A Quick Survey to Enhance IoT Security: The Role of Intrusion Detection Systems in Addressing Cyber Threats
Keywords:
Internet of Things (IOT), Intrusion Detection Systems (IDS), Machine Learning (ML), Deep learning (DL) and cyberattacks.Abstract
The rapid growth of Internet of Things (IoT) devices has transformed industries like healthcare and smart cities by improving connectivity and efficiency. However, this increased connectivity has also brought serious security risks, making IoT devices common targets for cyberattacks. Protecting these devices is essential to ensure the safety of critical systems and user privacy. Intrusion Detection Systems (IDS) play a key role in identifying and preventing malicious activities in IoT networks by using the techniques offered by Machine Learning (ML) and Deep learning (DL). This survey looks at the unique security challenges in IoT, highlights the importance of IDS in addressing these challenges, and discusses gaps in current research. It aims to provide simple and practical ideas for building better IDS solutions to secure IoT environments effectively.
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The overarching problem lies in developing effective and efficient intrusion detection mechanisms specifically designed for IoT environments. Conventional intrusion detection methods, largely developed for traditional networks, may not be directly applicable or optimized to address the unique challenges posed by IoT ecosystems. There exists a critical need for tailored intrusion detection approaches that consider the resource constraints, diverse communication protocols, and the dynamic nature of IoT networks. Research also shows that using techniques like FW-SMOTE can help fix problems with unbalanced data, making intrusion detection more effective. Many research works have focused on how deep learning can improve security in IoT, especially for healthcare systems, by detecting intrusions and anomalies in real time. Overall, combining different machine learning methods and adapting them to the specific needs of IoT can help strengthen security. As IoT systems grow, continued research and development of better IDS will be key to keeping these systems safe from new cyber threats. In conclusion, as IoT devices are used more in areas like healthcare and industry, protecting them from cyber threats becomes increasingly important. Intrusion detection systems (IDS) that use machine learning, especially deep learning models like CNN and GRU, can help detect unusual behavior and improve security. These models have been shown to work better than traditional methods in terms of accuracy and efficiency.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Articles in this journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. This license permits others to copy, distribute, and adapt the work, provided it is for non-commercial purposes, and the original author and source are properly credited.
