Hybrid Deep Learning Cloud Intrusion Detection

Title

Hybrid Deep Learning Cloud Intrusion Detection

Creator

Karnan L.; Mahalakshmi S.B.; Thirunavukkarasu V.

Description

The scalability and flexibility that cloud computing provides, organisations can readily adapt their resources to meet demand without having to make significant upfront expenditures in hardware infrastructure. Three main types of computing services are provided to people worldwide via the Internet. Increased performance and resource access are two benefits that come with using cloud computing, but there is also an increased chance of attack. As a result of this research, intrusion detection systems that can process massive amounts of data packets, analyse them, and produce reports using knowledge and behaviour analysis were created. Convolution Neural Network Algorithm encrypts data as it's being transmitted end-to-end and is stored in the cloud, providing an extra degree of security. Data protection in the cloud is improved by intrusion detection. This study uses a model to show how data is encrypted and decrypted, of an algorithm and describes the defences against attacks. When assessing the performance of the suggested system, it's critical to consider the time and memory needed to encrypt and decrypt big text files. Additionally, the security of the cloud has been investigated and contrasted with various encoding techniques now in use. 2024 IEEE.

Source

2024 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024 - Proceedings

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

-CNN; Cloud Security; Intrusion detection; k-NN DDoS

Coverage

Karnan L., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Mahalakshmi S.B., Coimbatore Institute of Technology, Department of Artificial Intelligence & Machine Learning, Tamil Nadu, Coimbatore, 641014, India; Thirunavukkarasu V., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India

Rights

Restricted Access

Relation

ISBN: 979-835036118-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/INCOS59338.2024.10527729

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194048134&doi=10.1109%2fINCOS59338.2024.10527729&partnerID=40&md5=5a550f81f2ae52e2af0c65a23b4a11fd