Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space

Buse Gul Atli, Yoan Miche*, Aapo Kalliola, Ian Oliver, Silke Holtmanns, Amaury Lendasse

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

26 Sitaatiot (Scopus)

Abstrakti

Recently, with the increased use of network communication, the risk of compromising the information has grown immensely. Intrusions have become more sophisticated and few methods can achieve efficient results while the network behavior constantly changes. This paper proposes an intrusion detection system based on modeling distributions of network statistics and Extreme Learning Machine (ELM) to achieve high detection rates of intrusions. The proposed model aggregates the network traffic at the IP subnetwork level and the distribution of statistics are collected for the most frequent IPv4 addresses encountered as destination. The obtained probability distributions are learned by ELM. This model is evaluated on the ISCX-IDS 2012 dataset, which is collected using a real-time testbed. The model is compared against leading approaches using the same dataset. Experimental results show that the presented method achieves an average detection rate of 91% and a misclassification rate of 9%. The experimental results show that our methods significantly improve the performance of the simple ELM despite a trade-off between performance and time complexity. Furthermore, our methods achieve good performance in comparison with the other few state-of-the-art approaches evaluated on the ISCX-IDS 2012 dataset.

AlkuperäiskieliEnglanti
Sivut848–863
Sivumäärä16
JulkaisuCognitive Computation
Vuosikerta10
Numero5
Varhainen verkossa julkaisun päivämäärä5 kesäkuuta 2018
DOI - pysyväislinkit
TilaJulkaistu - lokakuuta 2018
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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