Using Machine Learning Multiclass Classification Technique to Detect IoT Attacks in Real Time

被引:3
作者
Alrefaei, Ahmed [1 ]
Ilyas, Mohammad [1 ]
机构
[1] Florida Atlantic Univ, Elect Engn & Comp Sci, 777 Glades Rd, Boca Raton, FL 33431 USA
关键词
Internet of Things; intrusion detection system; machine learning; PySpark architecture;
D O I
10.3390/s24144516
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
This paper presents a real-time intrusion detection system (IDS) aimed at detecting the Internet of Things (IoT) attacks using multiclass classification models within the PySpark architecture. The research objective is to enhance detection accuracy while reducing the prediction time. Various machine learning algorithms are employed using the OneVsRest (OVR) technique. The proposed method utilizes the IoT-23 dataset, which consists of network traffic from smart home IoT devices, for model development. Data preprocessing techniques, such as data cleaning, transformation, scaling, and the synthetic minority oversampling technique (SMOTE), are applied to prepare the dataset. Additionally, feature selection methods are employed to identify the most relevant features for classification. The performance of the classifiers is evaluated using metrics such as accuracy, precision, recall, and F1 score. The results indicate that among the evaluated algorithms, extreme gradient boosting achieves a high accuracy of 98.89%, while random forest demonstrates the most efficient training and prediction times, with a prediction time of only 0.0311 s. The proposed method demonstrates high accuracy in real-time intrusion detection of IoT attacks, outperforming existing approaches.
引用
收藏
页数:19
相关论文
共 34 条
  • [1] A New Ensemble-Based Intrusion Detection System for Internet of Things
    Abbas, Adeel
    Khan, Muazzam A.
    Latif, Shahid
    Ajaz, Maria
    Shah, Awais Aziz
    Ahmad, Jawad
    [J]. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2022, 47 (02) : 1805 - 1819
  • [2] Machine learning approaches to IoT security: A systematic literature review
    Ahmad, Rasheed
    Alsmadi, Izzat
    [J]. INTERNET OF THINGS, 2021, 14
  • [3] Alghamdi Rubayyi, 2021, 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), P561, DOI 10.1109/ICAIBD51990.2021.9458974
  • [4] [Anonymous], Modeling an Inclusive Digital Future
  • [5] Anthi E., 2020, A Supervised Intrusion Detection System for Smart Home IoT Devices
  • [6] Apache Spark Classification and Regression, about Us
  • [7] Apache Spark Home Page, about Us
  • [8] Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach
    Bhandari, Guru
    Lyth, Andreas
    Shalaginov, Andrii
    Gronli, Tor-Morten
    [J]. ELECTRONICS, 2023, 12 (02)
  • [9] IoT anomaly detection methods and applications: A survey
    Chatterjee, Ayan
    Ahmed, Bestoun S.
    [J]. INTERNET OF THINGS, 2022, 19
  • [10] Choudhary P., 2021, P 2021 IEEE BOMB SEC, P1