Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems

• URL: http://arxiv.org/abs/2404.19114v1
• Date: Mon, 29 Apr 2024 21:26:18 GMT
• Title: Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems
• Authors: Afsaneh Mahanipour, Hana Khamfroush,
• Abstract summary: The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. This paper focuses on improving the effectiveness of ML-based IDS at the edge level by introducing a novel method to find a balanced trade-off between cost and accuracy.
• Score: 1.749521391198341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. IoT applications using cloud and edge computing are at higher risk of cyberattacks because of the expanded attack surface from distributed edge and cloud services, the vulnerability of IoT devices, and challenges in managing security across interconnected systems leading to oversights. This led to the rise of ML-based solutions for intrusion detection systems (IDSs), which have proven effective in enhancing network security and defending against diverse threats. However, ML-based IDS in IoT systems encounters challenges, particularly from noisy, redundant, and irrelevant features in varied IoT datasets, potentially impacting its performance. Therefore, reducing such features becomes crucial to enhance system performance and minimize computational costs. This paper focuses on improving the effectiveness of ML-based IDS at the edge level by introducing a novel method to find a balanced trade-off between cost and accuracy through the creation of informative features in a two-tier edge-user IoT environment. A hybrid Binary Quantum-inspired Artificial Bee Colony and Genetic Programming algorithm is utilized for this purpose. Three IoT intrusion detection datasets, namely NSL-KDD, UNSW-NB15, and BoT-IoT, are used for the evaluation of the proposed approach.

Related papers

• Hybrid Machine Learning Models for Intrusion Detection in IoT: Leveraging a Real-World IoT Dataset [0.0]
  Intrusion Detection Systems (IDS) are crucial for mitigating these threats. Recent advancements in Machine Learning (ML) offer promising avenues for improvement. This research explores a hybrid approach, combining several standalone ML models.
  arXiv  Detail & Related papers  (2025-02-17T23:41:10Z)
• Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions [0.0]
  This paper explores the challenges associated with securing IIoT-based smart metering networks. It proposes a Machine Learning-based Intrusion Detection and Prevention System (IDPS) for safeguarding edge devices.
  arXiv  Detail & Related papers  (2025-02-16T14:08:59Z)
• Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities [0.0]
  Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks. This study proposes a Federated Learning-Driven Cybersecurity Framework designed specifically for IoT environments. Secure aggregation of locally trained models is achieved using homomorphic encryption, allowing collaborative learning without exposing sensitive information.
  arXiv  Detail & Related papers  (2025-02-14T23:11:51Z)
• Federated PCA on Grassmann Manifold for IoT Anomaly Detection [23.340237814344384]
  Traditional machine learning-based intrusion detection systems (ML-IDS) possess limitations such as the requirement for labeled data. Recent unsupervised ML-IDS approaches such as AutoEncoders and Generative Adversarial Networks (GAN) offer alternative solutions. This paper proposes a novel federated unsupervised anomaly detection framework, FedPCA, that learns common representations of distributed non-i.i.d. datasets.
  arXiv  Detail & Related papers  (2024-07-10T07:23:21Z)
• A Cutting-Edge Deep Learning Method For Enhancing IoT Security [0.0]
  This paper proposes an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or IDS) using Deep Learning-integrated Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Our model, based on the CICIDS 2017 dataset, achieved an accuracy of 99.52% in classifying network traffic as either benign or malicious.
  arXiv  Detail & Related papers  (2024-06-18T08:42:51Z)
• Lightweight CNN-BiLSTM based Intrusion Detection Systems for Resource-Constrained IoT Devices [38.16309790239142]
  Intrusion Detection Systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. The limited computational resources available on Internet of Things (IoT) devices make it challenging to deploy conventional computing-based IDSs. We propose a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM (BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset.
  arXiv  Detail & Related papers  (2024-06-04T20:36:21Z)
• 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)
• Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI [46.61358701676358]
  Edge artificial intelligence (AI) has been proposed to provide high-performance computation of a conventional cloud down to the network edge. Recently, convergence of wireless sensing, computation and communication (SC$2$) for specific edge AI tasks, has aroused paradigm shift. It is paramount importance to advance fully integrated sensing, computation and communication (I SCC) to achieve ultra-reliable and low-latency edge intelligence acquisition.
  arXiv  Detail & Related papers  (2023-06-11T06:40:51Z)
• 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)
• RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems [41.1371349978643]
  We present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy. We formulate this methodology, as an optimization, where we establish a trade-off between the latency of co-inference and the privacy-level of data.
  arXiv  Detail & Related papers  (2022-08-27T14:50:00Z)
• Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [45.076180487387575]
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
  arXiv  Detail & Related papers  (2021-05-04T23:42:06Z)

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.