Dealing with Imbalanced Classes in Bot-IoT Dataset

• URL: http://arxiv.org/abs/2403.18989v1
• Date: Wed, 27 Mar 2024 20:09:59 GMT
• Title: Dealing with Imbalanced Classes in Bot-IoT Dataset
• Authors: Jesse Atuhurra, Takanori Hara, Yuanyu Zhang, Masahiro Sasabe, Shoji Kasahara,
• Abstract summary: We propose a binary classification method with synthetic minority over-sampling techniques (SMOTE) to address the class imbalance problem in the Bot-IoT dataset. The proposed classifier aims to detect attack packets and overcome the class imbalance problem using the SMOTE algorithm.
• Score: 3.7399138244928145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapidly spreading usage of Internet of Things (IoT) devices, a network intrusion detection system (NIDS) plays an important role in detecting and protecting various types of attacks in the IoT network. To evaluate the robustness of the NIDS in the IoT network, the existing work proposed a realistic botnet dataset in the IoT network (Bot-IoT dataset) and applied it to machine learning-based anomaly detection. This dataset contains imbalanced normal and attack packets because the number of normal packets is much smaller than that of attack ones. The nature of imbalanced data may make it difficult to identify the minority class correctly. In this thesis, to address the class imbalance problem in the Bot-IoT dataset, we propose a binary classification method with synthetic minority over-sampling techniques (SMOTE). The proposed classifier aims to detect attack packets and overcome the class imbalance problem using the SMOTE algorithm. Through numerical results, we demonstrate the proposed classifier's fundamental characteristics and the impact of imbalanced data on its performance.

Related papers

• FedMADE: Robust Federated Learning for Intrusion Detection in IoT Networks Using a Dynamic Aggregation Method [7.842334649864372]
  Internet of Things (IoT) devices across multiple sectors has escalated serious network security concerns. Traditional Machine Learning (ML)-based Intrusion Detection Systems (IDSs) for cyber-attack classification require data transmission from IoT devices to a centralized server for traffic analysis, raising severe privacy concerns. We introduce FedMADE, a novel dynamic aggregation method, which clusters devices by their traffic patterns and aggregates local models based on their contributions towards overall performance.
  arXiv  Detail & Related papers  (2024-08-13T18:42:34Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality [41.88338945821504]
  FedRFQ is a prototype-based federated learning approach that aims to reduce redundancy, minimize failures, and improve underlinequality. We introduce the BFT-detect, a BFT (Byzantine Fault Tolerance) detectable aggregation algorithm, to ensure the security of FedRFQ against poisoning attacks and server malfunctions.
  arXiv  Detail & Related papers  (2024-01-15T09:50:27Z)
• Multi-class Network Intrusion Detection with Class Imbalance via LSTM & SMOTE [1.0591656257413806]
  This paper proposes to use oversampling techniques along with appropriate loss functions to handle class imbalance for the detection of various types of network intrusions. Our deep learning model employs LSTM with fully connected layers to perform multi-class classification of network attacks.
  arXiv  Detail & Related papers  (2023-10-03T07:28:04Z)
• Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids [5.705281336771011]
  False data injection attack (FDIA) is one of the classes of attacks that target the smart measurement devices by injecting malicious data. We propose a federated learning-based scheme combined with a hybrid deep neural network architecture. We validate the proposed architecture by extensive simulations on the IEEE 57, 118, and 300 bus systems and real electricity load data.
  arXiv  Detail & Related papers  (2023-06-17T20:29:55Z)
• Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS [48.353590166168686]
  Internet of Things (IoT) networks contain benign traffic far more than abnormal traffic, with some rare attacks. Most existing studies have been focused on sacrificing the detection rate of the majority class in order to improve the detection rate of the minority class. We propose a lightweight framework named S2CGAN-IDS to expand the number of minority categories in both data space and feature space.
  arXiv  Detail & Related papers  (2023-06-06T14:19:23Z)
• Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Network [0.0]
  Internet of Things (IoT) has altered living by controlling devices/things over the Internet. To bring down the IoT network, attackers can utilise these devices to conduct a variety of network attacks. In this paper, we have developed an unsupervised ensemble learning model that is able to detect new or unknown attacks in an IoT network from an unlabelled dataset.
  arXiv  Detail & Related papers  (2022-07-16T11:12:32Z)
• 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)
• IoT Security: Botnet detection in IoT using Machine learning [0.0]
  This research work is to propose an innovative model using machine learning algorithm to detect and mitigate botnet-based distributed denial of service (DDoS) attack in IoT network. Our proposed model tackles the security issue concerning the threats from bots.
  arXiv  Detail & Related papers  (2021-04-06T01:47:50Z)
• Adversarial Self-Supervised Contrastive Learning [62.17538130778111]
  Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions. We propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples. We present a self-supervised contrastive learning framework to adversarially train a robust neural network without labeled data.
  arXiv  Detail & Related papers  (2020-06-13T08:24:33Z)
• 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.