Collaborative Network Defense

Collaborative Network Defense

With cyber attacks increasing in both their numbers and their sophistication, there is an urgent need for innovative defense mechanisms. At SPIN, we conduct research in collaborative network defense by emphasizing in novel detection and collaboration systems in the context of intrusion detection systems, honeypots as well as other network defense cutting edge technologies (e.g., utilizing machine learning to detect network traffic anomalies).

Responsible Researchers

  • Dr. Emmanouil Vasilomanolakis
  • Leon Böck
  • Carlos Garcia Cordero

Selected Publications

• Sphinx: a Colluder-Resistant Trust Mechanism for Collaborative Intrusion Detection (IEEE ACCESS 2018)

• Towards Blockchain-Based Collaborative Intrusion Detection Systems (CRITIS 2017)

• Analyzing flow-based anomaly intrusion detection using Replicator Neural Networks (PST 2016)

• Towards the creation of synthetic, yet realistic, intrusion detection datasets (NOMS 2016)

• Taxonomy and survey of collaborative intrusion detection (ACM CSUR 2015)


Related Projects / Funding