This paper introduces a Convolutional Neural Network (CNN) to jointly classify images with multiple classes on the Fashion-MNIST dataset, with a test accuracy of 90.20% and 0.11 million parameters of parameters a lightweight model, which significantly outperforms classical baselines (HOG+SVM: 85%) and is both computationally efficient. The CNN uses three convolutional blocks with varying filter depth (3264128), ReLU activation, MaxPooling, Batch Normalization, Dropout regularization, and fully connected classification head that is trained using Adam optimizer. These architectural concepts are generalised to the field of AI-related cybersecurity: namely, the deep learning-based Network Intrusion Detection Systems (NIDS) classifying network traffic flows as benign and attack ones - a problem that is characterised by the same core challenge architecture as Fashion-MNIST (spatial feature hierarchy extraction, multi-class discrimination, imbalanced class difficulty). State-of-the-art CNN based IDS are 94.8-97.5% accurate in detection (Attention-CNN-LSTM; Nature Scientific Reports, 2025), 98.5% with combined host/network data (Springer Nature, 2024), and 99.67% with encrypted malicious traffic.