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Malware Detection Using Machine Learning Methods on the APIMDS Dataset-1: Preparation of the…
- Traditional methods for malware detection are no longer sufficient in cybersecurity, leading to the adoption of machine learning-based approaches.
- The article focuses on using Machine Learning algorithms with the APIMDS dataset for malware detection.
- The dataset contains API call sequences of malware samples classified by Kaspersky AntiVirus.
- Each row in the dataset corresponds to a software sample, with API call sequences being the main components.
- The article discusses the challenges faced due to varying column lengths in the dataset when using Pandas for data processing.
- The manual operation involves organizing the data to represent API calls as columns with binary values for presence.
- The process includes cleaning the dataset, adjusting the malware_class column to differentiate between harmless and harmful software.
- After preparation, the dataset consists of 17268 rows and 1165 columns for machine learning analysis.
- Analysis reveals the most frequently used API calls in the dataset, showcasing critical calls for malware detection.
- The dataset is now clean and ready for model training using machine learning techniques for malware detection.
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