计算机科学
服务拒绝攻击
自编码
人工智能
感知器
入侵检测系统
深度学习
机器学习
假阳性悖论
稳健性(进化)
人工神经网络
多层感知器
计算机安全
数据挖掘
互联网
生物化学
化学
万维网
基因
作者
Oluwatosin Adeniyi,Ali Safaa Sadiq,Prashant Pillai,Mohammad Aljaidi,Omprakash Kaiwartya
出处
期刊:Computers
[MDPI AG]
日期:2024-01-16
卷期号:13 (1): 25-25
标识
DOI:10.3390/computers13010025
摘要
In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC’s services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder–Multi-Layer Perceptron (AE–MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE–MLP model leverages autoencoders’ feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE–MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques.
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