Ids.xls Official

A key feature of these files is their ability to be converted into machine-readable formats. For instance, the Excel2IDS tool on GitHub allows users to generate multiple IDS files from a single Excel template by mapping "purposes" or "disciplines" defined within the spreadsheet. Key Features of IDS Excel Workflows

How to Use This Paper

1. Replace placeholder findings (percentages, attack types, flags like RSTO) with your actual analysis of ids.xls.
2. If ids.xls is not an IDS log: Change the title and introduction to match the file’s true purpose (e.g., “Analysis of Student ID Data” if ids stands for “identifications”).
3. Add visualizations: Mention Figure 1 (class distribution bar chart) and Figure 2 (correlation heatmap) based on the Excel data.

• Rule Generation: Simple threshold rules (e.g., ICMP packets > 800 bytes) capture 89% of attacks with 2% false positives.
• Feature Redundancy: The land (source = destination) feature is zero for all records; it can be dropped to reduce dimensionality.
• Temporal Patterns: Attack intensity peaks between 2:00 AM and 4:00 AM (server time), suggesting automated scripts targeting off-peak hours.

3. Basic Steps to Work With "ids.xls"

A. Inspect the structure

• Open file → look for column headers like ID, Name, Description, Type.
• Check for multiple sheets (tabs at bottom).

Read the risky XLS file

df = pd.read_excel('ids.xls', sheet_name='Sheet1') ids.xls