Internet of Things: Evolution, Concerns and Security Challenges
Open accessAbstract
The escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives. The deployment of a large number of objects adhered to the internet has unlocked the vision of the smart world around us, thereby paving a road towards automation and humongous data generation and collection. This automation and continuous explosion of personal and professional information to the digital world provides a potent ground to the adversaries to perform numerous cyber-attacks, thus making security in IoT a sizeable concern. Hence, timely detection and prevention of such threats are pre-requisites to prevent serious consequences. The survey conducted provides a brief insight into the technology with prime attention towards the various attacks and anomalies and their detection based on the intelligent intrusion detection system (IDS). The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Additionally, a case study of healthcare in IoT is presented. The study depicts the architecture, security, and privacy issues and application of learning paradigms in this sector. The research assessment is finally concluded by listing the results derived from the literature. Additionally, the paper discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.
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Increasing adoption of IIoT technologies escalates security and privacy concerns, leading to information leakage, sensitive data disclosure, and data theft.
“With the increasing adoption of IIoT technologies, security and privacy concerns escalate, leading to information leakage, sensitive data disclosure, and data theft [12], [13].”
From A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario (2023) · cites this paper as [13]
The Introduction links IoT growth and integration to a larger attack surface, and §3.2 says attackers can steal valuable data through IoT network vulnerabilities. Section 5.3 identifies cyber threats in smart manufacturing, but it does not specifically link increasing IIoT adoption to information leakage, sensitive-data disclosure, and data theft in that setting. (LLM, conf=0.82)
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“With the increasing adoption of IIoT technologies, security and privacy concerns escalate, leading to information leakage, sensitive data disclosure, and data theft [12], [13].”
Background
- Authors
- Malhotra, P., Singh, Y., Anand, P., Bangotra, D. K., Singh, P. K., & Hong, W.-C.
- Affiliations
- Central University of Jammu (IN)
- Department of Higher Education (IN)
- ABES Engineering College (IN)
- Asia Eastern University of Science and Technology (TW)
- Corresponding author
- Singh, Pradeep Kumar
- Hong, Wei-Chiang
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-03-05
- Volume
- 21
- Issue
- 5
- Pages
- 1809
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7962037
- OpenAlex
- W3134260321
- Cited by
- 260
- References
- 133
- Citations by year
- 2026: 22 · 2025: 60 · 2024: 76 · 2023: 49 · 2022: 39 · 2021: 14
- FWCI
- 19.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology, Taiwan (MOST 108-2410-H-161-004)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- Internet of Things, Internet of Things security, network intrusion detection, intrusion detection systems, machine learning for intrusion detection, deep learning for intrusion detection, cyberattacks, anomaly detection, Internet of Medical Things, privacy, smart world
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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