Optimizing Mobile Platform Security with AI-Powered Real-Time Threat Intelligence: A Study on Leveraging Machine Learning for Enhancing Mobile Cybersecurity

Abstract

The increasing prevalence of mobile platforms in everyday life has made them a significant target for cybersecurity threats. As these threats become more sophisticated, traditional security measures are insufficient in providing real-time, dynamic protection. In response, artificial intelligence (AI) and machine learning (ML) have emerged as critical tools for enhancing mobile platform security through real-time threat intelligence. This paper explores the application of AI-powered threat intelligence systems in mobile cybersecurity, with a specific focus on the role of machine learning models in identifying, anticipating, and mitigating threats in real-time. By leveraging large datasets, ML algorithms can detect anomalous behavior, adapt to new attack patterns, and offer predictive insights that traditional security methods fail to deliver.

The study begins with an overview of the growing cybersecurity risks associated with mobile platforms, such as malware, phishing, data breaches, and network-based attacks. These vulnerabilities are exacerbated by the proliferation of mobile applications, bring-your-own-device (BYOD) policies, and the increasing interconnectivity of devices through the Internet of Things (IoT). In this context, mobile platforms face challenges such as fragmented ecosystems, limited computational resources, and diverse attack surfaces, making them particularly vulnerable to both known and unknown threats. The limitations of existing security frameworks highlight the need for a more proactive and intelligent approach to mobile security, which this paper argues can be achieved through AI and machine learning-driven threat intelligence.