A Robust Hybrid Model Based on ANN and KNN for Multi Class Network Attack Detection and Classification

作者：Assoc. Prof. Xusnutdin Samarov, Zakhro Barotova · 发表于：International Journal of Inventive Engineering and Sciences · 年份：2025 · DOI：10.35940/ijies.h1115.12090925

This paper investigates whether a lightweight hybrid approach, which combines learned representations with instance based decisions, can improve multi-class intrusion detection under realistic class imbalance conditions. We propose A2K, which uses an Artificial Neural Network (ANN) to learn discriminative embeddings from preprocessed network-flow features and a KNearest Neighbours(KNN) classifier to make final decisions in the ANN’s latent space. The pipeline begins with min–max normalization and a feature selection routine combining mutual information, correlation analysis, and an ANN-wrapper evaluation to retain the most informative, non-redundant predictors. The ANN is a compact feed-forward model (41-d input, two hidden layers with 64 and 32 neurons, softmax output), trained to capture non-linear structures; its 32-d intermediate activations form the embedding for KNN, which exploits neighbourhood structures via Euclidean distances and majority voting. Using the NSL-KDD benchmark, we adopt a 70/30 train–test split and evaluate with Accuracy, Precision, Recall, and F1-score, alongside class-wise analyses and confusion matrices. We compare our results against strong baselines, including SVM, standalone ANN, standalone KNN, and Random Forest, all under the same preprocessing and protocol. Empirically, A2K attains 97.75% accuracy, 96.80% precision, 96.65% recall, and 96.56% F1-score, outperforming SVM (94.25% accuracy), KNN (91.25%), standalone ANN (95.80%), and Random Forest...