Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network

• URL: http://arxiv.org/abs/2102.01873v1
• Date: Wed, 3 Feb 2021 04:24:34 GMT
• Title: Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network
• Authors: Praneet Singh, Jishnu Jaykumar, Akhil Pankaj, Reshmi Mitra
• Abstract summary: Edge nodes are crucial for detection against multitudes of cyber attacks on Internet-of-Things endpoints. We develop a novel light, fast and accurate 'Edge-Detect' model, which detects Denial of Service attack on edge nodes using DLM techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Edge nodes are crucial for detection against multitudes of cyber attacks on Internet-of-Things endpoints and is set to become part of a multi-billion industry. The resource constraints in this novel network infrastructure tier constricts the deployment of existing Network Intrusion Detection System with Deep Learning models (DLM). We address this issue by developing a novel light, fast and accurate 'Edge-Detect' model, which detects Distributed Denial of Service attack on edge nodes using DLM techniques. Our model can work within resource restrictions i.e. low power, memory and processing capabilities, to produce accurate results at a meaningful pace. It is built by creating layers of Long Short-Term Memory or Gated Recurrent Unit based cells, which are known for their excellent representation of sequential data. We designed a practical data science pipeline with Recurring Neural Network to learn from the network packet behavior in order to identify whether it is normal or attack-oriented. The model evaluation is from deployment on actual edge node represented by Raspberry Pi using current cybersecurity dataset (UNSW2015). Our results demonstrate that in comparison to conventional DLM techniques, our model maintains a high testing accuracy of 99% even with lower resource utilization in terms of cpu and memory. In addition, it is nearly 3 times smaller in size than the state-of-art model and yet requires a much lower testing time.

Related papers

• A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks [52.09243852066406]
  Adversarial Converging Time Score (ACTS) measures the converging time as an adversarial robustness metric. We validate the effectiveness and generalization of the proposed ACTS metric against different adversarial attacks on the large-scale ImageNet dataset.
  arXiv  Detail & Related papers  (2023-10-10T09:39:38Z)
• Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
  We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
  arXiv  Detail & Related papers  (2021-10-20T06:15:45Z)
• SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks [2.3204135551124407]
  Intrusion Detection Systems are widely used to detect cyberattacks. We present a deep learning-based sequential model for offline intrusion detection on SOME/IP protocol.
  arXiv  Detail & Related papers  (2021-08-04T09:58:06Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection [6.792495874038191]
  Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection. Network architecture is based on Convolutional SNN using leaky-integrate-fire neuron models.
  arXiv  Detail & Related papers  (2021-04-21T18:39:32Z)
• Malicious Requests Detection with Improved Bidirectional Long Short-term Memory Neural Networks [8.379440129896548]
  We formulate the problem of detecting malicious requests as a temporal sequence classification problem. We propose a novel deep learning model namely Convolutional Neural Network-Bidirectional Long Short-term Memory-Convolutional Neural Network (CNN-BiLSTM-CNN) Experimental results on HTTP dataset CSIC 2010 have demonstrated the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2020-10-26T02:27:44Z)
• Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks [8.291242737118482]
  We focus on edge computing supporting remote object detection by means of Deep Neural Networks (DNNs) We develop a framework to reduce the amount of data transmitted over the wireless link. The proposed technique represents an effective intermediate option between local and edge computing in a parameter region.
  arXiv  Detail & Related papers  (2020-07-31T03:11:46Z)
• 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)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

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.