Detecting Network Intrusions Using Signal Processing with Query-Based Sampling Filter

This paper presents a novel approach for training a network intrusion detection system based on a query-based sampling (QBS) filter. The proposed QBS filter applies the concepts of data quantization to signal processing in order to develop a novel classification system. Through interaction with a partially trained classifier, the QBS filter can use an oracle to produce high-quality training data. We tested the method with a benchmark intrusion dataset to verify its performance and effectiveness. Results show that selecting qualified training data will have an impact not only on the performance but also on overall execution (to reduce distortion). This method can significantly increase the accuracy of the detection rate for suspicious activity and can recognize rare attacks. Additionally, the method can improve the efficiency of real-time intrusion detection models.

• Publication year

  2009

• Venue

  EURASIP Journal on Advances in Signal Processing

• Publication date

  Unknown publication date

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1155/2009/735283
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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