Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Term Frequency Tokenization for Fake News Detection Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Attack Detection in IoT Using Machine Learning—A Survey

Authors : Saeed Ali Haifa Ali, J. Vakula Rani

Published in: Intelligent Cyber Physical Systems and Internet of Things

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In the last decade, the İnternet Of Things(IoT) increased dramatically until it became an integral part of our daily lives. These devices are interconnected to the internet without the need for human intervention. Due to the weak configuration and unique characteristics of the internet of things has become a robust target for cyber-attack that worry the general user of these devices. Furthermore, IoT security challenges are increasing day by day and are subject to a variety of attacks. The traditional security measures, such as authentication, access control, network security, and encryption, for IoT devices and their vulnerabilities, are insufficient, ineffective, and cannot process these issues. Existing security methods must be improved to protect the IoT environment. ML/DL provided many solutions that assisted solve the challenges of the IoT and provided safety for it. The goal of this paper is to provide a study on the attacks in IoT architectures such as the sensing layer, network layer, and application layer, then present ML and DL that contributed to the solution in attack detection. In addition, we discuss the challenges of IoT architectures.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Developing Machine Learning Based Mobile App for Agriculture Application
next chapter An Extensive Study on Logic Emerging IoT Adiabatic Techniques for Low-Power Circuit
Literature
1.
Zaman S et al (2021) Security threats and artificial intelligence based countermeasures for internet of things networks: a comprehensive survey. IEEE Access
2.
Lin J et al (2017) A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE İnternet of Things J 4(5):1125–1142
3.
Liang X, Kim Y (2021) A survey on security attacks and solutions in the IoT network. In: 2021 IEEE 11th annual computing and communication workshop and conference (CCWC). IEEE
4.
Upadhyaya B, Sun S, Sikdar B (2019) Machine learning-based jamming detection in wireless iot networks. In: 2019 IEEE VTS Asia Pacific wireless communications symposium (APWCS). IEEE
5.
Khatun MA, Chowdhury N, Uddin MN (2019) Malicious nodes detection based on artificial neural network in IoT environments. In: 2019 22nd International conference on computer and ınformation technology (ICCIT). IEEE
6.
Liu G et al (2021) Softwarized iot network immunity against eavesdropping with programmable data planes. IEEE Internet of Things J 8(8):6578–6590
7.
Shaniqua A, Mehmood A, Elhadef M (2021) Detecting signal spoofing attack in uavs using machine learning models. IEEE Access 9:93803–93815CrossRef
8.
Pathak AK et al (2021) Anomaly detection using machine learning to discover sensor tampering in IoT systems. In: ICC 2021-IEEE ınternational conference on communications. IEEE
9.
Mrabet H et al (2020) A survey of IoT security based on a layered architecture of sensing and data analysis. Sensors 20(13):3625.
10.
Hassija V et al (2019) A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access 7:82721–82743
11.
Amrish R, Bavapriyan K, Gopinaath V, Jawahar A, Vinoth Kumar C (2022) DDoS detection using machine learning techniques. J IoT Soc Mobile Analytics Cloud 4(1):24–32
12.
Kiran KVVNLS et al (2020) Building a intrusion detection system for iot environment using machine learning techniques. Procedia Comput Sci 171:2372–2379‏
13.
Newaz AKMI et al (2020) Heka: a novel intrusion detection system for attacks to personal medical devices. In: 2020 IEEE conference on communications and network security (CNS). IEEE
14.
Farzaneh B et al (2020) A new method for intrusion detection on RPL routing protocol using fuzzy logic. In: 2020 6th International conference on web research (ICWR). IEEE
15.
Alaiz-Moreton H et al (2019) Multiclass classification procedure for detecting attacks on MQTT-IoT protocol. Complexity 2019
16.
Anthi E et al (2019) A supervised intrusion detection system for smart home IoT devices. IEEE Internet of Things J 6(5):9042–9053
17.
Tawalbeh L et al (2020) IoT privacy and security: challenges and solutions. Appl Sci 10(12):4102
18.
Asharf J et al (2020) A review of intrusion detection systems using machine and deep learning in internet of things: challenges, solutions and future directions. Electronics 9(7):1177
19.
Azmoodeh A, Dehghantanha A, Choo KKR (2018) Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning. IEEE Trans Sustain Comput 4(1):88–95
20.
Mao J et al (2019) Phishing page detection via learning classifiers from page layout feature. EURASIP J Wirel Commun Netw 2019(1):1–14.
21.
Moti Z et al (2021) Generative adversarial network to detect unseen internet of things malware. Ad Hoc Netw 122(2021):102591
22.
Shafiq M et al (2020) CorrAUC: a malicious bot-IoT traffic detection method in IoT network using machine-learning techniques. IEEE Internet of Things J 8(5):3242–3254
23.
Abdulwahab HM, Ajitha S, Saif MAN (2022) Feature selection techniques in the context of big data: taxonomy and analysis. Appl Intell 1–46
24.
Sarkar S, Liu J, Jovanov E (2019) A robust algorithm for sniffing ble long-lived connections in real-time. In: 2019 IEEE global communications conference (GLOBECOM). IEEE
25.
Goel A et al (2019) DeepRing: protecting deep neural network with blockchain. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops
26.
Aref MA, Jayaweera SK, Machuzak S (2017) Multi-agent reinforcement learning based cognitive anti-jamming. In: 2017 IEEE wireless communications and networking conference (WCNC). IEEE
27.
Han G, Xiao L, Vincent Poor H (2017) Two-dimensional anti-jamming communication based on deep reinforcement learning. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE
28.
Xiao L, Wan X, Han Z (2017) PHY-layer authentication with multiple landmarks with reduced overhead. IEEE Trans Wireless Commun 17(3):1676–1687CrossRef
29.
Shi C et al (2017) Smart user authentication through actuation of daily activities leveraging WiFi-enabled IoT. In: Proceedings of the 18th ACM ınternational symposium on mobile Ad Hoc networking and computing
30.
Singh R, Singh J, Singh R (2017) Fuzzy based advanced hybrid intrusion detection system to detect malicious nodes in wireless sensor networks. Wirel Commun Mobile Comput 2017
31.
Roopak M, Tian GY, Chambers J (2019) Deep learning models for cyber security in IoT networks. In: 2019 IEEE 9th annual computing and communication workshop and conference (CCWC). IEEE
32.
Napiah MN et al (2018) Compression header analyzer intrusion detection system (CHA-IDS) for 6LoWPAN communication protocol. IEEE Access 6:16623–16638
33.
Li Q et al (2017) Channel-based sybil detection in industrial wireless sensor networks: a multi-kernel approach. In; GLOBECOM 2017–2017 IEEE global communications conference. IEEE
34.
Dovom EM et al (2019) Fuzzy pattern tree for edge malware detection and categorization in IoT. J Syst Architect 97:1–7
35.
Parra GDLT et al (2020) Detecting Internet of Things attacks using distributed deep learning. J Netw Comput Appl 163:102662
36.
Ahmad Z et al (2021) Anomaly detection using deep neural network for IoT architecture. Appl Sci 11(15):7050
37.
Kamel DK (2021) Wireless IoT with blockchain-enabled technology amidst attacks. IRO J Sustain Wirel Syst 2(3):133–137CrossRef
38.
Amrish R, Bavapriyan K, Gopinaath V, Jawahar A, Vinoth Kumar C (2022) DDoS detection using machine learning techniques. J IoT Soc Mob Analytics Cloud 4(1):24–32
Metadata
Title
Attack Detection in IoT Using Machine Learning—A Survey
Authors
Saeed Ali Haifa Ali
J. Vakula Rani
Copyright Year
2023
Book
Intelligent Cyber Physical Systems and Internet of Things

Print ISBN: 978-3-031-18496-3

Electronic ISBN: 978-3-031-18497-0

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-18497-0_16

Premium Partner

    Image Credits