Review on intrusion detection system for IoT/IIoT -brief study

被引：5
作者：
Bansal, Komal [1 ]
Singhrova, Anita [1 ]
机构：
[1] Deenbandhu Chhotu Ram Univ Sci & Technol, CSE Dept, Murthal, Haryana, India
来源：
MULTIMEDIA TOOLS AND APPLICATIONS | 2023年 / 83卷 / 8期
关键词：
Internet of Things (IoT); Industrial Internet of Things (IIoT); Intrusion Detection System (IDS); Signature-based (IDS); Anomaly-based (IDS); Security; Machine learning; Deep learning; INDUSTRIAL IOT NETWORKS; ATTACK DETECTION SCHEME; DETECTION FRAMEWORK; ANOMALY DETECTION; INTERNET; MODEL;
D O I：
10.1007/s11042-023-16395-6
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Recently, the Internet of Thing's (IoT's) rising popularity is offering a promising opportunity not just aimed at the diverse home automation systems' expansion however as well aimed at diverse industrial applications. By leveraging these advantages, automation is implemented in the industries resulting in the Industrial IoT (IIoT). Even though IoT/IIoT simplifies the daily activities that benefit human operations, they cause severe security challenges that are worth focusing on. Consequently, IoT/IIoT yields effective and efficient solutions by implementing an Intrusion Detection System (IDS). The IDS is a solution aimed at addressing the security and privacy challenges of detecting diverse IoT/IIoT attacks. Diverse IDS methodologies are employed aimed at identifying intrusion within the data however still require enhancement on the detection system. A literature survey regarding the IDS in the IoT/IIoT topic is offered that largely concentrated on the research's present state by evaluating the literature, discovering the existent trends, and offering open problems and upcoming directions.
引用
收藏
页码：23083 / 23108
页数：26
相关论文
共 70 条
  • [1] A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT
    Aboelwafa, Mariam M. N.
    Seddik, Karim G.
    Eldefrawy, Mohamed H.
    Gadallah, Yasser
    Gidlund, Mikael
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (09): : 8462 - 8471
  • [2] Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
    Abu Al-Haija, Qasem
    Krichen, Moez
    Abu Elhaija, Wejdan
    [J]. ELECTRONICS, 2022, 11 (04)
  • [3] An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System
    Al-Abassi, Abdulrahman
    Karimipour, Hadis
    Dehghantanha, Ali
    Parizi, Reza M.
    [J]. IEEE ACCESS, 2020, 8 : 83965 - 83973
  • [4] A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
    Al-Garadi, Mohammed Ali
    Mohamed, Amr
    Al-Ali, Abdulla Khalid
    Du, Xiaojiang
    Ali, Ihsan
    Guizani, Mohsen
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2020, 22 (03): : 1646 - 1685
  • [5] Attack and Defense Strategies for Intrusion Detection in Autonomous Distributed IoT Systems
    Al-Hamadi, Hamid
    Chen, Ing-Ray
    Wang, Ding-Chau
    Almashan, Meshal
    [J]. IEEE ACCESS, 2020, 8 : 168994 - 169009
  • [6] HLMCC: A Hybrid Learning Anomaly Detection Model for Unlabeled Data in Internet of Things
    Alghanmi, Nusaybah
    Alotaibi, Reem
    Buhari, Seyed M.
    [J]. IEEE ACCESS, 2019, 7 : 179492 - 179504
  • [7] Alghuried A, 2017, THESIS DUBLIN I TECH
  • [8] Alghuried A, 2017, THESIS TECHNOLOGICAL, DOI [10.21427/D7WK7S, DOI 10.21427/D7WK7S]
  • [9] Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection
    Alhakami, Wajdi
    Alharbi, Abdullah
    Bourouis, Sami
    Alroobaea, Roobaea
    Bouguila, Nizar
    [J]. IEEE ACCESS, 2019, 7 : 52181 - 52190
  • [10] GARUDA: Gaussian dissimilarity measure for feature representation and anomaly detection in Internet of things
    Aljawarneh, Shadi A.
    Vangipuram, Radhakrishna
    [J]. JOURNAL OF SUPERCOMPUTING, 2020, 76 (06) : 4376 - 4413
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