File size: 2,700 Bytes
d29bde4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import os
import joblib
import pandas as pd
import numpy as np
# Define paths
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
MODELS_DIR = os.path.join(BASE_DIR, "models")
SCALER_PATH = os.path.join(MODELS_DIR, "nids_scaler.pkl")
OUTPUT_PATH = os.path.join(BASE_DIR, "sample_nids.csv")
print("=== Generating Sample NIDS CSV ===")
# 1. Load the NIDS scaler
if not os.path.exists(SCALER_PATH):
raise FileNotFoundError(f"Scaler not found at {SCALER_PATH}. Please make sure files are in the models folder.")
try:
scaler = joblib.load(SCALER_PATH)
print("Successfully loaded nids_scaler.pkl")
except Exception as e:
raise RuntimeError(f"Error loading scaler: {str(e)}")
# 2. Get expected feature names
if not hasattr(scaler, "feature_names_in_"):
raise ValueError("The loaded scaler does not contain feature_names_in_. Cannot determine column names automatically.")
features = list(scaler.feature_names_in_)
print(f"Scaler expects {len(features)} features.")
# 3. Create realistic random values for NIDS dataset (based on KDD/NSL-KDD schema)
np.random.seed(42) # For reproducibility
num_rows = 10
data = {}
# Define typical categoricals and binary flags
categorical_choices = {
"protocol_type": ["tcp", "udp", "icmp"],
"service": ["http", "private", "smtp", "ftp_data", "domain_u", "other"],
"flag": ["SF", "S0", "REJ", "RSTR", "RSTO", "SH"]
}
binary_columns = ["land", "logged_in", "root_shell", "is_host_login", "is_guest_login"]
for col in features:
if col in categorical_choices:
data[col] = np.random.choice(categorical_choices[col], size=num_rows)
elif col in binary_columns:
data[col] = np.random.choice([0, 1], size=num_rows, p=[0.9, 0.1])
elif "rate" in col:
# Rates are values between 0.0 and 1.0
data[col] = np.round(np.random.uniform(0.0, 1.0, size=num_rows), 4)
elif "count" in col:
# Counts are integers, e.g., 0 to 511
data[col] = np.random.randint(0, 512, size=num_rows)
elif col in ["src_bytes", "dst_bytes"]:
# Bytes can be larger integers
data[col] = np.random.randint(0, 100000, size=num_rows)
elif col == "duration":
# Connection duration
data[col] = np.random.randint(0, 1000, size=num_rows)
else:
# Default fallback for other numeric columns
data[col] = np.round(np.random.uniform(0.0, 10.0, size=num_rows), 4)
# 4. Generate DataFrame and save to CSV
df = pd.DataFrame(data)
df.to_csv(OUTPUT_PATH, index=False)
print(f"\nSuccessfully generated sample CSV file: {OUTPUT_PATH}")
print(f"File contains {num_rows} rows and {len(df.columns)} columns.")
print("\nGenerated Column Names:")
print(list(df.columns))
|