Abstract
Due to the advancement in information exchange over the Internet and mobile technologies, malicious network attacks have significantly increased. Machine learning algorithms can play a vital role in network security and attacks classification. This paper compares two different types of classifiers (Naive Bayes and Decision Tree) for the intrusion detection system on the publicly available dataset. Simulations are carried out using the WEKA machine learning tool and experimentation is performed on full data and selected features using subset evaluator algorithm. The classifier performance is evaluated in terms of accuracy, specificity, recall, precision, f1-score, error rates and response time. Naive Bayes classifier performance was better in terms of computational time, however, the accuracy, error rate, f1-score, and recall values of Decision Tree were better than Naive Bayes.
Original language | English |
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Title of host publication | 2020 14th International Conference on Innovations in Information Technology (IIT) |
Publisher | IEEE |
Pages | 198-202 |
Number of pages | 5 |
ISBN (Electronic) | 9781728181844 |
ISBN (Print) | 9781728181851 |
DOIs | |
Publication status | Published - 25 Dec 2020 |
Event | IEEE 14th International Conference on Innovations in Information Technology (IIT'20) - Online Duration: 17 Nov 2020 → 18 Nov 2020 https://conferences.uaeu.ac.ae/iit20/en/ (Link to Conference website) |
Conference
Conference | IEEE 14th International Conference on Innovations in Information Technology (IIT'20) |
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Abbreviated title | IIT'20 |
Period | 17/11/20 → 18/11/20 |
Internet address |
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Keywords
- machine learning
- feature extraction
- intrusion detection system
- subset evaluator
ASJC Scopus subject areas
- Information Systems and Management
- Artificial Intelligence
- Information Systems
- Safety, Risk, Reliability and Quality
- Computer Science Applications