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Network Intrusion Detection of Android Smartphones Using Machine Learning and Ensemble Learning Techniques

Kumar, Sumit (2023) Network Intrusion Detection of Android Smartphones Using Machine Learning and Ensemble Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Mobile risks are growing at a quick rate as the number of mobile users significantly increases. Modern age’s major cyberattacks have been fuelled by the advanced development of mobile devices and technologies. The popular operating system Android, which is used in smartphones, is a prime target for dubious activities carried out by various intrusions and malware. Due to its popularity and open-source platform, Android has turned into a top target for unethical intrusions. A mobile virus can result in a number of cybersecurity problems. The volume of data created and the increase in zero-day threats make the present security applications insufficient to predict and detect intrusion with variable properties. These challenges have been addressed in recent years using machine learning classification algorithms, and this study compares classic machine learning models with ensemble learning models to determine which model can yield the greatest results. So, utilizing the Random Forest, Decision Tree, KNN techniques, and the Android Mischief dataset as training data, we presented a comparison between ensemble learning and the classic ML classification model in this study to detect remote access trojan (RAT). Metrics like accuracy, precision, and F1 score are used to measure the model's performance, and its performance is contrasted with that of more established models like Decision Tree and K Nearest Neighbor (KNN).

Item Type: Thesis (Masters)
Salahuddin, Jawad
Uncontrolled Keywords: Android; Remote Access Trojan; RAT; Network Intrusion Detection; Machine Learning; Ensemble Learning; Random Forest; Decision Tree; KNN
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Tamara Malone
Date Deposited: 28 Apr 2023 15:56
Last Modified: 28 Apr 2023 15:56

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