Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection

• URL: http://arxiv.org/abs/2202.01332v1
• Date: Wed, 2 Feb 2022 23:51:11 GMT
• Title: Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection
• Authors: Wen Xu, Julian Jang-Jaccard, Tong Liu, Fariza Sabrina
• Abstract summary: Existing generative adversarial networks (GANs) are primarily used for creating synthetic samples from reals. In our proposed method, we construct the trained encoder-discriminator as a one-class classifier based on Bidirectional GAN (Bi-GAN) Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks.
• Score: 8.158224495708978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The network intrusion detection task is challenging because of the imbalanced and unlabeled nature of the dataset it operates on. Existing generative adversarial networks (GANs), are primarily used for creating synthetic samples from reals. They also have been proved successful in anomaly detection tasks. In our proposed method, we construct the trained encoder-discriminator as a one-class classifier based on Bidirectional GAN (Bi-GAN) for detecting anomalous traffic from normal traffic other than calculating expensive and complex anomaly scores or thresholds. Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks and outperforms other similar generative methods on the NSL-KDD dataset.

Related papers

• Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers [1.6001193161043425]
  Botnets are computer networks controlled by malicious actors that present significant cybersecurity challenges. This research addresses the sophisticated adversarial manipulations posed by attackers, aiming to undermine machine learning-based botnet detection systems. We introduce a flow-based detection approach, leveraging machine learning and deep learning algorithms trained on the ISCX and ISOT datasets.
  arXiv  Detail & Related papers  (2024-09-01T08:53:21Z)
• DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection [0.0]
  Machine Learning approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs) Recent work has achieved near-perfect performance by following binary- and multi-class network anomaly detection tasks. This paper proposes a Deep One-Class (DOC) classifier for network intrusion detection by only training on benign network data samples.
  arXiv  Detail & Related papers  (2022-12-15T00:08:05Z)
• Label-Efficient Object Detection via Region Proposal Network Pre-Training [58.50615557874024]
  We propose a simple pretext task that provides an effective pre-training for the region proposal network (RPN) In comparison with multi-stage detectors without RPN pre-training, our approach is able to consistently improve downstream task performance.
  arXiv  Detail & Related papers  (2022-11-16T16:28:18Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Unsupervised Domain-adaptive Hash for Networks [81.49184987430333]
  Domain-adaptive hash learning has enjoyed considerable success in the computer vision community. We develop an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH.
  arXiv  Detail & Related papers  (2021-08-20T12:09:38Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Dual-encoder Bidirectional Generative Adversarial Networks for Anomaly Detection [0.0]
  We develop a dual-encoder in a bidirectional GAN architecture that is trained simultaneously with a generator and a discriminator network. We show that our proposed method performs well in capturing the distribution of normal samples, thereby improving anomaly detection on GAN-based models.
  arXiv  Detail & Related papers  (2020-12-22T05:05:33Z)
• DNS Covert Channel Detection via Behavioral Analysis: a Machine Learning Approach [0.09176056742068815]
  We propose an effective covert channel detection method based on the analysis of DNS network data passively extracted from a network monitoring system. The proposed solution has been evaluated over a 15-day-long experimental session with the injection of traffic that covers the most relevant exfiltration and tunneling attacks.
  arXiv  Detail & Related papers  (2020-10-04T13:28:28Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)
• BiDet: An Efficient Binarized Object Detector [96.19708396510894]
  We propose a binarized neural network learning method called BiDet for efficient object detection. Our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal. Our method outperforms the state-of-the-art binary neural networks by a sizable margin.
  arXiv  Detail & Related papers  (2020-03-09T08:16:16Z)
• Anomaly Detection by One Class Latent Regularized Networks [36.67420338535258]
  Semi-supervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently. A novel adversarial dual autoencoder network is proposed, in which the underlying structure of training data is captured in latent feature space. Experiments show that our model achieves the state-of-the-art results on MNIST and CIFAR10 datasets as well as GTSRB stop signs dataset.
  arXiv  Detail & Related papers  (2020-02-05T02:21:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.