Deep Insight: Enhancing Internet of Things (IoT) Security with Intrusive Deduction Systems (IDS)

Abstract

The Internet of Things (IoT) plays a vital role in recent technology by interconnecting various network devices that communicate with each other across the world via internet connections. These interconnected devices are embedded with multiple technologies, including hardware sensors, actuators, and software. Such devices allow for greater efficiency, convenience, and new possibilities in various aspects of daily life and industries like healthcare, transportation, and agriculture. Thus, IoT security is essential for protecting interconnected devices from cyber threats. As many devices are connected to the IoT system, the lack of security remains a primary task in preventing unauthorized access and data breaches. This paper uses Intrusion Detection Systems (IDS) powered by machine learning and deep learning to enhance IoT security. These systems monitor network traffic and device behavior to detect suspicious activities and threats, helping protect IoT systems from various attacks and unauthorized access. This work explores how IDS technology emphasizes the importance of security measures in safeguarding sensitive data and maintaining trust in IoT environments across different sectors.