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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Cloud computing security is paramount due to increasing demand.
  • Detecting distributed denial-of-service (DDoS) attacks presents significant challenges due to complex traffic patterns and numerous features.
  • Effective defense mechanisms are crucial for widespread cloud adoption.

Purpose of the Study:

  • To evaluate the efficacy of a novel perplexed-based classification algorithm for DDoS attack detection in cloud environments.
  • To compare the performance of the proposed algorithm against existing machine learning methods and nature-inspired feature selection techniques.

Main Methods:

  • A perplexed-based classifier was developed and evaluated with and without feature selection.
  • Performance metrics including accuracy, sensitivity, and specificity were used for comparison.
  • The proposed classifier was benchmarked against Naïve Bayes, Random Forest, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO).

Main Results:

  • The perplexed-based classifier achieved an accuracy of 99%, surpassing existing algorithms.
  • Feature selection using the perplexed Bayes classifier demonstrated superior performance compared to GA and PSO, with accuracies 2% and 8% higher, respectively.
  • The proposed algorithm proved highly efficient in identifying DDoS attacks within cloud computing systems.

Conclusions:

  • The novel perplexed-based classification algorithm offers a highly efficient and accurate solution for DDoS attack detection in cloud computing.
  • The proposed feature selection method outperforms nature-inspired algorithms, enhancing detection capabilities.
  • This research contributes to robust cloud security by providing an advanced defense mechanism against sophisticated cyber threats.