Computer network systems are often subject to several types of attacks. For example the distributed Denial of Service (DDoS) attack introduces an excessive traffic load to a web server to make it unusable. A popular method for detecting attacks is to use the sequence of source IP addresses to detect possible anomalies. With the aim of predicting the next IP address, the Probability Density Function of the IP address sequence is estimated. Prediction of source IP address in the future access to the server is meant to detect anomalous requests. In other words, during an access to the server, only predicted IP addresses are permitted and all others are blocked. 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, normally used by combining the K-Means algorithm. Instead, in this paper we consider the sequence of IP addresses as a numerical sequence and develop the nonlinear analysis of the numerical sequence. We used nonlinear analysis based on Volterra’s kernels and Hammerstein’s models.
Supporting cyber attack detection via non-linear analytic prediction of IP addresses: A big data analytics technique / Cuzzocrea, A.; Mumolo, E.; Fadda, E.; Tessarotto, M.. - (2020), pp. 28-33. (Intervento presentato al convegno 26th International DMS Conference on Visualization and Visual Languages tenutosi a Pittsburgh, Virtual nel 7 July 2020 through 8 July 2020) [10.18293/DMSVIVA2020-018].
Supporting cyber attack detection via non-linear analytic prediction of IP addresses: A big data analytics technique
Fadda E.;
2020
Abstract
Computer network systems are often subject to several types of attacks. For example the distributed Denial of Service (DDoS) attack introduces an excessive traffic load to a web server to make it unusable. A popular method for detecting attacks is to use the sequence of source IP addresses to detect possible anomalies. With the aim of predicting the next IP address, the Probability Density Function of the IP address sequence is estimated. Prediction of source IP address in the future access to the server is meant to detect anomalous requests. In other words, during an access to the server, only predicted IP addresses are permitted and all others are blocked. 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, normally used by combining the K-Means algorithm. Instead, in this paper we consider the sequence of IP addresses as a numerical sequence and develop the nonlinear analysis of the numerical sequence. We used nonlinear analysis based on Volterra’s kernels and Hammerstein’s models.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2990630