Majority Vote-Based Ensemble Approach for Distributed Denial of Service Attack Detection in Cloud Computing

Abstract

Cloud computing is considered as technical advancement in information technology. Many organizations have been motivated by this advancement to outsource their data and computational needs. Such platforms are required to fulfil basic security principles such as confidentiality, availability, and integrity. Cloud computing offers scalable and virtualized services with a high flexibility level and decreased maintenance costs to end-users. The infrastructure and protocols that are behind cloud computing may contain bugs and vulnerabilities. These vulnerabilities are being exploited by attackers, leading to attacks. Among the most reported attacks in cloud computing are distributed denial-of-service (DDOS) attacks. DDOS attacks are conducted by sending many data packets to the targeted infrastructure. This leads to most network bandwidth and server time being consumed, thus causing a denial of the service problem. Several methods have been proposed and experimented with for early DDOS attack detection. Employing a single machine learning classification model may give an adequate level of attack detection accuracy but needs an enhancement. In this study, we propose an approach based on an ensemble of machine learning classifiers. The proposed approach uses a majority vote-based ensemble of classifiers to detect attacks more accurately. A subset of the CICDDOS2019 dataset consisting of 32,000 instances, including 8450 benign and 23,550 DDOS attack instances was used in this study for results and evaluation. The experimental results showed that 98.02% accuracy was achieved with 97.45% sensitivity and 98.65% specificity.

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

“…Alduailij et al [1] proposed MI and RFF, which was helpful to reduce misclassifcation errors by using various classifers. However, this method failed to examine with DL-based detection and this DL-based detection was enhanced in [9]. Alqarni [9] introduced the ensemble approach for DDoS detection that limited the size of the feature and dataset producing higher performance.…”

“…However, this method failed to examine with DL-based detection and this DL-based detection was enhanced in [9]. Alqarni [9] introduced the ensemble approach for DDoS detection that limited the size of the feature and dataset producing higher performance. Here, drawbacks prevailed in its time of execution, which lasted for more time.…”

“…(ii) In method [1], MI feature selection only was utilized as this required much time with increased data dimensions for detecting an attack, whereas other feature selection techniques, such as wrapper and sequential feature selection, were not adopted for detecting DDoS and various other attacks. (iii) Te ensemble approach in [9] utilized decision trees, naive Bayes, K-NN, and SVMs as base classifers for detecting DDoS in cloud computing with high accuracy; however, other classifers used in this method, performed less in detection. (iv) CIC-DDoS2019 dataset used in [6]…”

“…Tey disturb regular and normal trafc on a network via sudden exponential upsurge in trafc and lastly prevent regular trafc from attaining its terminus. DDoS is considered as a type of malicious attack on cloud servers that creates many severe problems [9]. Tese attacks generate large network trafc containing packets sent on the network, making regular users in trouble who want to obtain services that not respond to their requirements [6,7].…”

Analysis of Network Security Countermeasures From the Perspective of Improved FS Algorithm and ICT Convergence

“…As mankind enters the era of knowledge economy, informatization is transforming from a technological revolution to an industrial revolution, and has become an irreversible trend of economic and social development [1,2]. At present, informatization has become an important driving force for promoting China's urban development, bringing the required information resources to urban economic development, and injecting new vitality into the development of urban economy.…”