Intrusion Detection with Interpretable Rules Generated Using the Tsetlin Machine
| Autor: | Vladimir A. Oleshchuk, Sasanka N. Ranasinghe, Harsha S. Gardiyawasam Pussewalage, K. Darshana Abeyrathna, Ole-Christoffer Granmo |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
business.industry Computer science 0206 medical engineering Decision tree 02 engineering and technology Intrusion detection system Machine learning computer.software_genre Random forest Support vector machine Statistical classification Knowledge extraction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer 020602 bioinformatics Interpretability |
| Zdroj: | SSCI |
| Popis: | The rapid deployment in information and communication technologies and internet-based services have made anomaly based network intrusion detection ever so important for safeguarding systems from novel attack vectors. To this date, various machine learning mechanisms have been considered to build intrusion detection systems. However, achieving an acceptable level of classification accuracy while preserving the interpretability of the classification has always been a challenge. In this paper, we propose an efficient anomaly based intrusion detection mechanism based on the Tsetlin Machine (TM). We have evaluated the proposed mechanism over the Knowledge Discovery and Data Mining 1999 (KDD’99) dataset and the experimental results demonstrate that the proposed TM based approach is capable of achieving superior classification performance in comparison to several simple Multi-Layered Artificial Neural Networks, Support Vector Machines, Decision Trees, Random Forest, and K-Nearest Neighbor machine learning algorithms while preserving the interpretability. |
| Databáze: | OpenAIRE |
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