AI’s Role in Strengthening IoT Security

By: Gonipalli Bharath, Vel Tech University, Chennai, India, International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan; gonipallibharath@gmail.com

Abstract:

The rapid growth of IoT has brought many positive effects, but at the same time, it has also presented new risks related to cybersecurity. Artificial Intelligence significantly underpins the improvement in IoT security to automate defense mechanisms, identify real-time threats, detect anomalies, and perform predictive analysis. The article examines the ways in which AI improves security in IoT systems. It does this by reviewing recent research in AI-based solutions, providing a methodology for the implementation of such solutions, and summarizing implications for IoT cybersecurity going forward.

Introduction:

Numerous items, from industrial sensors to smart appliances, are connected by the Internet of Things (IoT). Unauthorized access, viruses, and data breaches are just a few of the cyberattacks that are becoming more likely as connection increases. Because IoT networks are so large and incorporate so many different types of devices, traditional security techniques are frequently insufficient to meet the special difficulties they provide. With its sophisticated security features that assist in real-time cyber threat detection, prevention, and response, artificial intelligence (AI) provides a potent remedy. This article explores current research, approaches, and emerging developments in the sector to emphasize AI’s contribution to improving IoT security.

Literature Survey:

Artificial Intelligence(AI) has emerged as an important player in IoT cybersecurity, and a number of studies demonstrate how well it secures IoT systems.

  • Anomaly Detection:

This is one of the main applications of AI in IoT security. Unusual device behaviours that could indicate an attack are identified with the aid of machine learning algorithms, such as clustering and classification approaches demonstrated that machine learning algorithms could detect data pattern aberrations, which are frequently a sign of security risks [[1]].

  • Intrusion Detection Systems (IDS):

AI-based intrusion detection systems (or) IDS, are extensively used in Internet of Things networks. Such systems employ artificial intelligence (AI) to track device interactions and network traffic, learning to differentiate between benign and malevolent activity. The superiority of AI-enhanced IDS systems over conventional signature-based techniques [[2]].

  • Predictive Security:

By examining trends in past data, AI is also able to anticipate possible risks. And predictive algorithms may anticipate IoT device vulnerabilities based on historical security events, enabling businesses to proactively fix flaws [[3]]. AI-powered automated responses include the ability to protect critical data, isolate affected devices, and block malicious traffic [[4]]. This autonomous response capability is essential for reducing downtime and minimizing damage during intrusions. (Villegas-Ch, García-Ortiz, and Sánchez-Viteri) investigated how AI could facilitate automatic, real-time mitigation in Internet of Things systems [[5]].

Methodology:

The following procedures must be followed in order to implement AI-based solutions for IoT security:

  • Data collection:

To create datasets for machine learning model training, it is crucial to continuously monitor network traffic and IoT device activities. Sensor readings, traffic logs, device behaviour, and user interactions are a few examples of this data.

  • AI Model Training:

Machine learning models, such as supervised and unsupervised learning algorithms, are trained on gathered data in order to identify typical patterns of behavior and identify deviations. This is often accomplished by algorithms like support vector machines, decision trees, and neural networks.

  • Anomaly Detection and Threat Identification: 

AI models continuously examine the data stream from IoT devices after they have been trained for anomaly detection and threat identification. Unusual traffic, illegal access attempts, or strange device activity are examples of anomalies that the system detects and takes action against to stop the problem from getting worse.

  • Predictive Analysis:

Predictive analysis is the process of using AI models to predict possible vulnerabilities by examining patterns in previous device vulnerabilities and hacks. To protect IoT systems, proactive patching or configuration adjustments are made possible by predictive analysis.

  • Automated Response:

AI can initiate automated responses to stop additional harm, such as isolating a device, blocking dubious IP addresses, or encrypting private conversations, if a threat has been identified.

Flowchart Representation:

From data gathering and model training to threat detection, prediction, and automated actions, this flowchart illustrates the process of integrating AI into IoT security systems. Every step is necessary to guarantee a safe IoT ecosystem that can react to threats quickly and efficiently.

Conclusion:

Artificial intelligence is proving to be a potent instrument for improving IoT system security. AI reduces the dangers brought on by the increasing number of connected devices by providing real-time threat identification, anomaly detection, predictive protection, and automated actions. There will probably be more resilient, self-sustaining security systems that can change and adjust to new threats as AI in IoT security continues to advance. In order to preserve the security and integrity of IoT networks as they grow more intricate, AI integration will be essential.

References:

  1. Hulayyil, Sarah Bin, Shancang Li, and Lida Xu. “Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security.” Electronics 12, no. 18 (January 2023): 3927. https://doi.org/10.3390/electronics12183927.
  2. Markevych, Michal, and Maurice Dawson. “A Review of Enhancing Intrusion Detection Systems for Cybersecurity Using Artificial Intelligence (AI).” International Conference KNOWLEDGE-BASED ORGANIZATION 29, no. 3 (June 1, 2023): 30–37. https://doi.org/10.2478/kbo-2023-0072.
  3. Srivastava, Astha, Shashank Gupta, Megha Quamara, Pooja Chaudhary, and Vidyadhar Jinnappa Aski. “Future IoT-Enabled Threats and Vulnerabilities: State of the Art, Challenges, and Future Prospects.” International Journal of Communication Systems 33, no. 12 (2020): e4443. https://doi.org/10.1002/dac.4443.
  4. Syed, Aamiruddin. “AI-Powered Threat Detection and Mitigation.” In Supply Chain Software Security: AI, IoT, and Application Security, edited by Aamiruddin Syed, 249–87. Berkeley, CA: Apress, 2024. https://doi.org/10.1007/979-8-8688-0799-2_6.
  5. Villegas-Ch, William, Joselin García-Ortiz, and Santiago Sánchez-Viteri. “Toward Intelligent Monitoring in IoT: AI Applications for Real-Time Analysis and Prediction.” IEEE Access 12 (2024): 40368–86. https://doi.org/10.1109/ACCESS.2024.3376707.
  6. Alweshah, M., Khalaileh, S. A., Gupta, B. B., Almomani, A., Hammouri, A. I., & Al-Betar, M. A. (2022). The monarch butterfly optimization algorithm for solving feature selection problems. Neural Computing and Applications, 1-15.
  7. Manasrah, A. M., Aldomi, A. A., & Gupta, B. B. (2019). An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Cluster Computing, 22, 1639-1653.

Cite As

Bharath G. (2025) AI’s Role in Strengthening IoT Security, Insights2Techinfo, pp. 1

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