- Research Article
- 10.2174/0118722121368672250303051704
A Review on Attack Detection Using Machine Learning with Nature-inspired Optimization Techniques
- Mar 26, 2025
- Recent Patents on Engineering
- Seema Sharma + 1 more +1
<p>The security of web applications is a significant issue because of their widespread use in everyday activities. Although machine learning has shown success in identifying attacks, it can face difficulties when dealing with wide datasets. Algorithms inspired by nature, renowned for their optimization capabilities, offer potential solutions to this difficulty.</p><p> This paper analyses the use of Nature-Inspired Optimization Algorithms (NIOAs) to identify web application threats and assess their performance in detecting web application attackers.</p><p> This paper involves a comprehensive review of several kinds of patented algorithms that are based on nature- inspired algorithms to measure their performance in recognizing new types of web attacks. Additionally, this paper compares these methods with conventional machine learning approaches to reveal their strengths and weaknesses when used for web security purposes.</p><p> The study emphasizes the effectiveness of Nature-Inspired Optimization Algorithms (NIOAs) in enhancing detection mechanisms for evolving threats. These algorithms demonstrate adaptability and often outperform traditional methods in specific scenarios. The results reveal that optimization approaches like WOA, EO, GWO, and BAT achieved outstanding classification accuracy. WOA-stacking classifier gives the best performer with a classification accuracy of 99.28% and a leader fitness score of 99.17%. EO-Xgboost also stood out with a perfect accuracy of 100%. While traditional classifiers like Logistic Regression and SVM performed well, NIOA-based methods showed superior results.</p><p> Nature-inspired optimisation Algorithms can improve the security of web applications. The findings suggest that nature-inspired algorithms have potential applications across different problem domains, This empirical study provides valuable insights for researchers seeking accurate classifiers for detecting website attacks.</p>
Read more