Three Levels Network Analysis For Anomaly Detection

Anomaly detection is fundamental to ensure reliability and security in computer networks. In this work, it is proposed an anomaly detection system that monitors the network in three different levels. In the first one, data is collected from Simple Network Management Protocol (SNMP) objects and compared to profiles of normal traffic, in order to detect behavior changes. Second level of analysis includes a dependency graph that represents the relationships between SNMP objects. It is used to analyze first level alerts, confirming the occurrence of anomalies in device level. In the third level of analysis, second level alerts are grouped according to network topology information, and network administrators are informed about the context where the anomaly occurred. Tests were performed in a real network environment and good results were obtained.281285 IEEE Communications SocietyPark, A.P.E.J.M., An overview of anomaly detection techniques: Existing solutions and latest technological trends (2007) Computer Networks, V. 51 (12), pp. 3448-3470Thottan, M., Ji, C., Anomaly detection in IP networks (2003) IEEE Transactions in Signal Processing, V. 51 (8), pp. 2191-2204Denning, D.E., An intrusion-detection model (1987) IEEE Transactions on Software Engineering, 13 (2), pp. 222-232Barford, P., Kline, J., Plonka, D., Ron, A., A signal analysis of network traffic anomalies (2002) Proceedings of the 2nd ACM SIGCOMM Workshop on Internet Measurement, pp. 71-82Lakhina, A., Crovella, M., Diot, C., Diagnosing network- wide traffic anomalies (2004) ACM SIGCOMM Computer Communication Review, Proc. of the 2004 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, 34, pp. 219-230Kim, S.S., Reddy, A.L.N., Statistical techniques for detecting traffic anomalies through packet header data (2008) IEEE/ACM Transactions on Networking, 16 (3)Androulidakis, G., Chatziqiannakis, V., Papavassiliou, S., Network anomaly detection and classification via opportunistic sampling (2009) IEEE Network, 23 (1), pp. 6-12Ringberg, H., Soule, A., Rexford, J., Diot, C., Sensitivity of PCA for traffic anomaly detection (2007) Proceedings of the 2007 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, pp. 109-120Farraposo, S., Owezarski, P., Monteiro, E.E., A multi- scale tomographic algorithm for detecting and classifying traffic anomalies (2007) Proceedings of IEEE International Conference on Communications 2007, pp. 363-370Stallings, W., (1998) SNMP, SNMPv2, SNMPv3, and RMON 1, 2 and 3, , Addison-WesleyMccloghrie, K., Rose, M., Management information base for network management of TCP/IP-based internet: MIB-II (1991) RFC 1213, , marProença Jr., M.L., Coppelmans, C., Bottoli, M., Mendes, L.S., The hurst parameter for digital signature of network segment (2004) 11th International Conference on Telecommunications (ICT 2004), pp. 772-78