A Hybrid Deep Learning Model for Efficient IOT Botnet Attack Detection

An Enhanced Deep Learning Approach for IoT Botnet Attack Detection

The increasing development of the Internet of Things has led to several security threats and vulnerabilities associated with interconnected networks and, thus, makes them suitable targets for DDoS attacks. The increased dimensionality of traffic in the context of the Internet of Things makes IDS hard because deep learning models running independently are unable to adequately address the complexity associated with the interaction between network attributes. In this context, this research suggests a new architecture based on deep learning methods for binary classifications and featuring hybridization of multi-layer 1D-CNNs with LSTM networks. To reduce computational resources needed for training on IoT-based datasets, the ANOVA F-test is implemented to choose the key features before training. The proposed architecture includes dual convolutional layers having 64 and 128 filters, respectively, that allow to analyze the hierarchical spatial structures. Next, the data from convolutional layers is passed to the LSTM layer that analyzes high-order structural dependencies within feature maps generated at earlier stages. The proposed model was trained using the CICIoT2023 benchmark and showed superior performance compared to a standalone architecture featuring either CNNs or LSTM networks. Across five independent trials, the model achieved a mean accuracy of 98.72% ± 0.06%, with a precision of 98.91% and a recall of 98.65%. Furthermore, the proposed hybrid architecture shows high computational efficiency, achieving a remarkably low inference time of 0.15 ms per sample and a compact model footprint of 0.30 MB. These metrics confirm the model's suitability for deployment on resource-constrained IoT edge devices, providing a robust balance between high detection accuracy and low architectural complexity. These findings confirm that the constructive collaboration between spatial and structural feature modeling provides a robust, stable, and scalable solution for mitigating DDoS threats in modern IoT ecosystems.

Comparative Study of Deep Learning Models for Malware Detection in IoT Networks: CNN, LSTM, and Hybrid Architectures

A Hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model for effective IoT malware detection is proposed, which enhances detection capability for both known and zero-day attacks.

A Deep Learning-Based Framework for Cyber Attack Detection in IoT Networks

An intelligent cyberattack detection system that applies machine learning and deep learning techniques to classify network traffic as either normal or malicious, and demonstrates the potential of machine learningbased intrusion detection systems in improving network security and supporting the protection of modern smart environments.

A Hybrid CNN–BiGRU Deep Learning Model for DDoS Attack Detection in Cybersecurity

Distributed Denial of Service (DDoS) attacks remain one of the most serious threats to modern network infrastructures, as they overwhelm systems with massive traffic and disrupt legitimate services. Traditional intrusion detection systems often struggle to detect complex and evolving attack patterns due to their reliance on manual feature engineering and limited learning capability. To address this issue, this study proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) for efficient DDoS attack detection. The CNN component is used to automatically extract important spatial features from network traffic data, while the BiGRU layer captures temporal dependencies and sequential patterns in the traffic flows. The proposed model is evaluated using the CICDDoS2019 dataset, which contains realistic benign and attack traffic across multiple DDoS categories. Experimental results demonstrate that the model achieves high performance in both binary and multiclass classification tasks, obtaining an accuracy of 99.82% for binary detection and 99.21% for multiclass classification. The results indicate that the hybrid CNN–BiGRU architecture effectively improves detection accuracy and provides a reliable approach for intelligent network intrusion detection systems.

DTLgan-IDS: deep transfer learning with CGAN for optimized intrusion detection system on IoT networks

Deep Learning-Based Network Intrusion Detection Using Hybrid CNN and LSTM Architecture

The findings indicate that hybrid deep learning techniques can improve network security by enhancing intrusion detection capability while reducing false alarms.