Abstract
![CDATA[Software-Defined Networks (SDN) initiates a novel networking model. SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. The architecture enhances the network resilient, decompose management complexity, and support more straightforward network policies enforcement. However, the model suffers from severe security threats. Specifically, a centralized network controller is a precious target for two reasons. First, the controller is located at a central point between the application and data planes. Second, a controller is software which prone to vulnerabilities, e.g., buffer and stack overflow. Hence, providing security measures is a crucial procedure towards the fully unleash of the new model capabilities. Intrusion detection is an option to enhance the networking security. Several approaches were proposed, for instance, signature-based, and anomaly detection. Anomaly detection is a broad approach deployed by various methods, e.g., machine learning. For many decades intrusion detection solution suffers performance and accuracy deficiencies. This paper revisits network anomalies detection as recent advances in machine learning particularly deep learning proofed success in many areas like computer vision and speech recognition. The study proposes an intrusion detection framework based on unsupervised deep learning algorithms.]]
Original language | English |
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Title of host publication | Proceedings of the 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops: IEEE WAINA 2018, 16-18 May 2018, Krakow, Poland |
Publisher | IEEE |
Pages | 709-714 |
Number of pages | 6 |
ISBN (Print) | 9781538653944 |
DOIs | |
Publication status | Published - 2018 |
Event | International Conference on Advanced Information Networking and Applications - Duration: 16 May 2018 → … |
Conference
Conference | International Conference on Advanced Information Networking and Applications |
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Period | 16/05/18 → … |
Keywords
- computer networks
- machine learning
- security measures
- software-defined networking (computer network technology)