Intelligence Technique for Fixing Vulnerabilities in Mobile Applications

ABSTRACT - The increasing reliance on mobile applications for both personal and business purposes have led to a rise in security vulnerabilities, posing significant risks to users and organizations alike. As mobile applications evolve in complexity, so do the methods employed by attackers to exploit weaknesses. Traditional security practices often fall short in addressing the unique challenges of mobile platforms. This paper explores advanced intelligence techniques for identifying, analyzing, and mitigating vulnerabilities in mobile applications. Using different types of algorithms such as Gaussian, K-neighbors, SVM, Naïve bayes & Random Forest Algorithm. By leveraging machine learning, artificial intelligence (AI), and behavioral analytics, this approach enables the proactive detection of security flaws and the implementation of adaptive defenses. Key techniques include static and dynamic analysis, anomaly detection, and automated penetration testing, which together enhance the ability to identify critical vulnerabilities early in the development lifecycle. The integration of AI-powered security tools not only streamlines the vulnerability assessment process but also empowers developers to prioritize remediation efforts based on the severity of the threat landscape. This paper aims to highlight the effectiveness of intelligence-driven security solutions in safeguarding mobile applications against emerging cyber threats and ensuring robust, secure user experiences. KEYWORDS: Data collection, Vulnerability testing, Gaussian, K-neighbors, SVM, Random Forest, Naïve bayes algorithm.