Susilo Bambang, Muis Abdul, Sari Riri Fitri
Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia.
Sensors (Basel). 2025 Jan 20;25(2):580. doi: 10.3390/s25020580.
The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges. This study centers on employing deep learning methodologies to detect attacks. In general, this research aims to improve the performance of existing deep learning models. To mitigate data imbalances and enhance learning outcomes, the synthetic minority over-sampling technique (SMOTE) is employed. Our approach contributes to a multistage feature extraction process where autoencoders (AEs) are used initially to extract robust features from unstructured data on the model architecture's left side. Following this, long short-term memory (LSTM) networks on the right analyze these features to recognize temporal patterns indicative of abnormal behavior. The extracted and temporally refined features are inputted into convolutional neural networks (CNNs) for final classification. This structured arrangement harnesses the distinct capabilities of each model to process and classify IoT security data effectively. Our framework is specifically designed to address various attacks, including denial of service (DoS) and Mirai attacks, which are particularly harmful to IoT systems. Unlike conventional intrusion detection systems (IDSs) that may employ a singular model or simple feature extraction methods, our multistage approach provides more comprehensive analysis and utilization of data, enhancing detection capabilities and accuracy in identifying complex cyber threats in IoT environments. This research highlights the potential benefits that can be gained by applying deep learning methods to improve the effectiveness of IDSs in IoT security. The results obtained indicate a potential improvement for enhancing security measures and mitigating emerging threats.
物联网（IoT）已成为日常生活中的关键要素。由于其架构和支持技术存在诸多问题，物联网环境目前面临着重大的安全隐患。为确保物联网的全面安全，应对这些挑战至关重要。本研究聚焦于运用深度学习方法来检测攻击。总体而言，本研究旨在提升现有深度学习模型的性能。为缓解数据不平衡问题并提高学习效果，采用了合成少数类过采样技术（SMOTE）。我们的方法促成了一个多阶段特征提取过程，其中首先使用自动编码器（AE）从模型架构左侧的非结构化数据中提取稳健特征。在此之后，右侧的长短期记忆（LSTM）网络分析这些特征，以识别表明异常行为的时间模式。提取并经时间细化的特征被输入到卷积神经网络（CNN）进行最终分类。这种结构化安排利用了每个模型的独特能力，以有效地处理和分类物联网安全数据。我们的框架专门设计用于应对各种攻击，包括对物联网系统特别有害的拒绝服务（DoS）攻击和Mirai攻击。与可能采用单一模型或简单特征提取方法的传统入侵检测系统（IDS）不同，我们的多阶段方法提供了更全面的数据分析和利用，增强了在物联网环境中识别复杂网络威胁的检测能力和准确性。本研究凸显了应用深度学习方法提高物联网安全中IDS有效性可能带来的潜在益处。所获得的结果表明在加强安全措施和缓解新出现的威胁方面有潜在的改进。
