Security Testing for Mobile Applications Using AI and ML Algorithms

Abstract

Mobile apps have revolutionized the digital world, making mobile devices essential to billions of users' everyday lives. This growth in mobile use has also increased security concerns to mobile apps, from data breaches to malicious software assaults. Traditional security testing methodologies, although useful, sometimes fail to address these attackers' sophistication and evolution. This study examines the use of AI and ML algorithms in mobile application security testing to improve vulnerability discovery, analysis, and mitigation.
AI and ML algorithms use massive volumes of data and real-time analytics to spot vulnerabilities faster and more accurately than conventional security testing techniques. These technologies enable automated code analysis, anomaly detection, behavioral analysis, and penetration testing, creating a proactive and adaptive security framework. Automation employing AI and ML may find source code security flaws by learning from a massive database of known vulnerabilities and applying it to fresh code. This speeds up manual code checks and improves vulnerability detection. Anomaly detection techniques may monitor application user behavior for abnormalities that may signal security issues like illegal access or data exfiltration.
By identifying unusual user behavior and highlighting it, behavioral analysis improves application security. This method detects suspicious activity in real time, allowing fast threat action. AI-driven penetration testing may also mimic complex attacks to find application defensive gaps that hostile actors might exploit.
Due to frequent app updates and feature additions, AI and ML in mobile application security testing provide continuous security evaluation. These algorithms can learn and adapt to new risks, keeping security testing current and effective as threats change. Implementing AI and ML in security testing is difficult. AI systems may falsely label normal actions as security risks, which is a major worry. This might cause unneeded interruptions and diminish system dependability. Large datasets used to train AI algorithms present privacy and ethical problems. Despite these limitations, AI and ML in mobile app security assessment have substantial advantages. These technologies are crucial in the fight against mobile security risks because they can analyze massive volumes of data, discover complicated patterns, and respond to emerging threats in real time. AI and ML in security testing will likely become mainstream as mobile apps become more complicated and important, assuring user security and reliability.
This article indicates that AI and ML in mobile application security testing advances cybersecurity. These solutions solve mobile app security issues by improving security testing accuracy, speed, and flexibility. To properly secure mobile apps using AI and ML, future research should address security testing difficulties including false positives and data privacy.