Feature Selection Using COA with Modified Feedforward Neural Network for Prediction of Attacks in Cyber-Security

Research on network intrusion detection, prediction, and mitigation systems has been ongoing due to the exponential rise in cyber-attacks in recent times. The prediction of future network invasions is still an open research subject, despite the abundance of intrusion detection systems. Current solut...

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Bibliographic Details
Published in:2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT) pp. 1 - 6
Main Authors: Vallabhaneni, Rohith, H S, Nagamani, P, Harshitha, S, Sumanth
Format: Conference Proceeding
Language:English
Published: IEEE 15.03.2024
Subjects:
Adaptation models
Coot Optimization Algorithm
Cyber-Attacks
Feature extraction
Feature Selection
FeedForward Neural Network
Feedforward neural networks
Network intrusion detection
Reliability
Search methods
Teaching-learning-based optimization
Threat assessment
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Summary:Research on network intrusion detection, prediction, and mitigation systems has been ongoing due to the exponential rise in cyber-attacks in recent times. The prediction of future network invasions is still an open research subject, despite the abundance of intrusion detection systems. Current solutions need feature selection and engineering since they use statistical and/or shallow machine learning techniques to do the job. This study begins with pre-processing the BotNet dataset with feature encoding and scaling. The next step is to use the Coot Optimisation Algorithm (COA) to pick the most significant characteristics. The next step is for the FeedForward Neural Network (FFNN) model to do the classification. ETLBO is used to determine the model's weight appropriately. Using the adaptive weight approach with the Kent chaotic map improves the ideal performance of the standard TLBO. The projected technique aims to circumvent the primary problems with the original TLBO by not using the local optimum and by maintaining an equilibrium between the search methods. When compared to current methods, the experimental analysis reveals that the suggested model attained a precision of 96.76% and an accuracy of 97.56%.
DOI:10.1109/ICDCOT61034.2024.10516044