| Title | A Robust Support Vector Machine Based Auto-Encoder for DoS Attacks Identification in Computer Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Allagi, Shridhar, Rachh, Rashmi, Anami, Basavaraj |
| Conference Name | 2021 International Conference on Intelligent Technologies (CONIT) |
| Keywords | attack vectors, composability, Computational modeling, computer networks, DoS, feature extraction, Human Behavior, Intrusion detection, intrusion detection system, machine learning, Predictive Metrics, pubcrawl, Real-time Systems, Resiliency, Scalability, support vector machine classification, Support vector machines, threat vectors |
| Abstract | An unprecedented upsurge in the number of cyberattacks and threats is the corollary of ubiquitous internet connectivity. Among a variety of threats and attacks, Denial of Service (DoS) attacks are crucial and conventional mechanisms currently being used for detection/ identification of these attacks are not adequate. The use of real-time and robust mechanisms is the way to handle this. Machine learning-based techniques have been extensively used for this in the recent past. In this paper, a robust mechanism using Support Vector Machine Based Auto-Encoder is proposed for identifying DoS attacks. The proposed technique is tested on the CICIDS dataset and has given 99.32 % accuracy for DoS attacks. To study the effect of the number of features on the performance of the technique, a discriminant component analysis is deployed for feature reduction and independent experiments, namely SVM with 25 features, SVM with 30 features, SVM with 35 features, and PCA-SVM with 25 features, are conducted. From the experiments, it is observed that AE-SVM has performed better than others. |
| DOI | 10.1109/CONIT51480.2021.9498284 |
| Citation Key | allagi_robust_2021 |
A Robust Support Vector Machine Based Auto-Encoder for DoS Attacks Identification in Computer Networks