The evolution and rapid adoption of the Internet of Things (IoT) led to a rise in the number of attacks that target IoT environments. IoT environments are vulnerable to several attacks because many devices lack memory, processing power, and battery. Most of these vulnerabilities are relatively easy to mitigate when best practices are followed. However, even when best practices are followed, an attack to obtain a device credential and use it to generate false data is difficult to detect. Such an attack is called a replication attack and its impact can be catastrophic in crucial IoT scenarios such as smart transportation. In this sense, this paper proposes a solution to detect these attacks by analyzing abnormal network traffic through machine learning.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Detecting Compromised IoT Devices Through XGBoost



    Published in:

    Publication date :

    2023-12-01


    Size :

    576954 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    PV Power Prediction Based on XGBoost Algorithm

    Hong, Yufan / Yang, Jingxian / Yang, Zhen et al. | IEEE | 2023


    Urban Rail Transit Passenger Flow Forecasting—XGBoost

    Sun, Xiaoli / Zhu, Caihua / Ma, Chaoqun | ASCE | 2022


    Traffic accident prediction system based on Ada-XGBoost

    CHANG RUNQI | European Patent Office | 2021

    Free access