This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset. First, issues in ...
This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset. First, issues in ...
First, issues in supervised classification are discussed, then the incorporation of anomaly detection enhancing the modeling and prediction of cyber-attacks ...
This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset. First, issues in ...
People also ask
How can SVM be used for anomaly detection?
What is anomaly detection in intrusion detection system?
What is anomaly detection in cyber security using machine learning?
How does IPS anomaly detection differ from misuse detection?
Focusing on this issue, in this paper, we propose a hybrid intrusion detection system using the decision tree and support vector machine (SVM) approaches. In ...
The results indicate the superiority of RSVMs not only in terms of high intrusion detection accuracy and low false positives but also in terms of their ...
This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset.
In this paper, a popular statistical learning algorithm known as the support vector machine (SVM) was consider to solve the network anomaly detection problem.
In this paper, we propose an effective intrusion detection framework based on SVM with naïve Bayes feature embedding.
The basic model of SVM is a linear classifier with maximal margin defined in the feature space. By introducing kernel methods, SVM can also realize nonlinear.