project, JCOP will offer a platform consisting of multiple cybersecurity-oriented components. TUC has directly provided contributions to the technical task of JCOP, specifically: HoneyChart and GNORT. These tools are part of the locally deployed Cyber Threat Intelligence (CTI) assets of JCOP, and their main functionality is alerting/logging relevant information supporting preemptive steps such as blacklisting and monitoring. Such data can also be correlated with other threat indicators, serving as an important component for intrusion prevention or intrusion detection to secure the host. Such protection and prevention mechanisms have been effective over the years and are actively deployed in order to protect information systems that can be vulnerable to intrusion attacks. In the case of JCOP, it is imperative that the platform utilizes such tools to proactively fend off attacks by parsing alerts/logs through the rest of the platform's components.
The following paragraphs describe the tools developed by TUC:
Figure 2: HoneyChart web tool interface.
Figure 3: Honeypots' generation process in HoneyChart.
- GPU accelerated Snort, which is an intrusion detection system (IDS). Snor4 is an open-source tool that can detect intrusions by packet inspection and payload analysis, based on preconfigured rulesets. Typically an IDS is placed as a switch (Figure 4), between the host(s) and incoming packet traffic. Snort also provides a mode that can be set as an intrusion prevention system (IPS), which then directly controls the packet traffic, rather than simply making observations and alerts. It can be very easily configured by specifying certain rules that overwatch ports or IP addresses which the user wants to be alerted about, by detecting certain string patterns that may appear in the payload section of a packet, and many more functionalities. Since it is an open-source tool, there are plenty of community-created rules available. As an additional contribution, TUC provides an enhancement to Snort with a Deep Packet Inspection (DPI) tool5 (Figure 5), which transfers the packets to the GPU, implements the Aho-Corasick multi-pattern matching algorithm for string searching on the GPU and transfers the results back to the CPU. By using GPU resources it achieves parallel and faster computation. The GPU porting of the matching algorithm is written in OpenCL language.
Figure 5: Deterministic Finite Automaton. Depicts how Snort processes information, complemented with the GPU enhanced string searching algorithm. Note, that does not aim to change, but to enhance the way Snort operates.
You can read the full paper in the following url:
For technical details please visit our GitHub repo for this project: https://github.com/parasecurity/gnort_2.9.20
- Commission Recommendation (EU) 2017/1584 of 13 September 2017 on coordinated response to large-scale cybersecurity incidents and crises, https://eur-lex.europa.eu/eli/reco/2017/1584/oj
- HoneyChart: Automated Honeypot Management Over Kubernetes, 3rd International Workshop on Cyber-Physical Security for Critical Infrastructures Protection (CPS4CIP 2022)
- The much-publicized BlueKeep threat has finally emerged - why should you care? Available at: https://blog.avast.com/what-is-bluekeep
- G. Giakoumakis, E. Papadogiannaki, G. Vasiliadis and S. Ioannidis, "Pythia: Scheduling of Concurrent Network Packet Processing Applications on Heterogeneous Devices," 2020 6th IEEE Conference on Network Softwarization (NetSoft), 2020, pp. 145-149, doi: 10.1109/NetSoft48620.2020.9165447.