TY - JOUR
T1 - Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space
AU - Atli, Buse Gul
AU - Miche, Yoan
AU - Kalliola, Aapo
AU - Oliver, Ian
AU - Holtmanns, Silke
AU - Lendasse, Amaury
PY - 2018/10
Y1 - 2018/10
N2 - 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.
AB - 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.
KW - Extreme learning machine
KW - Hierarchical clustering
KW - Intrusion detection
KW - Network behavior analysis
KW - Probability density function
UR - http://www.scopus.com/inward/record.url?scp=85048044724&partnerID=8YFLogxK
U2 - 10.1007/s12559-018-9564-y
DO - 10.1007/s12559-018-9564-y
M3 - Article
AN - SCOPUS:85048044724
SN - 1866-9956
VL - 10
SP - 848
EP - 863
JO - Cognitive Computation
JF - Cognitive Computation
IS - 5
ER -