Ain Noor Ul, Sardaraz Muhammad, Tahir Muhammad, Abo Elsoud Mohamed W, Alourani Abdullah
Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan.
Department of Computer Science and Information, College of Science at Zulfi, Majmaah University, P.O. Box 66, Al-Majmaah 11952, Saudi Arabia.
Sensors (Basel). 2025 Feb 22;25(5):1346. doi: 10.3390/s25051346.
The Internet of Things (IoT) has revolutionized many domains. Due to the growing interconnectivity of IoT networks, several security challenges persist that need to be addressed. This research presents the application of deep learning techniques for Distributed Denial-of-Service (DDoS) attack detection in IoT networks. This study assesses the performance of various deep learning models, including Latent Autoencoders, LSTM Autoencoders, and convolutional neural networks (CNNs), for DDoS attack detection in IoT environments. Furthermore, a novel hybrid model is proposed, integrating CNNs for feature extraction, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and Autoencoders for dimensionality reduction. Experimental results on the CICIOT2023 dataset show the enhanced performance of the proposed hybrid model, achieving training and testing accuracy of 96.78% integrated with 96.60% validation accuracy. This presents its efficiency in addressing complex attack patterns within IoT networks. Results' analysis shows that the proposed hybrid model outperforms the others. However, this research has limitations in detecting rare attack types and emphasizes the importance of addressing data imbalance challenges for further enhancement of DDoS attack detection capabilities in future.
物联网（IoT）已经彻底改变了许多领域。由于物联网网络的互联性不断增强，仍然存在一些需要解决的安全挑战。本研究介绍了深度学习技术在物联网网络分布式拒绝服务（DDoS）攻击检测中的应用。本研究评估了各种深度学习模型的性能，包括潜在自动编码器、长短期记忆自动编码器和卷积神经网络（CNN），用于物联网环境中的DDoS攻击检测。此外，还提出了一种新颖的混合模型，该模型集成了用于特征提取的CNN、用于时间模式识别的长短期记忆（LSTM）网络和用于降维的自动编码器。在CICIOT2023数据集上的实验结果表明，所提出的混合模型性能得到了增强，训练和测试准确率达到了96.78%，验证准确率为96.60%。这表明其在应对物联网网络中复杂攻击模式方面的效率。结果分析表明，所提出的混合模型优于其他模型。然而，本研究在检测罕见攻击类型方面存在局限性，并强调了应对数据不平衡挑战对于未来进一步提高DDoS攻击检测能力的重要性。
