Malware Detection and Prevention Analysis: Leveraging NLP, this project examines programming languages associated with malware. Analyze trends, frequency distribution, and gain insights into languages used for detection and prevention. Valuable for security analysts, researchers, and developers.
Key Features:
Data Cleaning: The project includes a comprehensive data cleaning process that involves removing code snippets, HTML tags, stop words, punctuation marks, and non-alphabetic characters from the text data. This step ensures a clean and standardized dataset for analysis. Tokenization and Preprocessing: NLP techniques are employed to tokenize the text data and perform preprocessing tasks such as converting the text to lowercase, removing punctuation and digits, and eliminating stop words. These steps prepare the text for further analysis. Concatenation and Filtering: The "Title" and "Body" columns of the dataset are concatenated to create a unified text corpus. The dataset is then filtered to focus on questions related to malware creation or prevention, using keywords associated with these topics. Frequency Distribution Analysis: The project calculates the frequency distribution of programming languages mentioned in the context of malware prevention and creation. By extracting the year and month information from the dataset, the analysis provides insights into the popularity and prevalence of different programming languages over time. Visualizations: The project incorporates visualizations such as bar charts to present the findings of the frequency distribution analysis in an intuitive and informative manner. This project serves as a valuable resource for understanding the programming languages commonly associated with malware-related discussions. It can be used by security analysts, researchers, and developers to gain insights into the trends and patterns in programming languages used in the context of malware detection and prevention.