Trojan Detection And Analysis In Android Application Using Machine Learning

In reaction to the pressing need for enhanced cybersecurity, the Trojan Detection Using Machine Learning project successfully detects and eradicates trojan threats, especially within mobile settings such as Android.Trojan poses significant risks as it can jeopardize networks, disrupt services, and acquire sensitive information.Traditional Trojan detection methods, such as those based on signatures, often struggle to recognize new and evolving Trojan variants.This study employs ML methods to develop an advanced and adaptable detection system capable of identifying both familiar and novel trojan threats.By examining various network traffic features, including flow duration, packet metrics, and timing information, the system provides a concise overview of system activity.The detection framework includes several machine learning models, such as Random Forest, SVM, and Logistic Regression.To distinguish between harmful and harmless behavior, these models analyze network traffic patterns.Random Forest stands out as a strong candidate for practical trojan detection applications due to its promising results, which feature high accuracy and reliability in detection.To ensure the system remains effective and valuable, the project aims to lower false positives and enhance detection rates.The system's live monitoring capabilities improve security by quickly detecting potential Trojan threats and preventing harm.Additionally, its flexible design can evolve to handle emerging Trojan variants and ensures robust protection against new online dangers.