CISSAN

Collective intelligence supported by security aware nodes

CISSAN will build methods and technology for integrating collective security intelligence to IoT networks. CISSAN-powered networks will be able to collaboratively identify tampered and adversarial devices, detect malicious activities, and select effective countermeasures. Higher IoT network resilience will be accompanied by resource efficiency through intelligent distribution of security functionalities across network nodes.

Table of contents

Project duration
-
Core fields of research
Information technology and the human in the knowledge society
Research areas
Cybersecurity
Information systems
Faculty
Faculty of Information Technology

Project description

The proliferation of Internet of Things (IoT) with its smart devices has fundamentally changed how different environments, such as homes, offices, factories, smart buildings, and smart grids, are used and operated. However, as stated in the Technical Paper on Internet of Things (IoT), security is a major concern for IoT networks and environments, where the risks of physical device tampering, injection of malicious devices, and unpatched vulnerabilities are higher than in traditional networks. This is nicely captured in the Hyppönen’s law : “If it’s smart, it’s vulnerable.” Following “when everything is connected, everything must be protected”, CISSAN proposes and implements algorithms for mitigating IoT security threats (good reviews of which can be found in Technical Paper on Internet of Things (IoT) and IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework) through collective decision-making and with a reduced impact on the limited resources of IoT devices. These algorithms are based on research and innovation in optimizing the distribution of security capabilities and aggregating the intelligence in IoT network nodes. Three industrial use cases, which nowadays heavily rely on the use of IoT, inform the project developments and are used for validating and demonstrating the project results: (i) public transportation; (ii) smart energy grids; (iii) mining and tunnelling operations.

Partner Organizations

Publications

Publication | 2025

DiBSeLO: Distributed IoT Blacklisting and Server Location Optimization in Rayleigh Fading Channels

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

Tarlan, Ozan ; Şafak, Ilgin ; Kalkan, Kubra

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Publication | 2025

Dynamic time warping for classifying long-term trends in time series

Data & Knowledge Engineering

Glock, Anna-Christina ; Chmelina, Klaus ; Fürnkranz, Johannes ; Hütter, Thomas

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Publication | 2025

Aging-Based Weighting for Session Classification in User Behavior Analysis

Computational Science and Its Applications – ICCSA 2025 Workshops

Taşgetiren, Nail ; Şafak, Ilgın  ; Aktaş, Mehmet S.

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Publication | 2024

Synthetic Data Generation in Cybersecurity: A Comparative Analysis

Ammara, Dure Adan ; Ding, Jianguo ; Tutschku, Kurt

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Publication | 2023

An Architecture for Enabling Collective Intelligence in IoT Networks

Lecture Notes in Computer Science

Frantti, Tapio ; Safak, Ilgin

News

CELTIC-NEXT 01/2025 Newsletter

The CELTIC-NEXT Eureka Cluster 01/2025 newsletter included a project highlight of the CISSAN project detailing the project's motivation, use cases and lab setup. In addition, the project highlight contained information on synthetic network traffic data generation and details on the developed proof of concept for the collective intelligence.

CISSAN project Mid-term Review

The CISSAN project Mid-term Review was held in Vienna on the 16th of January, 2025. The project partners presented their results on different forms of anomaly detection, training and use of Large Language Models and Machine Learning models as well as first experiments  with collective intelligence. The feedback from the CELTIC-NEXT reviewers was positive.

CISSAN project members at the mid-term review

Driven by Data

Excerpt taken from: Tunneling Journal Online Issue, September 2024 (p. 16)

The company is continuing to develop AI-based technologies to assess the quality of monitoring data through participation in the cybersecurity-focused EUREKA Celtic Next research project CISSAN. They are developing a "data quality and security verification system allowing the detection of suspicious data, a data signing system using security chips, and a blockchain-based data verification system using the blockchain to guarantee manipulation free monitoring data", said Chmelina. All three systems are currently under development with prototypes to be expected in early 2025 and incorporation into Geodata's cloud monitoring platform Geodatahub (formerly named Kronos) in early 2026.

Geodata_ GeomechanikKolloquiumConference_booth

Geodata is presenting their CISSAN work in their booth at the Geomechanik Kolloquium conference in Salzburg (Austria) on October 10 -11 2024  (https://www.geomechanics-congress.com/en/program#gmk).

This includes the technical efforts in WP4 on the data quality verification method and system and on sensor data integrity protection (using both data signing and blockchain-based techniques) and a plan for integrating these new capabilities to Geodata monitoring products.

Geodata_GeomechanikKolloquiumConference_slide