Optimization Enabled Deep Learning-Based DDoS Attack Detection in Cloud Computing

Abstract

Cloud computing is a vast revolution in information technology (IT) that inhibits scalable and virtualized sources to end users with low infrastructure cost and maintenance. They also have much flexibility and these resources are supervised by various management organizations and provided over the Internet by known standards, formats, and networking protocols. Legacy protocols and underlying technologies consist of vulnerabilities and bugs which open doors for intrusion by network attackers. Attacks as distributed denial of service (DDoS) are one of most frequent attacks, which impose heavy damage and affect performance of the cloud. In this research work, DDoS attack detection is easily identified in an optimized way through a novel algorithm, namely, the proposed gradient hybrid leader optimization (GHLBO) algorithm. This optimized algorithm is responsible to train a deep stacked autoencoder (DSA) that detects the attack in an efficient manner. Here, fusion of features is carried out by deep maxout network (DMN) with an overlap coefficient, and augmentation of data is carried out by the oversampling process. Furthermore, the proposed GHLBO is generated by integrating the gradient descent and hybrid leader-based optimization (HLBO) algorithm. Also, this proposed method is assessed by various performance metrics, such as the true positive rate (TPR), true negative rate (TNR), and testing accuracy with values attained as 0.909, 0.909, and 0.917, accordingly.

An Innovative approach to Improve the Quality of Pharmaceuticals approach using Cloud Computing

“…The Azure service, within the realm of information technology, plays a crucial role in facilitating pharmaceutical compliance and change management. The present study aims to investigate the efficacy of Microsoft Azure Services as the exclusive solution for expediting GxP validation processes, specifically regarding certification acceleration and assistance provided to manufacturers in testing applications to meet specific standards [6].…”

Evaluating Machine Learning and Deep Learning Models for Enhanced DDoS Attack Detection

“…Publishing Year Methodology Result [11] 2020 Deep CNN ensemble framework Detecting DDoS attacks effectively in SDNs [12] 2023 Optimisation-enabled deep learning-based DDoS attack detection Detecting DDoS attacks in cloud computing [13] 2022 Machine learning, mutual information Using RF to detect DDoS attacks RF feature importance method [14] 2021 Optimised extreme learning machine DDoS attack detection in cloud computing [15] 2022 Majority vote-based ensemble approach Detecting DDoS Attacks in cloud computing [16] 2020 Distributed deep learning system Detecting web attacks on edge devices [17] 2020 Fuzzy-Taylor-elephant herd optimization Deep Belief DDoS attack detection Inspired deep belief network [18] 2017 Machine learning techniques Cloud computing DDoS attack detection [19] 2022 Detection of DDoS attacks using IDS mechanism A review [20] 2021 Survey of authentication and privacy schemes Automotive ad hoc networks [21] 2022…”

A Realizable Data Encryption Strategy

“…Numerous additional uses in a diagnostic picture, depend on similar temporal properties. The method for obtaining these characteristics is presented in the study [4]. DDoS attack source traceback is an unsolved and difficult issue.…”