Intelligent machine learning approach for cidscloud intrusion detection system

Title

Intelligent machine learning approach for cidscloud intrusion detection system

Creator

Sowmya T.; Muneeswari G.

Description

In this new era of information technology world, security in cloud computing has gained more importance because of the flexible nature of the cloud. In order to maintain security in cloud computing, the importance of developing an eminent intrusion detection system also increased. Researchers have already proposed intrusion detection schemes, but most of the traditional IDS are ineffective in detecting attacks. This can be attained by developing a new ML based algorithm for intrusion detection system for cloud. In the proposed methodology, a CIDS is incorporated that uses only selected features for the identification of the attack. The complex dataset will always make the observations difficult. Feature reduction plays a vital role in CIDS through time consumption. The current literature proposes a novel faster intelligent agent for data selection and feature reduction. The data selection agent selects only the data that promotes the attack. The selected data is passed through a feature reduction technique which reduces the features by deploying SVM and LR algorithms. The reduced features which in turn are subjected to the CIDS system. Thus, the overall time will be reduced to train the model. The performance of the system was evaluated with respect to accuracy and detection rate. Then, some existing IDS is analyzed based on these performance metrics, which in turn helps to predict the expected output. For analysis, UNSW-NB15 dataset is used which contains normal and abnormal data. The present work mainly ensures confidentiality and prevents unauthorized access. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

Source

Lecture Notes on Data Engineering and Communications Technologies, Vol-66, pp. 873-885.

Date

2021-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Cloud intrusion detection system; Feature selection; KLS framework; Machine learning; UNSW-NB15 dataset

Coverage

Sowmya T., School of Engineering and Technology CHRIST (Deemed to be University), Bangalore, India; Muneeswari G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23674512

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/978-981-16-0965-7_67

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114611150&doi=10.1007%2f978-981-16-0965-7_67&partnerID=40&md5=882b13c7e0696c7d005a4ae4d175e86a