The Future of IoT Security : Closing the Gaps to Prevent Cybercrimes

By: Vanna karthik; Vel Tech University, Chennai, India

Abstract

The way gadgets interact has been completely transformed by the Internet of Things (IoT), which has made it possible for smooth communication and automated in a variety of industries. However, serious weaknesses in security identified by the quick spread of IoT devices have made people and businesses more vulnerable to cybercrimes. The future of IoT security is examined in this article, with a focus on methods to stop possible threats and solve current security problems. In order to improve the capacity for recovery of IoT systems, it looks at best methods, upcoming technology, and regulatory frameworks. In order to help stakeholders create a safer IoT ecosystem, this study tries to provide practical advice by addressing present issues and predicting future developments.

Introduction

A revolutionary development in technology, the Internet of Things (IoT) links billions of objects globally to boost productivity, convenience, and creativity. However, the IoT ecosystem’s exposure to cyberattacks increases with its growth. IoT devices have become popular targets for attackers due to their broad nature, poor encryption, and poor security measures. Cybercrimes including distributed denial-of-service (DDoS) crimes, illegal access, and data breaches have brought attention to how urgently IoT security has to be made stronger.

The current status of IoT security, remaining gaps, and innovations needed to reduce risks are all examined in this article. Stakeholders may guarantee that IoT keeps advancing society without sacrificing security and privacy by filling in these gaps. In addition, the study examines how blockchain, machine learning, and artificial intelligence (AI) may help secure IoT systems while promoting strong industry standards and legal frameworks.

Literature review

The complex task of protecting a highly interconnected and diverse environment is shown by the literature on IoT security. Many IoT devices lack established security standards, which leaves them vulnerable to assaults, according to research by [1]. Stronger security measures are also recommended by studies that highlight the negative social and economic effects of IoT-related cybercrimes.

New technologies like artificial intelligence (AI) and machine learning (ML) have demonstrated promise in real-time threat identification and mitigation[2].examines several deep learning and machine learning methods for protecting IoT apps and networks. demonstrates, using a smart home as a case study, how blockchain can protect IoT devices. Focuses on real-world uses and difficulties while discussing how AI may improve IoT security[2].

A diagram of a computer network

Description automatically generated

Fig : IoT Security Architecture[3].

The applications and role of ML, DL, AI and Blockchain in IoT security

Technology

Role in IoT security

Applications

Machine Learning(ML)

identifies risks and automates reaction methods by learning from the behavior of IoT systems.

malware identification, user behavior analysis, and detection of breaches.

Deep Learning(DL)

analyzes huge quantities of IoT data to find complex and unidentified attack patterns.

effective threat detection, natural language processing in gadgets, and image recognition for IoT-enabled cameras.

Artificial Intelligence(AI)

improves threat prevention and detection through the analysis of detailed data patterns.

security incident response, predictive maintenance, and anomaly detection.

Blockchain

gives IoT data decentralized, unchangeable storage.

Secure communication, data integrity, and

secure device authentication.

Although these developments, there are still weaknesses in complete encryption, secure firmware updates, and authentication of devices. Furthermore, attempts to establish consistent security standards across IoT ecosystems are challenged by a lack of consistent worldwide rules and regulations. These issues are noted in this review, which also provides the foundation for researching creative solutions.

Methodology

The future of IoT security is examined in this study using an approach based on mixed methods that combines both quantitative and qualitative evaluations. Included in the study are:

1. Literature Analysis: Examining academic papers, business reports, and case studies to determine the most recent developments and difficulties in IoT security.

2. Case Studies: Studying actual instances of IoT-related cyberattacks to identify weak points and practical defenses.

The information gathered using these techniques will be combined to create realistic plans for addressing IoT security flaws and stopping online fraud.

Conclusion

Immediate steps to fix current issues and detect new threats are essential to the security of the Internet of Things in the future. This study highlights the necessity of strong authentication procedures, safe communication protocols, and ongoing AI and ML-powered monitoring. Additionally, blockchain technology has the potential to improve IoT network honesty and confidence. To further guarantee clarity and consistency, international regulatory frameworks and industry standards must be established.

The IoT ecosystem might be protected against cybercrimes by encouraging cooperation among stakeholders, such as governments, corporations, and academic institutions. In addition to protecting consumers, putting security first will maintain innovation and confidence in technologies that are connected as IoT use grows.

References

  1. O. I. Abiodun, E. O. Abiodun, M. Alawida, R. S. Alkhawaldeh, and H. Arshad, “A Review on the Security of the Internet of Things: Challenges and Solutions,” Wirel. Pers. Commun., vol. 119, no. 3, pp. 2603–2637, Aug. 2021, doi: 10.1007/s11277-021-08348-9.
  2. B. K. Mohanta, D. Jena, U. Satapathy, and S. Patnaik, “Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology,” Internet Things, vol. 11, p. 100227, Sep. 2020, doi: 10.1016/j.iot.2020.100227.
  3. “Understanding IoT Security – Part 1 of 3: IoT Security Architecture on the Device and Communication Layers,” IoT Analytics. Accessed: Dec. 30, 2024. [Online]. Available: https://iot-analytics.com/understanding-iot-security-part-1-iot-security-architecture/
  4. Singh, A., & Gupta, B. B. (2022). Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-43.
  5. Bhatti, M. H., Khan, J., Khan, M. U. G., Iqbal, R., Aloqaily, M., Jararweh, Y., & Gupta, B. (2019). Soft computing-based EEG classification by optimal feature selection and neural networks. IEEE Transactions on Industrial Informatics, 15(10), 5747-5754.
  6. Deepak Mahto, Sudhakar Kumar (2022) Log4Shell Vulnerability, Insights2Techinfo, pp.1

Cite As

Karthik V. (2025) The Future of IoT Security : Closing the Gaps to Prevent CybercrimesProtection, Insights2techinfo pp.1

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