CSAI2026 labs: Lab-2..Lab-6 (Practical + Solution, data), Module-2..Module-6 notebooks and datasets

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+# Dataset setup — UCI Malware Static and Dynamic Features
+
+All six notebooks use pre-extracted tabular features from the official UCI dataset **Malware Static and Dynamic Features VxHeaven and Virus Total**.
+
+## Download
+
+1. Open the [UCI dataset page](https://archive.ics.uci.edu/dataset/541/malware%2Bstatic%2Band%2Bdynamic%2Bfeatures%2Bvxheaven%2Band%2Bvirus%2Btotal).
+2. Select **Download** and extract the archive.
+3. Create `data/raw/` if it does not already exist.
+4. Copy these three files into `data/raw/`:
+
+```text
+staDynBenignLab.csv
+staDynVxHeaven2698Lab.csv
+staDynVt2955Lab.csv
+```
+
+The expected layout is:
+
+```text
+Practical-5/
+└── data/
+    ├── prepare_dataset.py
+    └── raw/
+        ├── staDynBenignLab.csv
+        ├── staDynVxHeaven2698Lab.csv
+        └── staDynVt2955Lab.csv
+```
+
+## Prepare the classroom table
+
+From the `Practical-5` directory, run:
+
+```bash
+python data/prepare_dataset.py
+```
+
+The utility:
+
+- finds the three required files under `data/raw/`;
+- normalizes column names and adds deterministic occurrence suffixes when
+  case- or punctuation-distinct UCI fields collapse to the same snake-case name;
+- keeps columns shared by all three sources;
+- removes obvious identifiers and pre-existing target columns;
+- converts usable columns to numeric values;
+- adds `source` and `label` (`0 = benign`, `1 = malware`);
+- retains missing values so imputation can be fitted on training data inside the notebooks;
+- writes `data/malware_features.csv` and `data/feature_manifest.csv`.
+
+## Quick verification
+
+```python
+from pathlib import Path
+import pandas as pd
+
+path = Path("data/malware_features.csv")
+df = pd.read_csv(path)
+print(df.shape)
+print(df["label"].value_counts().sort_index())
+print(df["source"].value_counts())
+```
+
+The original release contains approximately 6,248 rows: 595 benign records and 5,653 malware records. The exact number of retained feature columns can vary if the source archive changes column formatting.
+
+## Provenance and license
+
+- Dataset: Malware Static and Dynamic Features VxHeaven and Virus Total
+- Source: UCI Machine Learning Repository
+- DOI: [10.24432/C58K6H](https://doi.org/10.24432/C58K6H)
+- License: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+
+Retain the citation and license when redistributing derived classroom tables.
+
+## Important limitations
+
+- The class distribution is strongly imbalanced.
+- Benign and malware samples originate from different collections, so source-specific artifacts may inflate random-split performance.
+- The features represent a historical dataset and should not be treated as a current production benchmark.
+- No raw malware is required or permitted for this practical.