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Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review
Symbiosis International (Deemed) University, India.ORCID iD: 0000-0002-3790-8659
Symbiosis International (Deemed) University, India.ORCID iD: 0000-0002-4507-1844
2021 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 59353-59377Article in journal (Refereed) Published
Sustainable development
SDG 17: Strengthen the means of implementation and revitalize the global partnership for sustainable development
Abstract [en]

Internet of Things (IoT) technology is prospering and entering every part of our lives, be it education, home, vehicles, or healthcare. With the increase in the number of connected devices, several challenges are also coming up with IoT technology: heterogeneity, scalability, quality of service, security requirements, and many more. Security management takes a back seat in IoT because of cost, size, and power. It poses a significant risk as lack of security makes users skeptical towards using IoT devices. This, in turn, makes IoT vulnerable to security attacks, ultimately causing enormous financial and reputational losses. It makes up for an urgent need to assess present security risks and discuss the upcoming challenges to be ready to face the same. The undertaken study is a multi-fold survey of different security issues present in IoT layers: perception layer, network layer, support layer, application layer, with further focus on Distributed Denial of Service (DDoS) attacks. DDoS attacks are significant threats for the cyber world because of their potential to bring down the victims. Different types of DDoS attacks, DDoS attacks in IoT devices, impacts of DDoS attacks, and solutions for mitigation are discussed in detail. The presented review work compares Intrusion Detection and Prevention models for mitigating DDoS attacks and focuses on Intrusion Detection models. Furthermore, the classification of Intrusion Detection Systems, different anomaly detection techniques, different Intrusion Detection System models based on datasets, various machine learning and deep learning techniques for data pre-processing and malware detection has been discussed. In the end, a broader perspective has been envisioned while discussing research challenges, its proposed solutions, and future visions.

Place, publisher, year, edition, pages
IEEE, 2021. Vol. 9, p. 59353-59377
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Information Systems
Identifiers
URN: urn:nbn:se:lnu:diva-119179DOI: 10.1109/access.2021.3073408ISI: 000642756400001Scopus ID: 2-s2.0-85106538347OAI: oai:DiVA.org:lnu-119179DiVA, id: diva2:1735253
Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-02-10Bibliographically approved

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Pandya, Sharnil

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