Abstract
Because they’re facilitating life, using computers and other intelligent devices associated with internet has become vital in those days. Banking transactions, education, trade marketing, texting … are all daily and important operations that relies on such technology. Information systems that handle those operations must be kept secure from any intrusive activity. To help ensure that, we must take into consideration several subjects such as access control by managing confidentiality, integrity and availability, as well as deploying detection and prevention tools and mechanisms that help preparing for and dealing with attacks. In this perspective, we propose a network intrusion detection model based on multiclass logistic regression (MLR) and Correlation-based feature selection (CFS). Results will be discussed with respect to NSL-KDD Dataset, and compared to other techniques based on various classification methods.
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Ait tchakoucht, T., Ezziyyani, M. (2018). Detecting Network Intrusions Using Multi-class Logistic Regression and Correlation-Based Feature Selection. In: Ezziyyani, M., Bahaj, M., Khoukhi, F. (eds) Advanced Information Technology, Services and Systems. AIT2S 2017. Lecture Notes in Networks and Systems, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-69137-4_37
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DOI: https://doi.org/10.1007/978-3-319-69137-4_37
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