Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network

By Kaiyuan Jiang, Wenya Wang, Aili Wang, Haibin Wu. IEEE Access, 2020

342 citations recorded by the source corpus.

Intrusion detection system (IDS) plays an important role in network security by discovering and preventing malicious activities. Due to the complex and time-varying network environment, the network intrusion samples are submerged into a large number of normal samples, which leads to insufficient samples for model training and detection results with a high false detection rate. According to the problem of data imbalance, we propose a network intrusion detection algorithm combined hybrid sampling with deep hierarchical network. Firstly, we use the one-side selection (OSS) to reduce the noise samples in majority category, and then increase the minority samples by Synthetic Minority Over-sampling Technique (SMOTE). In this way, a balanced dataset can be established to make the model fully learn the features of minority samples and greatly reduce the model training time. Secondly, we use convolution neural network (CNN) to extract spatial features and Bi-directional long short-term memory (BiLSTM) to extract temporal features, which forms a deep hierarchical network model. The proposed network intrusion detection algorithm was verified by experiments on the NSL-KDD and UNSW-NB15 dataset, and the classification accuracy can achieve 83.58% and 77.16%, respectively.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/29c99503e8a15df7621b3f1f6ac6457a65f179c1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nivedita Mishra
2. Yanxia Sun
3. Sharnil Pandya
4. Sydney Mambwe Kasongo

In Computer Networks and Communications

1. Nivedita Mishra
2. Yanxia Sun
3. Sharnil Pandya
4. Sydney Mambwe Kasongo

Citations in the corpus

• Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review (2021, methodology): “Jiang et al. [143] addressed the issue of Data imbalance by using One-Side Selection (OSS) to reduce noise in majority classes and synthetically enhancing minority classes with the help of the Synthetic Minority Over-Sampling (SMOTE) technique.”
• Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset (2020, methodology): “[32] proposed an NIDS framework that used the One-Side Selection (O-SS) method to reduce the amount of noisy data records within the majority classes.”