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Georgios Androulidakis

Researcher at National Technical University of Athens

Publications -  21
Citations -  847

Georgios Androulidakis is an academic researcher from National Technical University of Athens. The author has contributed to research in topics: Anomaly detection & The Internet. The author has an hindex of 11, co-authored 21 publications receiving 755 citations. Previous affiliations of Georgios Androulidakis include National and Kapodistrian University of Athens.

Papers
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Journal ArticleDOI

Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments

TL;DR: This paper proposes a modular architecture for the separation of the data collection process from the SDN control plane with the employment of sFlow monitoring data and presents experimental results that demonstrate the effectiveness of the proposed sFlow-based mechanism compared to the native OF approach, in terms of overhead imposed on usage of system resources.
Journal ArticleDOI

Network anomaly detection and classification via opportunistic sampling

TL;DR: The inherently lossy sampling process is transformed to an advantageous feature in the anomaly detection case, allowing the revealing of anomalies that would be otherwise untraceable, and thus becoming the vehicle for efficient anomaly detection and classification.
Journal ArticleDOI

Improving network anomaly detection via selective flow-based sampling

TL;DR: A new flow-based sampling technique that focuses on the selection of small flows, which are usually the source of malicious traffic, is introduced and analysed and achieves to improve anomaly detection effectiveness and at the same time reduces the number of selected flows.
Proceedings ArticleDOI

Leveraging SDN for Efficient Anomaly Detection and Mitigation on Legacy Networks

TL;DR: This paper implemented and evaluated a sketch-based anomaly detection and identification mechanism, capable of pinpointing the victim and remotely triggering the mitigation of the offending network traffic, and demonstrated that the proposed approach succeeds in identifying the victim of the attack and efficiently filtering the malicious sources.