Software-Defined Networking-based Crypto Ransomware Detection Using HTTP Traffic Characteristics
Not on the map. Why?
Abstract (arXiv)
Ransomware is currently the key threat for individual as well as corporate Internet users. Especially dangerous is crypto ransomware that encrypts important user data and it is only possible to recover it once a ransom has been paid. Therefore devising efficient and effective countermeasures is a rising necessity. In this paper we present a novel Software-Defined Networking (SDN) based detection approach that utilizes characteristics of ransomware communication. Based on the observation of network communication of two crypto ransomware families, namely CryptoWall and Locky we conclude that analysis of the HTTP messages' sequences and their respective content sizes is enough to detect such threats. We show feasibility of our approach by designing and evaluating the proof-of-concept SDN-based detection system. Experimental results confirm that the proposed approach is feasible and efficient.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Nov 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.
2 papers.
- 2024Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sourcescited by 4
- 2021FedDICE: A ransomware spread detection in a distributed integrated clinical environment using federated learning and SDN based mitigationcited by 1
- 2024Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources23 Nov 2024
- 2021FedDICE: A ransomware spread detection in a distributed integrated clinical environment using federated learning and SDN based mitigation9 Jun 2021