Advancing IoT Security: Cryptographic Enhancements and Open Challenges

The tremendous increase of data produced by Internet of Things (IoT) networks emphasizes how important to have robust security mechanisms in place to guarantee authenticity, integrity, and confidentiality. Due to inherent limitations of IoT devices, like limited memory, computing power, and energy consumption, they are particularly vulnerable to security threats and attacks. This paper comprehensively surveys possible cryptographic solutions to enhance IoT cybersecurity, discusses cryptographic algorithm classification, implementation problems, and performance parameters in the IoT ecosystem. This study explores common cryptographic optimization techniques using meta-heuristic algorithms (MHA), machine learning (ML), and blockchain (BC). A taxonomy of emerging technology solutions was demonstrated based on IoT security issues. Additionally, the paper explores cryptographic improvement challenges in the IoT environment with possible solutions.

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Publication Details

Journal
ZANCO Journal of Pure and Applied Sciences
Published
2025-10-31
DOI
https://doi.org/10.21271/zjpas.37.5.7
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

Advancing IoT Security: Cryptographic Enhancements and Open Challenges

Jawaher Abdulwahab Fadhil, Marwan Aziz Mohammed
ZANCO Journal of Pure and Applied Sciences
IoT and Edge/Fog Computing
article

Advancing IoT Security: Cryptographic Enhancements and Open Challenges

Jawaher Abdulwahab Fadhil, Marwan Aziz Mohammed
article en

Abstract

The tremendous increase of data produced by Internet of Things (IoT) networks emphasizes how important to have robust security mechanisms in place to guarantee authenticity, integrity, and confidentiality. Due to inherent limitations of IoT devices, like limited memory, computing power, and energy consumption, they are particularly vulnerable to security threats and attacks. This paper comprehensively surveys possible cryptographic solutions to enhance IoT cybersecurity, discusses cryptographic algorithm classification, implementation problems, and performance parameters in the IoT ecosystem. This study explores common cryptographic optimization techniques using meta-heuristic algorithms (MHA), machine learning (ML), and blockchain (BC). A taxonomy of emerging technology solutions was demonstrated based on IoT security issues. Additionally, the paper explores cryptographic improvement challenges in the IoT environment with possible solutions.

ZANCO Journal of Pure and Applied SciencesVol. 37(5)
Salahaddin University-Erbil (IQ)
Openalex Percentile: Top 63%
IoT and Edge/Fog Computing
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