AI Fortification: Safeguarding IoT Systems Through Comprehensive Algorithmic Approaches

By Muhammad OsamaReviewed by Susha Cheriyedath, M.Sc.Dec 5 2023

In an article published in the journal Scientific Reports, researchers discussed the use of different Artificial Intelligence (AI) algorithms for enhancing the security and performance of Internet of Things (IoT) systems composed of interconnected devices that collect and exchange data from the environment. The team used different AI algorithms to classify and detect IoT attacks and intrusions and suggested a new and general taxonomy of AI methodologies for IoT security.

Background

An IoT system is a network of interconnected devices equipped with sensors and communication capabilities. These devices collect and exchange data, enabling them to interact and respond to each other or external inputs, contributing to the seamless integration of physical and digital environments for various applications. These systems are vulnerable to various security attacks due to their heterogeneity, scalability, interoperability, and resource constraints. Protecting IoT systems from vulnerabilities and malicious activity requires security solutions such as encryption, access control, key management, and intrusion detection systems (IDS).

AI technology is promising and has the potential to protect IoT systems. It can improve the performance and accuracy of IoT security systems using machine learning (ML) and deep learning (DL) methods. ML and DL are subsets of AI that enable machines to learn from data and perform tasks that require human intelligence. These methods can classify and detect IoT attacks based on classes, such as supervised, unsupervised, and reinforced classes.

About the Research

Researchers conducted a comprehensive and systematic comparison of the performance or the accuracy rate of different AI algorithms that have been used for improving IoT security systems. . This paper reviews existing studies on IoT security using AI methods such as ML and DL from 2018 to 2023 and analyzes the results of various AI algorithms that have been applied to IoT datasets for classifying and detecting IoT attacks and intrusions. It also proposes a new and general taxonomy of AI techniques for IoT security, which includes supervised, unsupervised, and hybrid classification methods.

Study Findings

The findings show that the most commonly used AI algorithms for IoT security are Support Vector Machine (SVM), K-nearest neighbors Algorithm (KNN), Decision Tress (DT), Naïve-Bayes (NB) classifier, Random Forest (RF), Linear Regression (LR), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Feed Forward Neural Network (FFNN), Long Short-Term Memory (LSTM), MLP (Multi-Layer Perceptron), and AE (Autoencoder). These algorithms can be classified into three categories: supervised, unsupervised, and hybrid AI techniques. Additionally, the most suitable IoT datasets for AI algorithms are Blockchain of Things for the Internet of Things (BoT-IoT), NSL-KDD, CICIDS2017, and UNSW-NB15.

On comparing the accuracy rates of different AI algorithms on different IoT datasets, it was found that the best AI algorithm for IoT security is LSTM, which achieves an accuracy rate of 99.97% on the BoT-IoT dataset. The study also discusses the advantages and disadvantages of each AI algorithm and the challenges and limitations of using AI for IoT security.

This research has potential applications in various fields that use IoT systems, such as healthcare, agriculture, smart homes, smart cities, education, smart grid, smart business, etc. It can help address different IoT security problems such as intrusion detection, anomaly detection, attack classification, etc., and help researchers select the most suitable AI technique for their IoT security problem based on the accuracy rate and other factors such as computational complexity, data requirement, explainability, etc. It can also be used to design and develop new and improved AI techniques for IoT security by addressing the limitations and challenges of the existing techniques.

Conclusion

In conclusion, this paper comprehensively explains the potential of AI algorithms for improving IoT security systems by providing intelligent methods for classifying and detecting IoT attacks and intrusions. It concludes that although there is no single best AI technique for IoT security, LSTM is the best-performing AI algorithm based on the accuracy rate. Different AI techniques have different advantages and disadvantages depending on the IoT security problem. BoT-IoT, NSL-KDD, CICIDS2017, and UNSW-NB15 were the most suitable IoT datasets for AI algorithms.

The researchers also presented a new and general taxonomy of AI techniques for IoT security, including supervised, unsupervised, and hybrid classification methods. They suggest that future research should consider other evaluation metrics besides accuracy, such as precision, recall, F1-score, etc.

Some future directions for AI and IoT security research include using hybrid AI techniques, developing new IoT datasets, addressing the challenges and limitations of AI for IoT security, and exploring other AI techniques such as Explainable AI (XAI), Generative Adversarial Networks (GANs), etc. Furthermore, the study highlights the importance of human factors and ethical issues in AI and IoT security.