基于深度稀疏自编码器和光梯度增强机的DDoS攻击混合检测系统

Rajasekhar Batchu, H. Seetha
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引用次数: 6

摘要

在互联网时代,基于网络的服务和联网设备越来越多,用户越来越多,因此网络攻击的数量也在增加。分布式拒绝服务(DDoS)攻击是一种网络攻击,其强度和对受害者的影响越来越大。通过DDoS检测系统有效地检测此类攻击是比较必要的研究。尽管机器学习技术在过去几年中在网络安全领域越来越受欢迎,但最近几天攻击模式的变化表明需要开发强大的DDoS预测模型。因此,我们建议使用两阶段混合方法的DDoS预测系统。首先,无监督深度稀疏自编码器(DSAE)使用最优超参数的弹性网正则化来提取特征。此外,调整了几个学习模型,根据提取的特征集对攻击进行分类。最后,利用提取的特征分析模型在平衡和不平衡数据场景下的性能。实验结果表明,该模型优于现有的方法。在CICIDS-2017和CICDDoS-2019数据集上对模型进行了评估,准确率分别达到99.98%和99.99%。
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A Hybrid Detection System for DDoS Attacks Based on Deep Sparse Autoencoder and Light Gradient Boost Machine
In the internet era, network-based services and connected devices are growing with many users, thus it became an increase in the number of cyberattacks. Distributed Denial of Service (DDoS) attacks are the type of cyberattacks increasing their strength and impact on the victim. Effective detection of such attacks through a DDoS Detection System is relatively essential research. Although machine learning techniques have grown in popularity in the field of cybersecurity over the last several years, the change in the attack patterns in recent days shows the need for developing a robust DDoS prediction model. Therefore, we suggested a DDoS prediction system using a two-stage hybrid methodology. Initially, features are extracted by the unsupervised Deep Sparse Autoencoder (DSAE) using Elastic Net regularisation with optimum hyperparameters. Further, several learning models are tuned to classify attacks based on the extracted feature sets. Finally, the models’ performance is analysed with extracted features in balanced and imbalanced data scenarios. The experimental outcomes show that the suggested model outperforms current approaches. The model was evaluated on the CICIDS-2017 and CICDDoS-2019 datasets and achieved an accuracy of 99.98% and 99.99%, respectively.
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