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24.10.2019 | Research Article - Computer Engineering and Computer Science | Ausgabe 4/2020

Arabian Journal for Science and Engineering 4/2020

An Adaptive Threshold-Based Attribute Selection to Classify Requests Under DDoS Attack in Cloud-Based Systems

Zeitschrift:
Arabian Journal for Science and Engineering > Ausgabe 4/2020
Autoren:
Priyanka Verma, Shashikala Tapaswi, W. Wilfred Godfrey

Abstract

Cloud-based services are increasing day by day for various purposes due to its perpetuity and diverse dexterity. However, offensive network traffic such as distributed denial of service (DDoS) plays a significant role in threatening the cloud-based services. Therefore, defense for such attacks is required to save the cloud resources. Most of the attribute selection approaches for request classification are based on static threshold statistics. These statistics are the default values used to reduce the dimensionality of data. However, these default statistics do not work well with varying network conditions and for different intensities of DDoS attack. Therefore, an adaptive statistic is required to deal with different network and incoming traffic conditions. This paper also gives an comprehensive analysis of TCP, UDP and ICMP protocol-based DDoS attack and its effects on the cloud network. Based on the analysis and above issues, this paper presents an adaptive hybrid approach for attribute selection and classification of incoming traffic. The proposed approach consists of three subsystems such as (1) preprocessing subsystem, (2) adaptive attribute selection subsystem and (3) detection and prevention subsystem. The work utilizes NSL-KDD dataset which helps in the evaluation of the proposed approach. It is concluded that the combination of Mean Absolute Deviation technique with Random Forest classifier (MAD-RF) outperforms the other combinations. Therefore, MAD-RF is selected for further analysis. MAD-RF is also capable of dealing with TCP, UDP and ICMP protocol-based DDoS attack. The result shows that MAD-RF outperforms dimensionality reduction, traditional attribute selection methods and state-of-the-art approaches.

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