Hybrid Deep Learning for Anomaly Detection in FANETs: A Defense Against DDoS Attacks
Keywords:
FANET, DDoS Detection, Anomaly Detection, ns-3 Simulation, Cybersecurity, Traffic monitoring, Autoencoder, LSTM, Network Security.Abstract
Flying ad-hoc networks (FANETs) have become indispensable in surveillance, disaster management, and environmental monitoring. The decentralized and dynamic characteristics of these systems make them vulnerable to substantial cybersecurity threats, namely distributed Denial of Service (DDoS) attacks, which can cause significant disruptions in vital activities. Here, we present VLDD-FANET (VAE-LSTM for DDoS Detection in FANETs), a novel framework designed to identify anomalies in FANETs. The system employs a robust integration of Variational Autoencoder (VAE) and Long Short-Term Memory (LSTM) networks to detect and prevent DDoS assaults in FANET traffic monitoring. A widely recognized method for modelling realistic networks, NS-3 simulation, was used to generate the dataset for this work. We employ sophisticated feature engineering techniques to measure essential network parameters, including packet rate, byte rate, flow duration, and number of communications. Identity of DDoS assaults can be achieved via detection of temporal and statistical irregularities in network traffic patterns. The suggested VLDD-FANET model exhibits exceptional performance with 0.9930 accuracy. It surpasses popular models such as LSTM, autoencoder, LSTM autoencoder, and ARIMA in performance. Through real-time detection of anomalies in FANET traffic monitoring software, our method improves FANET security against DDoS attacks. The VLDD-FANET methodology is a scalable approach to maintaining FANET integrity and functionality.
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