An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

• URL: http://arxiv.org/abs/2101.12270v1
• Date: Sun, 10 Jan 2021 11:48:37 GMT
• Title: An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks
• Authors: Andrew Churcher, Rehmat Ullah, Jawad Ahmad, Sadaqat ur Rehman, Fawad Masood, Mandar Gogate, Fehaid Alqahtani, Boubakr Nour and William J. Buchanan
• Abstract summary: In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS
• Score: 3.9236397589917127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

Related papers

• Performance evaluation of Machine learning algorithms for Intrusion Detection System [0.40964539027092917]
  This paper focuses on intrusion detection systems (IDSs) analysis using Machine Learning (ML) techniques. We analyze the KDD CUP-'99' intrusion detection dataset used for training and validating ML models.
  arXiv  Detail & Related papers  (2023-10-01T06:35:37Z)
• A Dependable Hybrid Machine Learning Model for Network Intrusion Detection [1.222622290392729]
  We propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022.
  arXiv  Detail & Related papers  (2022-12-08T20:19:27Z)
• Intrusion Detection in Internet of Things using Convolutional Neural Networks [4.718295605140562]
  We propose a novel solution to the intrusion attacks against IoT devices using CNNs. The data is encoded as the convolutional operations to capture the patterns from the sensors data along time. The experimental results show significant improvement in both true positive rate and false positive rate compared to the baseline using LSTM.
  arXiv  Detail & Related papers  (2022-11-18T07:27:07Z)
• 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)
• A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks [0.0]
  Intrusion detection is one of the challenging issues in the area of network security. In this paper, a comparative analysis of conventional machine learning classification algorithms has been performed. It can be observed that Multi-Layer Perception (MLP) has dependencies between input and output and relies more on network configuration for intrusion detection.
  arXiv  Detail & Related papers  (2021-11-02T05:58:07Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration [83.84684675841167]
  We propose a novel encoding scheme using -1, +1 to decompose quantized neural networks (QNNs) into multi-branch binary networks. We validate the effectiveness of our method on large-scale image classification, object detection, and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2021-06-18T03:11:15Z)
• Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network [62.77129284830945]
  We show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. We benchmark the performance of these attacks and show that with a small perturbation in the input of the neural network (NN), the white-box attacks can result in infeasible solutions up to 86%.
  arXiv  Detail & Related papers  (2021-01-28T16:18:19Z)
• 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)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• 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)

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.