@article{scholars7879,
            note = {cited By 28; Conference of International Conference on Machine Learning and Signal Processing, MALSIP 2015 ; Conference Date: 12 June 2015 Through 14 June 2015; Conference Code:176019},
       publisher = {Springer Verlag},
         journal = {Lecture Notes in Electrical Engineering},
           title = {Ant colony optimization and feature selection for intrusion detection},
           pages = {305--312},
          volume = {387},
             doi = {10.1007/978-3-319-32213-1{$_2$}{$_7$}},
            year = {2016},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975887120&doi=10.1007\%2f978-3-319-32213-1\%5f27&partnerID=40&md5=31c50b4594eeada395944dbd6a4df89a},
            isbn = {9783319322124},
        abstract = {Network intrusion detection gained a lot of attention from the security expert. Intrusion detection system has been designed for the purpose detecting attack and comprises of detection method that can be anomaly based or it can be signature based. These detection method, however, highly depends on the quality of the input features. Supervised learning approach for the detection method finds the relationship between the feature and its class. Therefore, irrelevant, redundant, and noisy features must be eliminated before applying supervised algorithm. This can be done by feature selection method. In this paper ant colony optimization has been applied for feature selection on KDD99 dataset. The reduced dataset is validated using support vector machine. Results show that accuracy of the SVM is significantly improved with reduced feature set. {\^A}{\copyright} Springer International Publishing Switzerland 2016.},
        keywords = {Ant colony optimization; Artificial intelligence; Feature extraction; Learning systems; Mercury (metal); Signal detection; Signal processing; Support vector machines, Detecting attacks; Detection methods; Feature selection methods; Intrusion Detection Systems; Network intrusion detection; Security experts; Supervised algorithm; Supervised learning approaches, Intrusion detection},
            issn = {18761100},
          author = {Mehmod, T. and Rais, H. B. M.}
}