A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
A Deep Auto-Encoder based Approach for Intrusion Detection System
Tekijät: Fatimeh Farahnakian, Jukka Heikkonen
Toimittaja: IEEE
Konferenssin vakiintunut nimi: International Conference on Advanced Communications Technology
Julkaisuvuosi: 2018
Kokoomateoksen nimi: 2018 20th International Conference on Advanced Communication Technology (ICACT)
Tietokannassa oleva lehden nimi: 2018 20TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT)
Lehden akronyymi: INT CONF ADV COMMUN
Aloitussivu: 178
Lopetussivu: 183
Sivujen määrä: 6
ISBN: 978-1-5386-4688-5
eISBN: 978-11-88428-01-4
ISSN: 1738-9445
DOI: https://doi.org/10.23919/ICACT.2018.8323688
Tiivistelmä
One of the most challenging problems facing network operators today is network attacks identification due to extensive number of vulnerabilities in computer systems and creativity of attackers. To address this problem, we present a deep learning approach for intrusion detection systems. Our approach uses Deep Auto-Encoder (DAE) as one of the most well-known deep learning models. The proposed DAE model is trained in a greedy layer-wise fashion in order to avoid overfitting and local optima. The experimental results on the KDD-CUP'99 dataset show that our approach provides substantial improvement over other deep learning-based approaches in terms of accuracy, detection rate and false alarm rate.
One of the most challenging problems facing network operators today is network attacks identification due to extensive number of vulnerabilities in computer systems and creativity of attackers. To address this problem, we present a deep learning approach for intrusion detection systems. Our approach uses Deep Auto-Encoder (DAE) as one of the most well-known deep learning models. The proposed DAE model is trained in a greedy layer-wise fashion in order to avoid overfitting and local optima. The experimental results on the KDD-CUP'99 dataset show that our approach provides substantial improvement over other deep learning-based approaches in terms of accuracy, detection rate and false alarm rate.
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