| Title | Comparison of Different Machine Learning Algorithms Based on Intrusion Detection System |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Dixit, Utkarsh, Bhatia, Suman, Bhatia, Pramod |
| Conference Name | 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) |
| Date Published | may |
| Keywords | composability, Decision Tree, Intrusion detection, intrusion detection system, KDD99, machine learning, machine learning algorithms, parallel processing, privacy, Programming, pubcrawl, Radio frequency, Random Forest, resilience, Resiliency, support vector machine, Support vector machines, Training |
| Abstract | An IDS is a system that helps in detecting any kind of doubtful activity on a computer network. It is capable of identifying suspicious activities at both the levels i.e. locally at the system level and in transit at the network level. Since, the system does not have its own dataset as a result it is inefficient in identifying unknown attacks. In order to overcome this inefficiency, we make use of ML. ML assists in analysing and categorizing attacks on diverse datasets. In this study, the efficacy of eight machine learning algorithms based on KDD CUP99 is assessed. Based on our implementation and analysis, amongst the eight Algorithms considered here, Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT) have the highest testing accuracy of which got SVM does have the highest accuracy |
| DOI | 10.1109/COM-IT-CON54601.2022.9850515 |
| Citation Key | dixit_comparison_2022 |
Comparison of Different Machine Learning Algorithms Based on Intrusion Detection System