Transfer Learning Based Intrusion Detection

Taghiyarrenani, Zahra; Fanian, Ali; Mahdavi, Ehsan; Mirzaei, Abdolreza; Farsi, Hamed

doi:10.1109/iccke.2018.8566601

Cited by 15 publications

(6 citation statements)

References 11 publications

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“…Yang et al [28] proposed IDS based on different TL models trained on the imagenet dataset, including VGG-16, VGG-19, InceptionNet, ResNet, and Incep-tionRestNet to develop the model on the CIC-IDS2017 and Car-Hacking datasets. The transfer learning (TL) model proposed by Taghiyarrenani et al, [30] for intrusion detection shows more efficient performance in both labeled and unlabeled data. To extract the attack invariant from the existing attack data set and transfer the knowledge to the target network system, Yanjie et al, [31] proposed a framework for transfer-learning-based network flow generation for deeplearning-based IDS.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

et al. 2023

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Data centralization can potentially increase Internet of Things (IoT) usage. The trend is to move IoT devices to a centralized server with higher memory capacity and a more robust management interface. Hence, a larger volume of data will be transmitted, resulting in more network security issues. Cloud IoT offers more advantages for deploying and managing IoT systems through minimizing response delays, optimal latency, and effective network load distribution. As a result, sophisticated network attack strategies are deployed to leverage the vulnerabilities in the extensive network space and exploit user information. Several attempts have been made to provide network intrusion detection systems (IDS) to the cloud IoT interface using machine learning and deep learning approaches on dedicated IDS datasets. This paper proposes a transfer learning IDS based on the Convolutional Neural Network (CNN) architecture that has shown excellent results on image classification. We use five pre-trained CNN models, including VGG16, VGG19, Inception, MobileNet, and EfficientNets, to train on two selected datasets: CIC-IDS2017 and CSE-CICIDS2018. Before the training, we carry out preprocessing, imbalance treatment, dimensionality reduction, and conversion of the feature vector into images suitable for the CNN architecture using Quantile Transformer. Three best-performing models (InceptionV3, MobileNetV3Small, and EfficientNetV2B0) are selected to develop an ensemble model called efficient-lightweight ensemble transfer learning (ELETL-IDS) using the model averaging approach. On evaluation, the findings show that the ELETL-IDS outperformed existing state-of-the-art proposals in all evaluation metrics, reaching 100% in accuracy, precision, recall, and F-score. We use Matthew's Correlation Coefficient (MCC) to validate this result and compared it to the AUC-ROC, which maintained an exact value of 0.9996. To this end, our proposed model is lightweight, efficient, and reliable enough to be deployed in cloud IoT systems for intrusion detection.

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Section: Related Workmentioning

confidence: 99%

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

et al. 2023

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“…Furthermore, while many existing studies address common issues such as limited training samples or unknown attack detection, they tend to focus on one problem rather than comprehensively evaluating all three issues. In addition, most of the current research, such as in [26,[31][32][33]40,41], has been conducted in traditional networks. In contrast, a few others, including [27,34,35], have explored the IoT, wireless networks, and cloud networks.…”

Section: Transfer Learning In Intrusion Detection Issuesmentioning

confidence: 99%

Applying Transfer Learning Approaches for Intrusion Detection in Software-Defined Networking

Chuang

2023

Sustainability

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In traditional network management, the configuration of routing policies and associated settings on individual routers and switches was performed manually, incurring a considerable cost. By centralizing network management, software-defined networking (SDN) technology has reduced hardware construction costs and increased flexibility. However, this centralized architecture renders information security vulnerable to network attacks, making intrusion detection in the SDN environment crucial. Machine-learning approaches have been widely used for intrusion detection recently. However, critical issues such as unknown attacks, insufficient data, and class imbalance may significantly affect the performance of typical machine learning. We addressed these problems and proposed a transfer-learning method based on the SDN environment. The following experimental results showed that our method outperforms typical machine learning methods. (1) our model achieved a F1-score of 0.71 for anomaly detection for unknown attacks; (2) for small samples, our model achieved a F1-score of 0.98 for anomaly detection and a F1-score of 0.51 for attack types identification; (3) for class imbalance, our model achieved an F1-score of 1.00 for anomaly detection and 0.91 for attack type identification. In addition, our model required 15,230 seconds (4 h 13 m 50 s) for training, ranking second among the six models when considering both performance and efficiency. In future studies, we plan to combine sampling techniques with few-shot learning to improve the performance of minority classes in class imbalance scenarios.

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“…Mapping-based TL maps instances from the source domain and target domain into a new latent space with same features and labels, suitable for training. The training process afterwards may utilize Machine Learning as in [19]. The authors extended DAMA [20] to transform the domains into the common latent space.…”

Section: Transfer Learning Approaches and Ids Studiesmentioning

confidence: 99%

Network attacks detection across infrastructures using an auto-labeling method and network-based Deep Inductive Transfer Learning approach

Nguyen

Tran

2023

Preprint

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An alarming number of cyber attacks have been witnessed in recent years, especially during COVID-19 epidemic. Many prevention measures have been proposed as a defense line for a network infrastructure, including Intrusion Detection System (IDS). A variance of IDS using Machine Learning and Deep Learning to detect network anomalies is gaining promising results. However, this approach also poses limitations regarding the indigenous dataset acquisition or the ability to apply a model learned from a benchmark dataset to different network infrastructures. Therefore, this paper proposes a reliable automatic labeling method for a new network dataset, and a Deep Transfer Learning model to detect both known and unknown attacks across different network infrastructures, then compares with other approaches. Network attacks detection using auto-labeling and network-based Deep Learning The obtained results reveal an outstanding performance of Transfer-Learning model, in comparison with non Transfer-Learning method, on two benchmark datasets (NSL-KDD, CIC-IDS2017) and a self-captured simulated network dataset using the auto-labeling approach. Furthermore, the model is verified to retain both new and old knowledge after the transfer learning process, which has not been mentioned in other studies, focusing only on learning new knowledge ability.

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Transfer Learning Based Intrusion Detection

Cited by 15 publications

References 11 publications

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

Applying Transfer Learning Approaches for Intrusion Detection in Software-Defined Networking

Network attacks detection across infrastructures using an auto-labeling method and network-based Deep Inductive Transfer Learning approach

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