Detection of Signature-Based Attacks in Cloud Infrastructure Using Support Vector Machine

Abstract

AbstractIn modern years, cloud computing has arisen as a widely utilized innovation in IT area. With increase in the use of cloud computing, it has become more prone to intrusions. Indeed, even a little interruption assault can bargain whole framework; subsequently, interruption can be considered as a basic issue for cloud-based platforms. The substantial growth in the number of applications using cloud-based infrastructures calls for the need of security mechanisms for their protection. Intrusion detection systems are one of the most suitable security solutions for protecting cloud-based environments. The signature-based intrusion detection and support vector machine have emerged as a recent interest and research area. With their robust learning models and data-centric approach, SVM-based security solutions for cloud environments have been proven effective. Attack features are extracted from organization’s network and application logs. Attack presence is confirmed by performing support vector machine. Performance measures such as average detection time are used to evaluate the performance of the detection system.KeywordsMachine learningSVMCloud