Runze Wang
ABSTRACT
With the Internet's rapid evolution, the incidence of cyberattacks has surged significantly. Employing machine learning to precisely detect and thwart malicious network traffic has emerged as a novel and effective solution for safeguarding computer networks. This research program centers on the identification of suitable machine learning models and the meticulous curation of data features. Within this study, a total of 13 features, encompassing conventional timestamps, the volume of traffic packets in data streams, and their associated sizes, are extracted as key features following the dataset's traffic packet consolidation process. Three algorithms such as Random Forest, Decision Tree and Support Vector Machine were chosen for training and testing the dataset. In addition, Principle Component Analysis dimensionality reduction is performed for these 13 features to determine the effect on the accuracy of the results before and after the dimensionality reduction process. The final result is that the Random Forest algorithm achieves best processing power, but produces large fluctuations in the accuracy in one dimension. In the face of large-scale network traffic analysis, the random forest model should be preferred as the machine learning model, while ensuring that the dimension is greater than one dimension after dimensionality reduction.
- Al-Alawi, Adel Ismail, Sara Abdulrahman Al-Bassam, and Arpita A. Mehrotra. 2020. Critical cybersecurity threats: frontline issues faced by Bahraini organizations. Implementing Computational Intelligence Techniques for Security Systems Design. IGI Global. 210-229.Google Scholar
- Cisco, U. 2020. Cisco annual internet report (2018–2023) white paper. Cisco: San Jose, CA, USA 10.1, 1-35.Google Scholar
- Hao Li, 2019. Unknown Malware detection based on network traffic analysis. Journal of Jinan University (Natural Science Edition) 33.06, 500-505. doi: 10.13349/j.cnki.jdxbn.2019.06.004.Google ScholarCross Ref
- Rathore, Hemant, 2018. Malware detection using machine learning and deep learning. Big Data Analytics: 6th International Conference, BDA 2018, Warangal, India, December 18–21, Proceedings 6. Springer International Publishing, 2018.Google Scholar
- El Merabet, Hoda, and Abderrahmane Hajraoui. 2019. A survey of malware detection techniques based on machine learning. International Journal of Advanced Computer Science and Applications 10.1.Google Scholar
- Jordan Holland, Paul Schmitt, Nick Feamster, and Prateek Mittal. 2021. New Directions in Automated Traffic Analysis. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (CCS '21). Association for Computing Machinery, New York, NY, USA, 3366–3383. https://doi.org/10.1145/3460120.3484758.Google ScholarDigital Library
- Kurita, Takio. 2019. Principal component analysis (PCA). Computer Vision: A Reference Guide.1-4.Google Scholar
- Hongyan Lv, and Qian Feng. 2019. A Review of Research on Random Forest Algorithms. Journal of Hebei Academy of Sciences 36.3. 37-41.Google Scholar
- Yanli Liu, Yourong Wang, and Jian Zhang. 2012. New machine learning algorithm: Random forest. Information Computing and Applications: Third International Conference, ICICA 2012, Chengde, China, September 14-16, 2012. Proceedings 3. Springer Berlin Heidelberg.Google ScholarDigital Library
- Charbuty, Bahzad, and Adnan Abdulazeez. 2021. Classification based on decision tree algorithm for machine learning." Journal of Applied Science and Technology Trends 2.01, 20-28.Google Scholar
- Hasan, Basna Mohammed Salih, and Adnan Mohsin Abdulazeez. 2021. A review of principal component analysis algorithm for dimensionality reduction." Journal of Soft Computing and Data Mining 2.1, 20-30.Google Scholar
Index Terms
- Investigation the Impact of Features on Malicious Traffic Identification Based on Different Machine Learning Algorithms Combined with Dimensionality Reduction
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