Automated Malware Classification using LSTM and Naive Bayes
Malware classification is a critical task in cybersecurity, aiming to identify and categorize malicious software into different types. Traditional approaches rely on manual analysis, which is time-consuming, labor-intensive, and often ineffective. To address this challenge, we can leverage machine learning (ML) techniques, specifically integrating Long Short-Term Memory (LSTM) and Naive Bayes algorithms. In this post, we will explore how to automate malware classification using these powerful tools.
Dataset and Preprocessing
To build a robust classifier, we need a comprehensive dataset containing features extracted from malware samples. Popular features include:
- Static features (e.g., API calls, system c…
Automated Malware Classification using LSTM and Naive Bayes
Malware classification is a critical task in cybersecurity, aiming to identify and categorize malicious software into different types. Traditional approaches rely on manual analysis, which is time-consuming, labor-intensive, and often ineffective. To address this challenge, we can leverage machine learning (ML) techniques, specifically integrating Long Short-Term Memory (LSTM) and Naive Bayes algorithms. In this post, we will explore how to automate malware classification using these powerful tools.
Dataset and Preprocessing
To build a robust classifier, we need a comprehensive dataset containing features extracted from malware samples. Popular features include:
- Static features (e.g., API calls, system calls, registry keys)
- Dynamic features (e.g., execution flow, API call sequences)
- Behavioral features (e.g., network activity, file access)
We’ll use the scikit-learn library to preprocess our dataset....
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