Empowering Cyber Defense: Advanced Machine Learning for Detecting Malicious URLs
Keywords:
Modified Random Forest Algorithm, Decision Tree Analysis, URL Trustworthiness Evaluation, Malware Source Identification, Advanced Feature Selection Techniques, Cybersecurity Threat Analysis, Machine Learning in Malware Detection, Efficient Malware ScreeningAbstract
This paper explores a novel strategy for malware detection by imposing the abilities of machine learning algorithms, in specific
Random Forest and Decision Trees. Our research revolves around the development of a model that assesses URLs to determine their
trustworthiness and identifies malware originating from various online sources. By integrating custom modifications into the Random
Forest and Decision Tree algorithms, our model achieves enhanced sensitivity to the nuanced indicators of malicious content. This approach
not only facilitates the detection of malware through URL analysis but also extends to the scrutiny of files downloaded from the internet,
providing a comprehensive solution for cybersecurity threats. A key innovation in our method is the application of advanced feature
selection techniques, which significantly improve the model's accuracy and efficiency in identifying potential threats. Our findings indicate
a substantial improvement in malware detection rates, setting a new precedent in the field of cybersecurity. This study contributes to the
ongoing efforts in digital security, offering a robust tool for the early detection and mitigation of malware risks.
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References
Liu, Kaijun, et al. "A review of android malware detection approachesbased on machine learning." IEEE Access 8 (2020): 124579-124607.
Senanayake, Janaka, Harsha Kalutarage, and Mhd Omar Al-Kadri. "Android mobile malware detection using machine learning: A systematic review." Electronics 10.13 (2021): 1606.
Kouliaridis, Vasileios, and Georgios Kambourakis. "A comprehensivesurvey on machine learning techniques for android malware detection." Information 12.5 (2021): 185
Alqahtani, Ebtesam J., Rachid Zagrouba, and Abdullah Almuhaideb. "A survey on android malware detection techniques using machine learning algorithms." Software Defined Systems (SDS). IEEE, 2019
Rathore, Hemant, et al. "Malware detection using machine learning and deep learning." Big Data Analytics: 6th International Conference, BDA 2018, Warangal, India, December 18–21, 2018, Proceedings 6. Springer International Publishing, 2018.
Joshi, Santosh, et al. "Machine learning approach for malware detection using random forest classifier on process list data structure." Proceedings of the 2nd International Conference on Information System and Data Mining. 2018.
Singh, Jagsir, and Jaswinder Singh. "A survey on machine learning-based malware detection in executable files." Journal of Systems Architecture 112 (2021): 101861.
Saad, Sherif, William Briguglio, and Haytham Elmiligi. "The curious case of machine learning in malware detection." arXiv preprint arXiv:1905.07573 (2019).
Fernando, Damien Warren, Nikos Komninos, and Thomas Chen. "A study on the evolution of ransomware detection using machine learning and deep learning techniques." IoT 1.2 (2020): 551-604.
Mills, Alan, Theodoros Spyridopoulos, and Phil Legg. "Efficient and interpretable real-time malware detection using random-forest." 2019 International conference on cyber situational awareness, data analytics and assessment (Cyber SA). IEEE, 2019.
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