Affiliation:
1. Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi 110006, India
2. Department of Income Tax (Systems), Delhi, India
Abstract
Cloud computing security has been a critical issue with its increase in demand. One of the most challenging problems in cloud computing is detecting distributed denial-of-service (DDoS) attacks. The attack detection framework for the DDoS attack is tricky because of its nonlinear nature of interruption activities, atypical system traffic behaviour, and many features in the problem space. As a result, creating defensive solutions against these attacks is critical for mainstream cloud computing adoption. In this novel research, by using performance parameters, perplexed-based classifiers with and without feature selection will be compared with the existing machine learning algorithms such as naïve Bayes and random forest to prove the efficacy of the perplexed-based classification algorithm. Comparing the performance parameters like accuracy, sensitivity, and specificity, the proposed algorithm has an accuracy of 99%, which is higher than the existing algorithms, proving that the proposed algorithm is highly efficient in detecting the DDoS attacks in cloud computing systems. To extend our research in the area of nature-inspired computing, we compared our perplexed Bayes classifier feature selection with nature-inspired feature selection like genetic algorithm (GA) and particle swarm optimization (PSO) and found that our classifier is highly efficient in comparison with GA and PSO and their accuracies are 2% and 8%, respectively, less than those of perplexed Bayes classifier.
Subject
General Mathematics,General Medicine,General Neuroscience,General Computer Science
Cited by
6 articles.
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1. Detection and Classification of DDoS Attacks in Cloud Data Using Hybrid LSTM and RNN for Feature Selection;2023 International Conference on Circuit Power and Computing Technologies (ICCPCT);2023-08-10
2. Detection of DDoS Attacks on Clouds Computing Environments Using Machine Learning Techniques;2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS);2023-06-19
3. ML based D3 R: Detecting DDoS using Random Forest;2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW);2023-05
4. DDOS Attack Detection with Machine Learning: A Systematic Mapping of Literature;2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT);2023-01-23
5. Performance analysis of trusted security environment in cloud;Journal of Discrete Mathematical Sciences & Cryptography;2023