Ensemble Filter technique for Detection and Classification of attacks in Cloud Computing

Abstract

In all technologies, including traditional computing and cloud computing, security has always been the primary concern. In recent years, cloud computing has become widely accepted on a global scale. Cyber attacks aimed at it have increased along with its widespread acceptance. Although ample research is done in the security domain and cloud computing is based on rigid security fundamentals, the advancing network security attacks create the need for an advanced security mechanism. Also, the multiclass classification strategy has received very little attention, and classification accuracy can yet be improved. Hence, this work proposes an Ensemble Filter-based Intrusion Detection System (EFIDS) to address the limitations of previous research work. It not only identifies malicious traffic but also categorizes the attempted attacks (multiclass classification). The famous intrusion detection benchmark dataset, NSL KDD, is used to evaluate the model. Using the model, it was possible to enhance the classification accuracy of both binary and multiclass approaches up to 99.85 percent and 99.63 percent, respectively. Additionally, both forms of classification have shown a 65–70% improvement in training time.