Incremental Support Vector Machine Approach for DoS and DDoS Attack Detection

Description

<div>
<div>
<div>
<p>Support Vector Machines (SVMs) have generally been effective in detecting instances of network intrusion. However, from a practical point of view, a standard SVM is not able to handle large-scale data efficiently due to the computation complexity of the algorithm and extensive memory requirements. To cope with the limitation, this study presents an incremental SVM method combined with a k-nearest neighbors (KNN) based candidate support vectors (CSV) selection strategy in order to speed up training and test process. The proposed incremental SVM method constructs or updates the pattern classes by incrementally incorporating new signatures without having to load and access the entire previous dataset in order to cope with evolving DoS and DDoS attacks. Performance of the proposed method is evaluated with experiments and compared with the standard SVM method and the simple incremental SVM method in terms of precision, recall, F1-score, and training and test duration.<br></p>
</div>
</div>
</div>

Links & Downloads

1. 10.25394/pgs.8044598.v1

Tags

Applied Computer Science

SVM

Incremental SVM

ISVM

KNN algorithm

Incremental learning

Intrusion Detection System (IDS)

Additional Fields

Identifer	oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8044598
Date	14 May 2019
Creators	Seunghee Lee (6636224)
Source Sets	Purdue University
Detected Language	English
Type	Text, Thesis
Rights	CC BY 4.0
Relation	https://figshare.com/articles/Incremental_Support_Vector_Machine_Approach_for_DoS_and_DDoS_Attack_Detection/8044598