Accurate Network Intrusion Detection using a Feedforward Neural Network and Bee Colony Optimization Algorithm
Keywords:
Intrusion detection, Machine learning, Multi-layer Perceptron Neural Network, Bee Colony Optimization Algorithm.Abstract
Intrusion detection in computer networks is essential for protecting sensitive information and maintaining network security. This paper proposes a hybrid intrusion detection approach using a feedforward multilayer perceptron (MLP) neural network optimized by the Artificial Bee Colony (ABC) algorithm instead of traditional gradient descent. The proposed model enables it to detect both known and novel attack patterns. The model is evaluated using the NSL-KDD dataset after preprocessing, normalization, and PCA-based feature reduction, and performance is assessed using accuracy, precision, recall, and F1-score.The optimization algorithm fine-tunes the network’s weights and biases, leading to improved classification performance. The proposed model achieved an accuracy of 99.1%, surpassing other methods by an average of 3%, thus proving its effectiveness for secure and reliable intrusion detection.
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