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Cyber-attack detection via non-linear prediction of IP addresses: an innovative big data analytics approach

Abstract : Abstract Computer network systems are often subject to several types of attacks. For example, an excessive traffic load sent to a web server for making it unusable is the main technique introduced by the Distributed Denial of Service (DDoS) attack. A well-known method for detecting attacks consists in analyzing the sequence of source IP addresses for detecting possible anomalies. With the aim of predicting the next IP address, the Probability Density Function of the IP address sequence is estimated. Anomalous requests are detected via predicting source’s IP addresses in future accesses to the server. Thus, when an access to the server occurs, the server accepts only the requests from the predicted IP addresses and it blocks all the others. The approaches used to estimate the Probability Density Function of IP addresses range from the sequence of IP addresses seen previously and stored in a database to address clustering, for instance via the K-Means algorithm. Instead, the sequence of IP addresses is considered as a numerical sequence in this paper, and non-linear analysis of this numerical sequence is applied. In particular, we exploited non-linear analysis based on Volterra Kernels and Hammerstein models. The experiments carried out with datasets of source IP address sequences show that the prediction errors obtained with Hammerstein models are smaller than those obtained both with the Volterra Kernels and with the sequence clustering based on the K-Means algorithm.
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https://hal.univ-lorraine.fr/hal-03352525
Contributor : Maira Nassau Connect in order to contact the contributor
Submitted on : Thursday, September 23, 2021 - 11:48:06 AM
Last modification on : Friday, April 1, 2022 - 3:44:21 AM

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Alfredo Cuzzocrea, Edoardo Fadda, Enzo Mumolo. Cyber-attack detection via non-linear prediction of IP addresses: an innovative big data analytics approach. Multimedia Tools and Applications, Springer Verlag, 2021, ⟨10.1007/s11042-021-11390-1⟩. ⟨hal-03352525⟩

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