Intrusion Detection Based on Fusing Deep Neural Networks and Transfer Learning

Xu, Yingying; Li, Zhi; Li, Yanmiao; Zheng, Yanjun; Hou, Huijie; Gao, Mingcheng; Song, Yonghui; Yang, Xin

doi:10.1007/978-981-15-3341-9_18

Cited by 17 publications

(14 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental analysis shows that the model has greatly improved the detection accuracy rate by 23%. Xu et al [24] proposed an intrusion detection approach based on deep learning and transfer learning. Here, transfer learning is initiated to enhance the efficiency and adaptability of the model.…”

Section: Related Workmentioning

confidence: 99%

“…A new-generation labeled TON_IoT_Telemetry_Dataset of IoT devices for data-driven IDS was proposed by Alsaedi et al [6], which is more suitable for applying deep transfer learning models. The deep transfer learning approach shows better performances in time series classification than other models [24]. Because of the original situation of heterogeneous IoT applications, this paper has comprehensively improved the existing transfer learning model to ensure its dependability for detecting various complex types of cyber-attacks.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

Mehedi,

Anwar,

Rahman

et al. 2022

Preprint

View full text Add to dashboard Cite

Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning-based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning, and deep transfer learning have progressed and experienced great success in different fields and have emerged as a potential solution for dependable network intrusion detection. However, new and emerging challenges have arisen related to the accuracy, efficiency, scalability, and dependability of the traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep transfer learning-based dependable IDS model that outperforms several existing approaches. The unique contributions include effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data, designing a dependable deep transfer learning-based ResNet model, and evaluating considering real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Extensive analysis and performance evaluation show that the proposed model is robust, more efficient, and has demonstrated better performance, ensuring dependability.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

Mehedi,

Anwar,

Rahman

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The experimental results show that, when compared to existing TML and DL methods, this model significantly improved detection accuracy by at least 23%. Xu et al [ 42 ] recently proposed an IDS based on DL and transfer learning. To improve the model’s efficiency and adaptability, transfer learning is implemented here.…”

Section: Background and Related Workmentioning

confidence: 99%

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Mehedi

Anwar

Rahman

et al. 2021

Sensors

View full text Add to dashboard Cite

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

show abstract

“…Some of the works found in this area have taken the approach of converting NIDS dataset into image and treating the problem as an image classification one using Convolutional Neural Networks CNN. Xu et al [28] in their work used a source domain KDD Cup 99 dataset and a target domain "corrected" KDD Cup 99 dataset (which has 17 intrusion types not found in the source dataset) for their experiment. As the two datasets used in their experiment have the same set of features and only differ in the types of intrusion, this is clearly an homogeneous features scenario with source task different than target task: (Y S = Y T ).…”

Section: A Domain Adaptive Nids With Homogeneous Featuresmentioning

confidence: 99%

“…In contrast to [28], a more recent dataset with attacks relevant to our time has been used by [10]. The work however revealed little details on their method for the choice of hyperparameters.…”

Section: A Domain Adaptive Nids With Homogeneous Featuresmentioning

confidence: 99%

DAHID: Domain Adaptive Host-based Intrusion Detection

Ajayi

Gangopadhyay

2021

2021 IEEE International Conference on Cyber Security and Resilience (CSR)

View full text Add to dashboard Cite

Cybersecurity is becoming increasingly important with the explosion of attack surfaces as more cyber-physical systems are being deployed. It is impractical to create models with acceptable performance for every single computing infrastructure and the various attack scenarios due to the cost of collecting labeled data and training models. Hence it is important to be able to develop models that can take advantage of knowledge available in an attack source domain to improve performance in a target domain with little domain specific data.In this work we proposed Domain Adaptive Host-based Intrusion Detection DAHID; an approach for detecting attacks in multiple domains for cybersecurity. Specifically, we implemented a deep learning model which utilizes a substantially smaller amount of target domain data for host-based intrusion detection.In our experiments, we used two datasets from Australian Defense Force Academy; ADFA-WD as the source domain and ADFA-WD:SAA as the target domain datasets. We recorded a significant improvement in Area Under Curve AUC from 83% to 91%, when we fine-tuned a deep learning model trained on ADFA-WD with as little as 20% of ADFA-WD:SAA. Our result shows transfer learning can help to alleviate the need of huge domain specific dataset in building host-based intrusion detection models.

show abstract

Intrusion Detection Based on Fusing Deep Neural Networks and Transfer Learning

Cited by 17 publications

References 23 publications

Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

DAHID: Domain Adaptive Host-based Intrusion Detection

Contact Info

Product

Resources

About