Advancing Security of Mobile and IOT Devices with AI Threat Detection Strategies
Ali Shikai · 2024
Because of their ubiquitous connectivity and major influence on many sectors, mobile devices and the Internet of Things (IoT) are becoming even more vital. Through the app economy and mobile commerce, mobile devices stimulate economic growth and innovation; IoT improves efficiency and intelligence in fields including smart homes, healthcare, and industrial automation. These technologies' confluence guarantees flawless, data-driven experiences and promotes ongoing technological innovation, therefore transforming our way of life and employment. Data privacy issues, vulnerability to cyberattacks, and vulnerabilities resulting from insufficient security policies in many IoT devices define the security difficulties in the areas of mobile devices and IoT. Constant connectivity increases the possibility of network breaches; hence, maintaining compliance with data security rules is difficult. To reduce these threats, strong encryption, frequent upgrades, and strict access limits—all part of effective security strategies—are vital. By means of machine learning to recognize and react to threats in real-time, artificial intelligence-based threat detection improves security for mobile devices and IoT. Providing a proactive defense against changing cyber threats, it detects anomalies, forecasts breaches, and automates reactions. Based on their assessment criteria including accuracy, precision, recall, and f1-score, this study contrasts several machine learning and deep learning models for the most effective intrusion detection approach on mobiles devices and IOT. Comparative analysis and evaluation made use of many datasets