Protecting the Cybersecurity Network Using Lotus Effect Optimization Algorithm Based SDL Model
Rohith Vallabhaneni, H S Nagamani, P Harshitha, Sneha Sumanth · 2024
A one-dimensional convolution model serves as the feature extraction and classifier in the study work's application of the simple deep learning model (SDL). The Lotus Effort Optimization Algorithm (LEOA) is developed to fine-tune the parameters of the suggested model. Combining exploration-oriented operators from the dragonfly algorithm—like the way dragonflies travel during pollination—with extraction- and local search-oriented operators from the lotus effect—the way water on flower leaves cleans itself—is what the LEOA model does. The suggested model was then tested using cybersecurity datasets and its efficacy was assessed by computing the f-score, accuracy, precision, and recall values. In addition, the model evaluates the efficacy of the generated security model by comparing its results with those of many well-established, popular machine learning techniques.