A Lightweight Model for Malicious Code Classification Based on Structural Reparameterisation and Large Convolutional Kernels

Abstract: The classification of encrypted traffic is critical for network security, yet it faces a significant “few-shot” challenge as novel applications with scarce labeled data continuously emerge. This complexity arises from the high-dimensional, noisy nature of traffic data, making it difficult for models to generalize from few examples. Existing paradigms, such as meta-learning from scratch or standard pre-train/fine-tune methods, often fail in this scenario. To address this gap, we propose Contrastive Learning Meta-Flow (CL-MetaFlow), a novel two-stage learning framework that uniquely synergizes the strengths of contrastive representation learning and meta-learning adaptation. In the first stage, a robust feature encoder is pre-trained using supervised contrastive learning on known traffic classes, shaping a highly discriminative and metric-friendly embedding space. In the second stage, this pre-trained encoder initializes a Prototypical Network, enabling rapid and effective adaptation to new, unseen classes from only a few samples. Extensive experiments on a benchmark dataset (ISCX-VPN-2016 & ISCX-Tor-2017) demonstrate the superiority of our approach. Notably, in a five-way five-shot setting, CL-MetaFlow achieves a Macro F1-Score of 0.620, significantly outperforming from-scratch ProtoNet (0.384), a standard fine-tuning baseline (0.160), and strong pre-training counterparts like SimCLR+ProtoNet (0.545) and a re-implemented T-Sanitation (0.591). Our work validates that a high-quality, domain-adapted feature prior is the key to unlocking high-performance few-shot learning in complex network environments, providing a practical and powerful solution for real-world traffic analysis.

TL;DR: The study introduces novel fuzzy systems coupled with optimization algorithms to forecast deformation of geogrid-reinforced soil structures. The integrated system with the gannet optimization algorithm achieved the highest accuracy, demonstrating its effectiveness in reducing time and cost of numerical modeling.

TL;DR: This study develops bilateral models combining Vision Transformer and Multilayer Perceptron for traversable area detection in autonomous vehicles, achieving improved prediction accuracy and real-time capabilities in challenging environments with minimal distinction between traversable areas and surrounding ground.

TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).

TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.

TL;DR: MobileNetV2 as mentioned in this paper is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers and intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity.

TL;DR: In this article, the authors show that training with residual connections accelerates the training of Inception networks significantly, and they also present several new streamlined architectures for both residual and non-residual Inception Networks.