6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement Learning

• URL: http://arxiv.org/abs/2204.09839v1
• Date: Thu, 21 Apr 2022 01:25:20 GMT
• Title: 6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement Learning
• Authors: Tianyu Cui, Gaopeng Gou, Gang Xiong, Chang Liu, Peipei Fu, Zhen Li
• Abstract summary: 6GAN is a novel architecture built with Generative Adrial Net (GAN) and reinforcement learning for multi-pattern target generation. 6GAN's generators could keep a strong imitating ability for each pattern and 6GAN's discriminator obtains outstanding pattern discrimination ability with a 0.966 accuracy.
• Score: 10.054944443127376
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Global IPv6 scanning has always been a challenge for researchers because of the limited network speed and computational power. Target generation algorithms are recently proposed to overcome the problem for Internet assessments by predicting a candidate set to scan. However, IPv6 custom address configuration emerges diverse addressing patterns discouraging algorithmic inference. Widespread IPv6 alias could also mislead the algorithm to discover aliased regions rather than valid host targets. In this paper, we introduce 6GAN, a novel architecture built with Generative Adversarial Net (GAN) and reinforcement learning for multi-pattern target generation. 6GAN forces multiple generators to train with a multi-class discriminator and an alias detector to generate non-aliased active targets with different addressing pattern types. The rewards from the discriminator and the alias detector help supervise the address sequence decision-making process. After adversarial training, 6GAN's generators could keep a strong imitating ability for each pattern and 6GAN's discriminator obtains outstanding pattern discrimination ability with a 0.966 accuracy. Experiments indicate that our work outperformed the state-of-the-art target generation algorithms by reaching a higher-quality candidate set.

Related papers

• Generative AI Enabled Matching for 6G Multiple Access [51.00960374545361]
  We propose a GenAI-enabled matching generation framework to support 6G multiple access. We show that our framework can generate more effective matching strategies based on given conditions and predefined rewards.
  arXiv  Detail & Related papers  (2024-10-29T13:01:26Z)
• Designing Network Algorithms via Large Language Models [11.055072300500104]
  We introduce NADA, the first framework to autonomously design network algorithms by leveraging the generative capabilities of large language models (LLMs) We demonstrate that NADA produces novel ABR algorithms that consistently outperform the original algorithm in diverse network environments, including broadband, satellite, 4G, and 5G.
  arXiv  Detail & Related papers  (2024-04-02T03:43:55Z)
• GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model [69.71629949747884]
  Adversarial generative models, such as Generative Adversarial Networks (GANs), are widely applied for generating various types of data. In this work, we propose a novel algorithm named GE-AdvGAN to enhance the transferability of adversarial samples.
  arXiv  Detail & Related papers  (2024-01-11T16:43:16Z)
• 6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target Generation [7.462399334010083]
  In this paper, we try to use deep learning to design such IPv6 target generation algorithms. The model effectively learns the address structure by stacking the gated convolutional layer to construct Variational Autoencoder (VAE) Experiments indicate that our approach 6GCVAE outperformed the conventional VAE models and the state-of-the-art target generation algorithm in two active address datasets.
  arXiv  Detail & Related papers  (2022-04-20T12:36:19Z)
• Generative Cooperative Networks for Natural Language Generation [25.090455367573988]
  We introduce Generative Cooperative Networks, in which the discriminator architecture is cooperatively used along with the generation policy to output samples of realistic texts. We give theoretical guarantees of convergence for our approach, and study various efficient decoding schemes to empirically achieve state-of-the-art results in two main NLG tasks.
  arXiv  Detail & Related papers  (2022-01-28T18:36:57Z)
• 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)
• Multi-Source Domain Adaptation for Object Detection [52.87890831055648]
  We propose a unified Faster R-CNN based framework, termed Divide-and-Merge Spindle Network (DMSN) DMSN can simultaneously enhance domain innative and preserve discriminative power. We develop a novel pseudo learning algorithm to approximate optimal parameters of pseudo target subset.
  arXiv  Detail & Related papers  (2021-06-30T03:17:20Z)
• Determinantal Beam Search [75.84501052642361]
  Beam search is a go-to strategy for decoding neural sequence models. In use-cases that call for multiple solutions, a diverse or representative set is often desired. By posing iterations in beam search as a series of subdeterminant problems, we can turn the algorithm into a diverse subset selection process.
  arXiv  Detail & Related papers  (2021-06-14T13:01:46Z)
• MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images [51.227489316673484]
  We propose an arbitrary-oriented region proposal network (AO-RPN) to generate oriented proposals transformed from horizontal anchors. To obtain accurate bounding boxes, we decouple the detection task into multiple subtasks and propose a multi-head network. Each head is specially designed to learn the features optimal for the corresponding task, which allows our network to detect objects accurately.
  arXiv  Detail & Related papers  (2020-12-24T06:36:48Z)
• 6VecLM: Language Modeling in Vector Space for IPv6 Target Generation [26.73994727119052]
  We introduce our approach 6VecLM to explore achieving such target generation algorithms. The architecture can map addresses into a vector space to interpret semantic relationships. Experiments indicate that our approach can perform semantic classification on address space.
  arXiv  Detail & Related papers  (2020-08-05T16:26:50Z)

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.