| Title | Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Álvarez Almeida, Luis Alfredo, Carlos Martinez Santos, Juan |
| Conference Name | 2019 IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI) |
| Date Published | jun |
| Keywords | aforementioned data-set, big companies, classification model, Companies, composability, cyber-attacks, Data models, data set, Denial of Service attacks, digital industry, DoS attacks, economic repercussions, feature extraction, feature selection, global information security, Intrusion detection, Kernel, machine learning, Measurement, NSL-KDD data-set, Predictive Metrics, pubcrawl, Resiliency, security of data, support vector machine, support vector machine model, support vector machine-based intrusion detection system, Support vector machines |
| Abstract | The integrity of information and services is one of the more evident concerns in the world of global information security, due to the fact that it has economic repercussions on the digital industry. For this reason, big companies spend a lot of money on systems that protect them against cyber-attacks like Denial of Service attacks. In this article, we will use all the attributes of the data-set NSL-KDD to train and test a Support Vector Machine model. This model will then be applied to a method of feature selection to obtain the most relevant attributes within the aforementioned data-set and train the model again. The main goal is comparing the results obtained in both instances of training and validate which was more efficient. |
| DOI | 10.1109/ColCACI.2019.8781803 |
| Citation Key | alvarez_almeida_evaluating_2019 |
Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System