Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning

被引:0
|
作者
Balega, Maria [1 ,2 ]
Farag, Waleed [1 ]
Wu, Xin-Wen [3 ]
Ezekiel, Soundararajan [1 ]
Good, Zaryn [1 ]
机构
[1] Indiana Univ Penn, Dept Math & Comp Sci, Indiana, PA 15705 USA
[2] Carnegie Mellon Univ, Informat Networking Inst, Pittsburgh, PA 15289 USA
[3] Univ Mary Washington, Dept Comp Sci, Fredericksburg, VA 22401 USA
关键词
anomaly detection; DCNN; Internet of Things (IoT); machine learning (ML); SVM; XGBoost;
D O I
10.3390/electronics13112148
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge. Anomaly detection is a crucial procedure in protecting the IoT. A promising way to perform anomaly detection in the IoT is through the use of machine learning (ML) algorithms. There is a lack of studies in the literature identifying optimal (with regard to both effectiveness and efficiency) anomaly detection models for the IoT. To fill the gap, this work thoroughly investigated the effectiveness and efficiency of IoT anomaly detection enabled by several representative machine learning models, namely Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVMs), and Deep Convolutional Neural Networks (DCNNs). Identifying optimal anomaly detection models for IoT anomaly detection is challenging due to diverse IoT applications and dynamic IoT networking environments. It is of vital importance to evaluate ML-powered anomaly detection models using multiple datasets collected from different environments. We utilized three reputable datasets to benchmark the aforementioned machine learning methods, namely, IoT-23, NSL-KDD, and TON_IoT. Our results show that XGBoost outperformed both the SVM and DCNN, achieving accuracies of up to 99.98%. Moreover, XGBoost proved to be the most computationally efficient method; the model performed 717.75 times faster than the SVM and significantly faster than the DCNN in terms of training times. The research results have been further confirmed by using our real-world IoT data collected from an IoT testbed consisting of physical devices that we recently built.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Enhancing Drone Security Through Multi-Sensor Anomaly Detection and Machine Learning
    Mohammed Y. Alzahrani
    [J]. SN Computer Science, 5 (5)
  • [2] Enhancing Security and Reliability in Industrial IoT Networks through Machine Learning
    V. Barekar, Praful
    Purandare, Radhika
    Sawlikar, Alka
    Welekar, Rashmi R.
    Ingole, Piyush K.
    Shelke, Nilesh
    [J]. JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 (01) : 289 - 302
  • [3] Enhancing electrical panel anomaly detection for predictive maintenance with machine learning and IoT
    Peksen, Muhammed Fatih
    Yurtsever, Ulas
    Uyaroglu, Yilmaz
    [J]. ALEXANDRIA ENGINEERING JOURNAL, 2024, 96 : 112 - 123
  • [4] Anomaly detection in IoT-based healthcare: machine learning for enhanced security
    Khan, Maryam Mahsal
    Alkhathami, Mohammed
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01):
  • [5] A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats
    Alsalman, Dheyaaldin
    [J]. IEEE ACCESS, 2024, 12 : 14719 - 14730
  • [6] Enhancing IoT anomaly detection performance for federated learning
    Weinger, Brett
    Kim, Jinoh
    Sim, Alex
    Nakashima, Makiya
    Moustafa, Nour
    Wu, K. John
    [J]. DIGITAL COMMUNICATIONS AND NETWORKS, 2022, 8 (03) : 314 - 323
  • [7] Enhancing IoT Anomaly Detection Performance for Federated Learning
    Weinger, Brett
    Kim, Jinoh
    Sim, Alex
    Nakashima, Makiya
    Moustafa, Nour
    Wu, K. John
    [J]. 2020 16TH INTERNATIONAL CONFERENCE ON MOBILITY, SENSING AND NETWORKING (MSN 2020), 2020, : 206 - 213
  • [8] Enhancing IoT anomaly detection performance for federated learning
    Brett Weinger
    Jinoh Kim
    Alex Sim
    Makiya Nakashima
    Nour Moustafa
    KJohn Wu
    [J]. Digital Communications and Networks, 2022, 8 (03) - 323
  • [9] Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration
    Nazir, Ahsan
    He, Jingsha
    Zhu, Nafei
    Wajahat, Ahsan
    Ullah, Faheem
    Qureshi, Sirajuddin
    Ma, Xiangjun
    Pathan, Muhammad Salman
    [J]. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2024, 36 (02)
  • [10] Enhancing IoT Device Security: A Comparative Analysis of Machine Learning Algorithms for Attack Detection
    Alzahrani, Abdulaziz
    Alshammari, Abdulaziz
    [J]. FORTHCOMING NETWORKS AND SUSTAINABILITY IN THE AIOT ERA, VOL 1, FONES-AIOT 2024, 2024, 1035 : 71 - 91