August 4, 2025

A Stratified Deep Detection Pipeline For Ddos Threats Leveraging Recursive Dimensionality Reduction, Probabilistic Latent Encoding, And Temporal Contextualization Networks

Key Points

Enhanced detection accuracy was observed with a model that differentiates malicious from benign traffic, optimizing response time.
Key finding includes improved F1-score based on recent datasets like CIC-DDoS2020 and CIC-DDoS2019, demonstrating effective threat detection capabilities.
Analysis utilized an Attention-Enriched Transfer Learning framework alongside variational autoencoders for latent representation learning.
This approach highlights efficient scaling and adaptability to new attack vectors in complex DDoS environments.

Abstract

Intelligent and adaptable detection systems are required due to the increasing complexity of Distributed Denial of Service (DDoS) attacks. This paper discuss an innovative blended approach for detecting DDoS threats which uses an Attention-Enriched Transfer Learning (AETL) 20 framework with Variational Autoencoders (VAEs) 19. In this model, we aimed to enhance anomaly detection. The model utilizes Deep Packet Inspection (DPI), sophisticated Recursive Feature Elimination (RFE), and temporal pattern recognition by Long-Short-Term Memory (LSTM) networks. To optimize the model's efficiency by differentiating between malicious and benign flows, VAEs are used, hence the unsupervised learning of latent representations of network traffic is done. These new experimental findings utilized the most recent datasets, such as CIC-DDoS2020, CIC-DDoS2019, CIC-DDoS2017 and TONIoT, show enhanced detection accuracy, F1-score, and response time. This architecture is shown to scale efficiently while adapting to emerging attack vectors.

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Cite This Study

GIBI et al. (Mon,) studied this question.

synapsesocial.com/papers/68af4322ad7bf08b1ead1f01 https://doi.org/https://doi.org/10.64252/mjsgvm74

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