A Transfer Learning Approach for Network Intrusion Detection

2019-09-05 · Peilun Wu, Hui Guo, Richard Buckland

Convolution Neural Network (ConvNet) offers a high potential to generalize input data. It has been widely used in many application areas, such as visual imagery, where comprehensive learning datasets are available and a ConvNet model can be well trained and perform the required function effectively. ConvNet can also be applied to network intrusion detection. However, the currently available datasets related to the network intrusion are often inadequate, which makes the ConvNet learning deficient, hence the trained model is not competent in detecting unknown intrusions. In this paper, we propose a ConvNet model using transfer learning for network intrusion detection. The model consists of two concatenated ConvNets and is built on a two-stage learning process: learning a base dataset and transferring the learned knowledge to the learning of the target dataset. Our experiments on the NSL-KDD dataset show that the proposed model can improve the detection accuracy not only on the test dataset containing mostly known attacks (KDDTest+) but also on the test dataset featuring many novel attacks (KDDTest-21) -- about 2.68\% improvement on KDDTest+ and 22.02\% on KDDTest-21 can be achieved, as compared to the traditional ConvNet model.

📄 PDF Abstract BibTeX arXiv:1909.02352

Code (0)

등록된 구현이 없습니다.

Tasks

Intrusion Detection Network Intrusion Detection Transfer Learning

Improving Transferability of Network Intrusion Detection in a Federated Learning Setup

2024-01-07 · Shreya Ghosh, Abu Shafin Mohammad Mahdee Jameel, Aly El Gamal

Network Intrusion Detection Systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device. Data-driven deep learning systems, popular due to their superior …

Federated Learning Intrusion Detection Network Intrusion Detection

Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection

2023-03-25 · Jiashu Wu, Yang Wang, Hao Dai, Chengzhong Xu 외

As Internet of Things devices become prevalent, using intrusion detection to protect IoT from malicious intrusions is of vital importance. However, the data scarcity of IoT hinders the effectiveness of traditional intrus…

Domain Adaptation Intrusion Detection Pseudo Label Recommendation Systems+1

Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems

2021-12-22 · Islam Debicha, Thibault Debatty, Jean-Michel Dricot, Wim Mees 외

In the last decade, the use of Machine Learning techniques in anomaly-based intrusion detection systems has seen much success. However, recent studies have shown that Machine learning in general and deep learning specifi…

BIG-bench Machine Learning Intrusion Detection Network Intrusion Detection

Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach

2023-01-24 · Jiashu Wu, Hao Dai, Yang Wang, Kejiang Ye 외

Data scarcity hinders the usability of data-dependent algorithms when tackling IoT intrusion detection (IID). To address this, we utilise the data rich network intrusion detection (NID) domain to facilitate more accurate…

Domain Adaptation Intrusion Detection Network Intrusion Detection Pseudo Label+1

TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems

2022-10-27 · Islam Debicha, Richard Bauwens, Thibault Debatty, Jean-Michel Dricot 외

Nowadays, intrusion detection systems based on deep learning deliver state-of-the-art performance. However, recent research has shown that specially crafted perturbations, called adversarial examples, are capable of sign…