Research Article Open Access


G. Palanivel1 and K. Duraiswamy2
  • 1 Department of Electronics and Communication Engineering, University College of Engineering-Pattukottai, Rajamadam 614701, India
  • 2 Department of Computer Science and Engineering, K.S.Rangasamy College of Technology, Tiruchengode-637209, India


We propose an anomaly-based network intrusion detection system, which analyzes traffic features to detect anomalies. The proposed system can be used both in online as well as off-line mode for detecting deviations from the expected behavior. Although our approach uses network packet or flow data, it is general enough to be adaptable for use with any other network variable, which may be used as a signal for anomaly detection. It differs from most existing approaches in its use of wavelet transform for generating different time scales for a signal and using these scales as an input to a two-stage neural network predictor. The predictor predicts the expected signal value and labels considerable deviations from this value as anomalies. The primary contribution of our work would be to empirically evaluate the effectiveness of multi resolution analysis as an input to neural network prediction engine specifically for the purpose of intrusion detection. The role of Intrusion Detection Systems (IDSs), as special-purpose devices to detect anomalies and attacks in a network, is becoming more important. First, anomaly-based methods cannot achieve an outstanding performance without a comprehensive labeled and up-to-date training set with all different attack types, which is very costly and time-consuming to create if not impossible. Second, efficient and effective fusion of several detection technologies becomes a big challenge for building an operational hybrid intrusion detection system.

American Journal of Applied Sciences
Volume 11 No. 8, 2014, 1405-1411


Submitted On: 28 November 2013 Published On: 27 June 2014

How to Cite: Palanivel, G. & Duraiswamy, K. (2014). MULTI SCALE TIME SERIES PREDICTION FOR INTRUSION DETECTION. American Journal of Applied Sciences, 11(8), 1405-1411.

  • 0 Citations



  • Anomaly Detection
  • Two-Stage Neural Network Predictor
  • Multi-Resolution Analysis