Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond

• URL: http://arxiv.org/abs/2412.03483v1
• Date: Wed, 04 Dec 2024 17:20:01 GMT
• Title: Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond
• Authors: Loukas Ilias, George Doukas, Vangelis Lamprou, Christos Ntanos, Dimitris Askounis,
• Abstract summary: 6G/NextG networks may become vulnerable to new security threats. Existing studies on the intrusion detection task rely on the train of shallow machine learning classifiers. We present the first study integrating Mixture of Experts (MoE) for identifying malicious traffic.
• Score: 5.452584641316627
• License:
• Abstract: The advent of 6G/NextG networks comes along with a series of benefits, including extreme capacity, reliability, and efficiency. However, these networks may become vulnerable to new security threats. Therefore, 6G/NextG networks must be equipped with advanced Artificial Intelligence algorithms, in order to evade these attacks. Existing studies on the intrusion detection task rely on the train of shallow machine learning classifiers, including Logistic Regression, Decision Trees, and so on, yielding suboptimal performance. Others are based on deep neural networks consisting of static components, which are not conditional on the input. This limits their representation power and efficiency. To resolve these issues, we present the first study integrating Mixture of Experts (MoE) for identifying malicious traffic. Specifically, we use network traffic data and convert the 1D array of features into a 2D matrix. Next, we pass this matrix through convolutional neural network (CNN) layers followed by batch normalization and max pooling layers. After obtaining the representation vector via the CNN layers, a sparsely gated MoE layer is used. This layer consists of a set of experts (dense layers) and a router, where the router assigns weights to the output of each expert. Sparsity is achieved by choosing the most relevant experts of the total ones. Finally, we perform a series of ablation experiments to prove the effectiveness of our proposed model. Experiments are conducted on the 5G-NIDD dataset, a network intrusion detection dataset generated from a real 5G test network. Results show that our introduced approach reaches weighted F1-score up to 99.95% achieving comparable performance to existing approaches. Findings also show that our proposed model achieves multiple advantages over state-of-the-art approaches.

Related papers

• TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks [3.489779105594534]
  We introduce a novel approach to backdoor detection using two tensor decomposition methods applied to network activations. This has a number of advantages relative to existing detection methods, including the ability to analyze multiple models at the same time. Results show that our method detects backdoored networks more accurately and efficiently than current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-06T03:08:28Z)
• Active search and coverage using point-cloud reinforcement learning [50.741409008225766]
  This paper presents an end-to-end deep reinforcement learning solution for target search and coverage. We show that deep hierarchical feature learning works for RL and that by using farthest point sampling (FPS) we can reduce the amount of points. We also show that multi-head attention for point-clouds helps to learn the agent faster but converges to the same outcome.
  arXiv  Detail & Related papers  (2023-12-18T18:16:30Z)
• Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference [13.924924047051782]
  Deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. This research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs) We propose an innovative spiking architecture that uses batch normalization to retain MFI compatibility. We establish an efficient multi-stage spiking network that blends effectively global receptive fields with local feature extraction.
  arXiv  Detail & Related papers  (2023-06-21T16:52:20Z)
• Diffused Redundancy in Pre-trained Representations [98.55546694886819]
  We take a closer look at how features are encoded in pre-trained representations. We find that learned representations in a given layer exhibit a degree of diffuse redundancy. Our findings shed light on the nature of representations learned by pre-trained deep neural networks.
  arXiv  Detail & Related papers  (2023-05-31T21:00:50Z)
• Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks [6.44397009982949]
  We introduce a novel method for backdoor detection that extracts features from pre-trained DNN's weights. In comparison to other detection techniques, this has a number of benefits, such as not requiring any training data. Our method outperforms the competing algorithms in terms of efficiency and is more accurate, helping to ensure the safe application of deep learning and AI.
  arXiv  Detail & Related papers  (2022-12-15T20:20:18Z)
• NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks [9.194664029847019]
  We show how to use Machine Learning for Network Intrusion Detection (NID) in a principled way. We propose NetSentry, perhaps the first of its kind NIDS that builds on Bi-ALSTM, an original ensemble of sequential neural models. We demonstrate F1 score gains above 33% over the state-of-the-art, as well as up to 3 times higher rates of detecting attacks such as XSS and web bruteforce.
  arXiv  Detail & Related papers  (2022-02-20T17:41:02Z)
• 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)
• Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming [97.40955121478716]
  We propose a first-order dual SDP algorithm that requires memory only linear in the total number of network activations. We significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively. We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
  arXiv  Detail & Related papers  (2020-10-22T12:32:29Z)
• EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion Detection [2.223733768286313]
  We propose a new architecture to detect network attacks with minimal resources. The architecture is able to deal with either binary or multiclass classification problems and trades prediction speed for the accuracy of the network.
  arXiv  Detail & Related papers  (2020-07-27T11:31:37Z)
• Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks [100.14670789581811]
  We train a graph convolutional network to fit the performance of sampled sub-networks. With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates.
  arXiv  Detail & Related papers  (2020-04-17T19:12:39Z)
• DHP: Differentiable Meta Pruning via HyperNetworks [158.69345612783198]
  This paper introduces a differentiable pruning method via hypernetworks for automatic network pruning. Latent vectors control the output channels of the convolutional layers in the backbone network and act as a handle for the pruning of the layers. Experiments are conducted on various networks for image classification, single image super-resolution, and denoising.
  arXiv  Detail & Related papers  (2020-03-30T17:59:18Z)

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.