Twin Auto-Encoder Model for Learning Separable Representation in Cyberattack Detection

• URL: http://arxiv.org/abs/2403.15509v1
• Date: Fri, 22 Mar 2024 03:39:40 GMT
• Title: Twin Auto-Encoder Model for Learning Separable Representation in Cyberattack Detection
• Authors: Phai Vu Dinh, Quang Uy Nguyen, Thai Hoang Dinh, Diep N. Nguyen, Bao Son Pham, Eryk Dutkiewicz,
• Abstract summary: We propose a novel mod called Twin Auto-Encoder (TAE) for cyberattack detection. Experiment results show the superior accuracy of TAE over state-of-the-art RL models and well-known machine learning algorithms.
• Score: 21.581155557707632
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Representation Learning (RL) plays a pivotal role in the success of many problems including cyberattack detection. Most of the RL methods for cyberattack detection are based on the latent vector of Auto-Encoder (AE) models. An AE transforms raw data into a new latent representation that better exposes the underlying characteristics of the input data. Thus, it is very useful for identifying cyberattacks. However, due to the heterogeneity and sophistication of cyberattacks, the representation of AEs is often entangled/mixed resulting in the difficulty for downstream attack detection models. To tackle this problem, we propose a novel mod called Twin Auto-Encoder (TAE). TAE deterministically transforms the latent representation into a more distinguishable representation namely the \textit{separable representation} and the reconstructsuct the separable representation at the output. The output of TAE called the \textit{reconstruction representation} is input to downstream models to detect cyberattacks. We extensively evaluate the effectiveness of TAE using a wide range of bench-marking datasets. Experiment results show the superior accuracy of TAE over state-of-the-art RL models and well-known machine learning algorithms. Moreover, TAE also outperforms state-of-the-art models on some sophisticated and challenging attacks. We then investigate various characteristics of TAE to further demonstrate its superiority.

Related papers

• Towards Characterizing Cyber Networks with Large Language Models [0.0]
  We employ latent features of cyber data to find anomalies via a prototype tool called Cyber Log Embeddings Model (CLEM) CLEM was trained on Zeek network traffic logs from both a real-world production network and an from Internet of Things (IoT) cybersecurity testbed.
  arXiv  Detail & Related papers  (2024-11-11T16:09:13Z)
• AI-Generated Video Detection via Spatio-Temporal Anomaly Learning [2.1210527985139227]
  Users can easily create non-existent videos to spread false information. A large-scale generated video dataset (GVD) is constructed as a benchmark for model training and evaluation.
  arXiv  Detail & Related papers  (2024-03-25T11:26:18Z)
• Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems [30.16714420093091]
  This paper first introduces a novel neural network architecture called Multiple-Input Auto-Encoder (MIAE) MIAE consists of multiple sub-encoders that can process inputs from different sources with different characteristics. To distil and retain more relevant features but remove less important/redundant ones during the training process, we further design and embed a feature selection layer. This layer learns the importance of features in the representation vector, facilitating the selection of informative features from the representation vector.
  arXiv  Detail & Related papers  (2024-03-22T03:54:04Z)
• Zero Day Threat Detection Using Metric Learning Autoencoders [3.1965908200266173]
  The proliferation of zero-day threats (ZDTs) to companies' networks has been immensely costly. Deep learning methods are an attractive option for their ability to capture highly-nonlinear behavior patterns. The models presented here are also trained and evaluated with two more datasets, and continue to show promising results even when generalizing to new network topologies.
  arXiv  Detail & Related papers  (2022-11-01T13:12:20Z)
• A White-Box Adversarial Attack Against a Digital Twin [0.0]
  This paper explores the susceptibility of Digital Twin (DT) to adversarial attacks. We first formulate a DT of a vehicular system using a deep neural network architecture and then utilize it to launch an adversarial attack. We attack the DT model by perturbing the input to the trained model and show how easily the model can be broken with white-box attacks.
  arXiv  Detail & Related papers  (2022-10-25T13:41:02Z)
• CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models [61.68061613161187]
  This paper presents CARLA-GeAR, a tool for the automatic generation of synthetic datasets for evaluating the robustness of neural models against physical adversarial patches. The tool is built on the CARLA simulator, using its Python API, and allows the generation of datasets for several vision tasks in the context of autonomous driving. The paper presents an experimental study to evaluate the performance of some defense methods against such attacks, showing how the datasets generated with CARLA-GeAR might be used in future work as a benchmark for adversarial defense in the real world.
  arXiv  Detail & Related papers  (2022-06-09T09:17:38Z)
• From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks [82.21746840893658]
  This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network. We show that while the ResNet-18 model trained on DWT spectrograms achieves a high recognition accuracy, attacking this model is relatively more costly for the adversary.
  arXiv  Detail & Related papers  (2022-04-14T15:14:08Z)
• Defending Variational Autoencoders from Adversarial Attacks with MCMC [74.36233246536459]
  Variational autoencoders (VAEs) are deep generative models used in various domains. As previous work has shown, one can easily fool VAEs to produce unexpected latent representations and reconstructions for a visually slightly modified input. Here, we examine several objective functions for adversarial attacks construction, suggest metrics assess the model robustness, and propose a solution.
  arXiv  Detail & Related papers  (2022-03-18T13:25:18Z)
• Hierarchical Variational Autoencoder for Visual Counterfactuals [79.86967775454316]
  Conditional Variational Autos (VAE) are gathering significant attention as an Explainable Artificial Intelligence (XAI) tool. In this paper we show how relaxing the effect of the posterior leads to successful counterfactuals. We introduce VAEX an Hierarchical VAE designed for this approach that can visually audit a classifier in applications.
  arXiv  Detail & Related papers  (2021-02-01T14:07:11Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• 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)

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.