RePIDS: A multi tier Real-time Payload-based Intrusion Detection System

Aruna Jamdagni, Zhiyuan Tan, Xiangjian He, Priyadarsi Nanda, Ren Ping Liu

Research output: Journal PublicationReview articlepeer-review

64 Citations (Scopus)


Intrusion Detection System (IDS) deals with huge amount of network traffic and uses large feature set to discriminate normal pattern and intrusive pattern. However, most of existing systems lack the ability to process data for real-time anomaly detection. In this paper, we propose a 3-Tier Iterative Feature Selection Engine (IFSEng) for feature subspace selection. Principal Component Analysis (PCA) technique is used for the pre-processing of data. Mahalanobis Distance Map (MDM) is used to discover hidden correlations between the features and between the packets. We also propose a novel Real-time Payload-based Intrusion Detection System (RePIDS) that integrates a 3-Tier IFSEng and the MDM approach. Mahalanobis Distance (MD) dissimilarity criterion is used to classify each packet as either a normal or an attack packet. The effectiveness of the proposed RePIDS is evaluated using DARPA 99 dataset and Georgia Institute of Technology attack dataset. The traffic for Web-based application is considered for validating our model. F-value, a criterion, is used to evaluate the detection performance of RePIDS. Experimental results show that RePIDS achieves better performance (high F-values, 0.9958 for DARPA 99 dataset and 0.976 for Georgia Institute of Technology attack dataset respectively, with only 0.85% false alarm rate) and lower computational complexity when compared against two state-of-the-art payload-based intrusion detection systems. Additionally, it has 1.3 time higher throughput in comparison with real scenario of medium sized enterprise network.

Original languageEnglish
Pages (from-to)811-824
Number of pages14
JournalComputer Networks
Issue number3
Publication statusPublished - 26 Feb 2013
Externally publishedYes


  • Data pre-processing
  • Intrusion detection
  • Iterative feature selection
  • Mahalanobis Distance Map
  • Principal component analysis
  • Principal components

ASJC Scopus subject areas

  • Computer Networks and Communications


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