| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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González, J.
Ministerio de Ciencia e Innovación
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (14/14 displayed)
- 2024Development of anisotropic Nd-Fe-B powder from isotropic gas atomized powder.
- 2020Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDNcitations
- 2020CaCu<sub>3</sub>Ti<sub>4</sub>O<sub>12</sub>: Pressure dependence of electronic and vibrational structurescitations
- 2019Pressure-induced spin transition and site-selective metallization in CoCl 2citations
- 2019Effect of stress and/or field annealing on the magnetic behavior of the „Co77Si13.5B9.5…90Fe7Nb3 amorphous alloycitations
- 2014Energy-efficient PEO process of aluminium alloyscitations
- 2014Annealing effect on the crystal structure and exchange bias in Heusler Ni45.5Mn43.0In11.5 alloy ribbonscitations
- 2012Correlation between the wear resistance, and the scratch resistance, for nanocomposite coatingscitations
- 2008Nanocrystallization by current annealing (with and without tensile stress) of Fe73.5−xNixSi13.5B9Nb3Cu1 alloy ribbons (x=5, 10, and 20citations
- 2006Soft magnetic behaviour of nanocrystalline Fe-based glass-coated microwires
- 2005Effect of stress and/or field annealing on the magnetic behavior of the (Co77Si13.5B9.5)90Fe7Nb3 amorphous alloycitations
- 2004Preparation and characterization of (CuInSe2)1-x(CoSe)x alloys in the composition range 0 x 2/3.citations
- 2000Magnetic and structural features of glass-coated Cu-based (Co,Fe,Ni,Mn–Cu) alloy microwirescitations
- 2000Stress induced magnetic anisotropy and coercivity in Fe73.5Cu1Ta3Si13.5B9 amorphous alloycitations
Places of action
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article
Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN
Abstract
Distributed Denial of Service (DDoS) is one of the most rampant attacks in the modern Internet of Things (IoT) network infrastructures. Security plays a very vital role for an ever-growing heterogeneous network of IoT nodes, which are directly connected to each other. Due to the preliminary stage of Software Defined Networking (SDN), in the IoT network, sampling based measurement approaches currently results in low-accuracy, higher memory consumption, higher-overhead in processing and network, and low attack-detection. To deal with these aforementioned issues, this paper proposes sFlow and adaptive polling based sampling with Snort Intrusion Detection System (IDS)and deep learning based model, which helps to lower down the various types of prevalent DDoS attacks inside the IoT network. The flexible decoupling property of SDN enables us to program network devices for required parameters without utilizingthird-party propriety based hardware or software. Firstly, in data-plane, to lower down processing and network overhead of switches, we deployed sFlow and adaptive polling based sampling individually. Secondly, in control-plane, to optimize detection accuracy, we deployed Snort IDS collaboratively with Stacked Autoencoders (SAE) deep learning model. Furthermore, after applying performance metrics on collected traffic streams, we quantitatively investigate trade\- off among attack detection accuracy and resources overhead. The evaluation of the proposed system demonstrates higher detection accuracy with 95\% of True Positive rate with less than 4\% of False Positive rate within sFlow based implementation compared to adaptive polling.