Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions

• URL: http://arxiv.org/abs/2406.05443v1
• Date: Sat, 8 Jun 2024 11:26:44 GMT
• Title: Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions
• Authors: Asmaa Benchama, Khalid Zebbara,
• Abstract summary: This paper introduces an innovative intrusion detection system that harnesses Generative Adversarial Networks (GANs), Multi-Scale Convolutional Neural Networks (MSCNNs), and Bidirectional Long Short-Term Memory (BiLSTM) networks. The system generates realistic network traffic data, encompassing both normal and attack patterns. Evaluation on the Hogzilla dataset, a standard benchmark, showcases an impressive accuracy of 99.16% for multi-class classification and 99.10% for binary classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces an innovative intrusion detection system that harnesses Generative Adversarial Networks (GANs), Multi-Scale Convolutional Neural Networks (MSCNNs), and Bidirectional Long Short-Term Memory (BiLSTM) networks, supplemented by Local Interpretable Model-Agnostic Explanations (LIME) for interpretability. Employing a GAN, the system generates realistic network traffic data, encompassing both normal and attack patterns. This synthesized data is then fed into an MSCNN-BiLSTM architecture for intrusion detection. The MSCNN layer extracts features from the network traffic data at different scales, while the BiLSTM layer captures temporal dependencies within the traffic sequences. Integration of LIME allows for explaining the model's decisions. Evaluation on the Hogzilla dataset, a standard benchmark, showcases an impressive accuracy of 99.16\% for multi-class classification and 99.10\% for binary classification, while ensuring interpretability through LIME. This fusion of deep learning and interpretability presents a promising avenue for enhancing intrusion detection systems by improving transparency and decision support in network security.

Related papers

• XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model [5.298018090482744]
  "XG-NID" is the first to fuse flow-level and packet-level data within a heterogeneous graph structure. XG-NID uniquely enables real-time inference while effectively capturing the intricate relationships between flow and packet payload data.
  arXiv  Detail & Related papers  (2024-08-27T01:14:34Z)
• BiLSTM and Attention-Based Modulation Classification of Realistic Wireless Signals [2.0650230600617534]
  The proposed model exploits multiple representations of the wireless signal as inputs to the network. An attention layer is used after the BiLSTM layer to emphasize the important temporal features. The experimental results on the recent and realistic RML22 dataset demonstrate the superior performance of the proposed model with an accuracy up to around 99%.
  arXiv  Detail & Related papers  (2024-08-14T01:17:19Z)
• X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System [2.556190321164248]
  Using machine learning (ML) and deep learning (DL) models in Intrusion Detection Systems has led to a trust deficit due to their non-transparent decision-making. This paper introduces a novel Explainable IDS approach, called X-CBA, that leverages the structural advantages of Graph Neural Networks (GNNs) to effectively process network traffic data. Our approach achieves high accuracy with 99.47% in threat detection and provides clear, actionable explanations of its analytical outcomes.
  arXiv  Detail & Related papers  (2024-02-01T18:29:16Z)
• Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
  Entropy and mutual information in neural networks provide rich information on the learning process. We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
  arXiv  Detail & Related papers  (2023-12-04T01:32:42Z)
• Bayesian Neural Network Language Modeling for Speech Recognition [59.681758762712754]
  State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. In this paper, an overarching full Bayesian learning framework is proposed to account for the underlying uncertainty in LSTM-RNN and Transformer LMs.
  arXiv  Detail & Related papers  (2022-08-28T17:50:19Z)
• An Explainable Machine Learning-based Network Intrusion Detection System for Enabling Generalisability in Securing IoT Networks [0.0]
  Machine Learning (ML)-based network intrusion detection systems bring many benefits for enhancing the security posture of an organisation. Many systems have been designed and developed in the research community, often achieving a perfect detection rate when evaluated using certain datasets. This paper tightens the gap by evaluating the generalisability of a common feature set to different network environments and attack types.
  arXiv  Detail & Related papers  (2021-04-15T00:44:45Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN [59.57221522897815]
  We propose a neural network model based on trajectories information for driving behavior recognition. We evaluate the proposed model on the public BLVD dataset, achieving a satisfying performance.
  arXiv  Detail & Related papers  (2021-03-01T06:47:29Z)
• Understanding Self-supervised Learning with Dual Deep Networks [74.92916579635336]
  We propose a novel framework to understand contrastive self-supervised learning (SSL) methods that employ dual pairs of deep ReLU networks. We prove that in each SGD update of SimCLR with various loss functions, the weights at each layer are updated by a emphcovariance operator. To further study what role the covariance operator plays and which features are learned in such a process, we model data generation and augmentation processes through a emphhierarchical latent tree model (HLTM)
  arXiv  Detail & Related papers  (2020-10-01T17:51:49Z)
• Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks [61.76338096980383]
  A range of neural architecture search (NAS) techniques are used to automatically learn two types of hyper- parameters of state-of-the-art factored time delay neural networks (TDNNs) These include the DARTS method integrating architecture selection with lattice-free MMI (LF-MMI) TDNN training. Experiments conducted on a 300-hour Switchboard corpus suggest the auto-configured systems consistently outperform the baseline LF-MMI TDNN systems.
  arXiv  Detail & Related papers  (2020-07-17T08:32:11Z)
• Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values [23.504633202965376]
  We focus on RNN-based models and attempt to reformulate the way to incorporate RNN and its variants into traffic prediction models. A stacked bidirectional and unidirectional LSTM network architecture (SBU-LSTM) is proposed to assist the design of neural network structures for traffic state forecasting. We also propose a data imputation mechanism in the LSTM structure (LSTM-I) by designing an imputation unit to infer missing values and assist traffic prediction.
  arXiv  Detail & Related papers  (2020-05-24T00:17:15Z)

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.