Implementation of Block-Level Double Encryption Based on Machine Learning Techniques for Attack Detection and Prevention. (9th July 2022)

• Record Type: Journal Article
• Authors: Gnanavel, S.; Narayana, K. E.; Jayashree, K.; Nancy, P.; Teressa, Dawit Mamiru
• Other Names: Hashmi Mohammad Farukh Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-07-09
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/4255220
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Cloud computing is one of the most important business models of modern information technology. It provides a minimum of various services to the user interaction and low cost (hardware and software). Cloud services are based on the newline architectures on virtualization by using the multitenancy for better resource management and newline strong isolation between several virtual machines (VMs). The spying on a victim VM is challenging, particularly when one wants to use per-core microarchitectural features as a side channel. For example, the cache contains the most potential for damaging side channels, but shared information across different cores affects the cloud information. To overcome this problem, propose the Secure Block-Level Double Encryption (SBLDE) algorithm for user signature verification in the cloud server. It uses identity-based detection techniques to monitor the colocated VMs to identify abnormal cache data and channel behaviors typically during VM data transformation. The identity-based linear classification (IBLC) method is used for classifying the attacker channel when the data is transferred/retrieved from the VM cloud server. This cloud controller finds the channel misbehavior to block the port or channel, changing other available ports' communication. The service verification provides strong user access permission on the cloud server when the unknown request to the cloud server suddenly executes the key authentication to verify the user permission. This linear classification trains the existing side-channel attack datasets to the classifier and identifies the VM cloud's attack channel. The study focused on preventing attacks from interrupting the system and serves as an effective means for cross-VM side-channel attacks. This proposed method protects the cloud data and prevents cross-VM channel attack detection efficiently, compared to other existing methods. In this overall proposed method, SBLDE's performance is to be evaluated and then compared with the existing method.