Detecting network intrusions using signal processing with query-based sampling Filter
Journal
Eurasip Journal on Advances in Signal Processing
Journal Volume
2009
Date Issued
2009
Author(s)
Abstract
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.
SDGs
Other Subjects
Benchmarking; Computer crime; Internet; Security of data; Signal processing; Classification systems; Data sets; Detection rates; High qualities; Intrusion detection models; Network intrusion detection systems; Network intrusions; Overall executions; Training datum; Intrusion detection
Type
journal article