logoChat01.ai

Class Imbalance Detection and Balancing

Creato il: 24 gennaio 2025

Risposto usando O1 da Chat01

Domanda

BE MASTER OF PANDAS AND MACHINE LEARNING.
Analyze First_part_code, First_part_code_output, Second_part_code, Second_part_code_output that i used for my dataset. ANALYZE EVERYTHING. Write me code for Identify class imbalance issues. Apply a balancing technique (e.g., SMOTE, oversampling). Compare class distributions before and after balancing. DEEPLY ANALYZE EVERYTHING AND DO Identify class imbalance issues. Apply a balancing technique (e.g., SMOTE, oversampling).

First_part_code : {
import pandas as pd

Replace 'your_dataset.csv' with the path to your CSV file

csv_file_path = 'your_dataset.csv'

1. Load CSV

df = pd.read_csv(csv_file_path, low_memory=False) # low_memory=False helps with large files

2. Basic dataset information

print("Dataset Dimensions (rows, columns):", df.shape)
print("\nColumn Names:\n", df.columns.tolist())

3. Data Types of Each Column

print("\nColumn Data Types:\n", df.dtypes)

4. Basic Info (including non-null counts)

print("\nDataFrame Info:")
df.info()

5. Statistics (numerical and non-numerical)

'include="all"' shows stats for both numeric and object type columns

print("\nDescriptive Statistics (including object columns):")
print(df.describe(include='all'))

6. Missing Values Check

missing_values = df.isnull().sum()
print("\nNumber of Missing Values per Column:\n", missing_values)

7. (Optional) Quick Look at First Few Rows

print("\nFirst 5 Rows of the Dataset:")
print(df.head())

8. (Optional) Quick Look at Last Few Rows

print("\nLast 5 Rows of the Dataset:")
print(df.tail())

9. (Optional) Count of Unique Values in Each Column

unique_counts = df.nunique()
print("\nNumber of Unique Values per Column:\n", unique_counts)
}

First_part_code_OUTPUT : {
Dataset Dimensions (rows, columns): (355630, 86)

Column Names:
['Unnamed: 0', 'Flow ID', ' Source IP', ' Source Port', ' Destination IP', ' Destination Port', ' Protocol', ' Timestamp', ' Flow Duration', ' Total Fwd Packets', ' Total Backward Packets', 'Total Length of Fwd Packets', ' Total Length of Bwd Packets', ' Fwd Packet Length Max', ' Fwd Packet Length Min', ' Fwd Packet Length Mean', ' Fwd Packet Length Std', 'Bwd Packet Length Max', ' Bwd Packet Length Min', ' Bwd Packet Length Mean', ' Bwd Packet Length Std', 'Flow Bytes/s', ' Flow Packets/s', ' Flow IAT Mean', ' Flow IAT Std', ' Flow IAT Max', ' Flow IAT Min', 'Fwd IAT Total', ' Fwd IAT Mean', ' Fwd IAT Std', ' Fwd IAT Max', ' Fwd IAT Min', 'Bwd IAT Total', ' Bwd IAT Mean', ' Bwd IAT Std', ' Bwd IAT Max', ' Bwd IAT Min', 'Fwd PSH Flags', ' Bwd PSH Flags', ' Fwd URG Flags', ' Bwd URG Flags', ' Fwd Header Length', ' Bwd Header Length', 'Fwd Packets/s', ' Bwd Packets/s', ' Min Packet Length', ' Max Packet Length', ' Packet Length Mean', ' Packet Length Std', ' Packet Length Variance', 'FIN Flag Count', ' SYN Flag Count', ' RST Flag Count', ' PSH Flag Count', ' ACK Flag Count', ' URG Flag Count', ' CWE Flag Count', ' ECE Flag Count', ' Down/Up Ratio', ' Average Packet Size', ' Avg Fwd Segment Size', ' Avg Bwd Segment Size', ' Fwd Header Length.1', 'Fwd Avg Bytes/Bulk', ' Fwd Avg Packets/Bulk', ' Fwd Avg Bulk Rate', ' Bwd Avg Bytes/Bulk', ' Bwd Avg Packets/Bulk', 'Bwd Avg Bulk Rate', 'Subflow Fwd Packets', ' Subflow Fwd Bytes', ' Subflow Bwd Packets', ' Subflow Bwd Bytes', 'Init_Win_bytes_forward', ' Init_Win_bytes_backward', ' act_data_pkt_fwd', ' min_seg_size_forward', 'Active Mean', ' Active Std', ' Active Max', ' Active Min', 'Idle Mean', ' Idle Std', ' Idle Max', ' Idle Min', 'Label']

Column Data Types:
Unnamed: 0 int64
Flow ID object
Source IP object
Source Port int64
Destination IP object
...
Idle Mean float64
Idle Std float64
Idle Max float64
Idle Min float64
Label object
Length: 86, dtype: object

DataFrame Info:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 355630 entries, 0 to 355629
Data columns (total 86 columns):

Column Non-Null Count Dtype

0 Unnamed: 0 355630 non-null int64
1 Flow ID 355629 non-null object
2 Source IP 355630 non-null object
3 Source Port 355630 non-null int64
4 Destination IP 355630 non-null object
5 Destination Port 355630 non-null float64
6 Protocol 355630 non-null float64
7 Timestamp 355630 non-null object
8 Flow Duration 355630 non-null int64
9 Total Fwd Packets 355630 non-null int64
10 Total Backward Packets 355630 non-null int64
11 Total Length of Fwd Packets 355630 non-null float64
12 Total Length of Bwd Packets 355630 non-null float64
13 Fwd Packet Length Max 355630 non-null float64
14 Fwd Packet Length Min 355630 non-null float64
15 Fwd Packet Length Mean 355630 non-null float64
16 Fwd Packet Length Std 355630 non-null float64
17 Bwd Packet Length Max 355630 non-null float64
18 Bwd Packet Length Min 355630 non-null float64
19 Bwd Packet Length Mean 355630 non-null float64
20 Bwd Packet Length Std 355630 non-null float64
21 Flow Bytes/s 355630 non-null float64
22 Flow Packets/s 355630 non-null float64
23 Flow IAT Mean 355630 non-null float64
24 Flow IAT Std 355630 non-null float64
25 Flow IAT Max 355630 non-null float64
26 Flow IAT Min 355630 non-null float64
27 Fwd IAT Total 355630 non-null float64
28 Fwd IAT Mean 355630 non-null float64
29 Fwd IAT Std 355630 non-null float64
30 Fwd IAT Max 355630 non-null float64
31 Fwd IAT Min 355630 non-null float64
32 Bwd IAT Total 355630 non-null float64
33 Bwd IAT Mean 355630 non-null float64
34 Bwd IAT Std 355630 non-null float64
35 Bwd IAT Max 355630 non-null float64
36 Bwd IAT Min 355630 non-null float64
37 Fwd PSH Flags 355630 non-null float64
38 Bwd PSH Flags 355630 non-null float64
39 Fwd URG Flags 355630 non-null int64
40 Bwd URG Flags 355630 non-null int64
41 Fwd Header Length 355630 non-null int64
42 Bwd Header Length 355630 non-null int64
43 Fwd Packets/s 355630 non-null float64
44 Bwd Packets/s 355629 non-null float64
45 Min Packet Length 355629 non-null float64
46 Max Packet Length 355629 non-null float64
47 Packet Length Mean 355629 non-null float64
48 Packet Length Std 355629 non-null float64
49 Packet Length Variance 355629 non-null float64
50 FIN Flag Count 355629 non-null float64
51 SYN Flag Count 355629 non-null float64
52 RST Flag Count 355629 non-null float64
53 PSH Flag Count 355629 non-null float64
54 ACK Flag Count 355629 non-null float64
55 URG Flag Count 355629 non-null float64
56 CWE Flag Count 355629 non-null object
57 ECE Flag Count 355628 non-null float64
58 Down/Up Ratio 355628 non-null object
59 Average Packet Size 355627 non-null float64
60 Avg Fwd Segment Size 355627 non-null float64
61 Avg Bwd Segment Size 355627 non-null float64
62 Fwd Header Length.1 355627 non-null float64
63 Fwd Avg Bytes/Bulk 355627 non-null object
64 Fwd Avg Packets/Bulk 355626 non-null float64
65 Fwd Avg Bulk Rate 355626 non-null float64
66 Bwd Avg Bytes/Bulk 355626 non-null float64
67 Bwd Avg Packets/Bulk 355626 non-null float64
68 Bwd Avg Bulk Rate 355626 non-null float64
69 Subflow Fwd Packets 355626 non-null float64
70 Subflow Fwd Bytes 355626 non-null float64
71 Subflow Bwd Packets 355626 non-null float64
72 Subflow Bwd Bytes 355626 non-null float64
73 Init_Win_bytes_forward 355626 non-null float64
74 Init_Win_bytes_backward 355626 non-null float64
75 act_data_pkt_fwd 355626 non-null float64
76 min_seg_size_forward 355626 non-null float64
77 Active Mean 355626 non-null float64
78 Active Std 355626 non-null float64
79 Active Max 355626 non-null float64
80 Active Min 355626 non-null float64
81 Idle Mean 355626 non-null float64
82 Idle Std 355626 non-null float64
83 Idle Max 355626 non-null float64
84 Idle Min 355626 non-null float64
85 Label 355630 non-null object
dtypes: float64(69), int64(9), object(8)
memory usage: 233.3+ MB

Descriptive Statistics (including object columns):
Unnamed: 0 Flow ID Source IP Source Port
count 355630.000000 355629 355630 3.556300e+05
unique NaN 176955 2536 NaN
top NaN 8.0.6.4-8.6.0.1-0-0-0 10.42.0.211 NaN
freq NaN 2097 178753 NaN
mean 1945.719231 NaN NaN 3.914129e+04
std 1840.477646 NaN NaN 1.794632e+04
min 0.000000 NaN NaN 0.000000e+00
25% 640.000000 NaN NaN 3.455900e+04
50% 1568.000000 NaN NaN 4.322600e+04
75% 2718.000000 NaN NaN 5.228300e+04
max 14157.000000 NaN NaN 1.045267e+06

    Destination IP   Destination Port       Protocol            Timestamp  \

count 355630 3.556300e+05 355630.000000 355630
unique 4746 NaN NaN 72218
top 10.42.0.1 NaN NaN 30/06/2017 01:14:36
freq 67213 NaN NaN 135
mean NaN 5.202541e+03 9.765190 NaN
std NaN 1.452135e+04 750.541237 NaN
min NaN 0.000000e+00 0.000000 NaN
25% NaN 8.000000e+01 6.000000 NaN
50% NaN 4.430000e+02 6.000000 NaN
75% NaN 4.430000e+02 6.000000 NaN
max NaN 1.328159e+06 442719.666667 NaN

     Flow Duration   Total Fwd Packets  ...   min_seg_size_forward  \

count 3.556300e+05 355630.000000 ... 3.556260e+05
unique NaN NaN ... NaN
top NaN NaN ... NaN
freq NaN NaN ... NaN
mean 1.092975e+07 7.433718 ... -2.831850e+04
std 2.180861e+07 81.015658 ... 5.543875e+06
min -1.000000e+00 0.000000 ... -1.395063e+09
25% 4.888600e+04 1.000000 ... 2.000000e+01
50% 5.602255e+05 2.000000 ... 3.200000e+01
75% 1.076907e+07 5.000000 ... 3.200000e+01
max 1.199999e+08 27165.000000 ... 1.705248e+08

     Active Mean    Active Std    Active Max    Active Min     Idle Mean  \

count 3.556260e+05 3.556260e+05 3.556260e+05 3.556260e+05 3.556260e+05
unique NaN NaN NaN NaN NaN
top NaN NaN NaN NaN NaN
freq NaN NaN NaN NaN NaN
mean 1.628143e+05 2.195225e+04 1.851564e+05 1.456534e+05 4.025724e+06
std 8.817765e+05 2.860244e+05 1.019477e+06 8.423659e+05 1.441518e+07
min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
25% 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
50% 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
75% 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
max 9.718488e+07 4.020815e+07 9.718488e+07 9.718488e+07 1.199514e+08

        Idle Std      Idle Max      Idle Min           Label

count 3.556260e+05 3.556260e+05 3.556260e+05 355630
unique NaN NaN NaN 4
top NaN NaN NaN Android_Adware
freq NaN NaN NaN 147443
mean 3.182527e+05 4.304876e+06 3.779037e+06 NaN
std 2.882901e+06 1.514008e+07 1.407042e+07 NaN
min 0.000000e+00 0.000000e+00 0.000000e+00 NaN
25% 0.000000e+00 0.000000e+00 0.000000e+00 NaN
50% 0.000000e+00 0.000000e+00 0.000000e+00 NaN
75% 0.000000e+00 0.000000e+00 0.000000e+00 NaN
max 7.737303e+07 1.199514e+08 1.199514e+08 NaN

[11 rows x 86 columns]

Number of Missing Values per Column:
Unnamed: 0 0
Flow ID 1
Source IP 0
Source Port 0
Destination IP 0
..
Idle Mean 4
Idle Std 4
Idle Max 4
Idle Min 4
Label 0
Length: 86, dtype: int64

First 5 Rows of the Dataset:
Unnamed: 0 Flow ID Source IP
0 0 172.217.6.202-10.42.0.211-443-50004-6 10.42.0.211
1 1 172.217.6.202-10.42.0.211-443-35455-6 10.42.0.211
2 2 131.253.61.68-10.42.0.211-443-51775-6 10.42.0.211
3 3 131.253.61.68-10.42.0.211-443-51775-6 10.42.0.211
4 4 131.253.61.68-10.42.0.211-443-51776-6 10.42.0.211

Source Port  Destination IP   Destination Port   Protocol  \

0 50004 172.217.6.202 443.0 6.0
1 35455 172.217.6.202 443.0 6.0
2 51775 131.253.61.68 443.0 6.0
3 51775 131.253.61.68 443.0 6.0
4 51776 131.253.61.68 443.0 6.0

         Timestamp   Flow Duration   Total Fwd Packets  ...  \

0 13/06/2017 11:52:39 37027 1 ...
1 13/06/2017 11:52:39 36653 1 ...
2 13/06/2017 11:52:42 534099 8 ...
3 13/06/2017 11:52:43 9309 3 ...
4 13/06/2017 11:52:42 19890496 8 ...

min_seg_size_forward  Active Mean   Active Std   Active Max   Active Min  \

0 32.0 0.0 0.0 0.0 0.0
1 32.0 0.0 0.0 0.0 0.0
2 20.0 0.0 0.0 0.0 0.0
3 20.0 0.0 0.0 0.0 0.0
4 20.0 0.0 0.0 0.0 0.0

Idle Mean Idle Std Idle Max Idle Min Label
0 0.0 0.0 0.0 0.0 Android_Adware
1 0.0 0.0 0.0 0.0 Android_Adware
2 0.0 0.0 0.0 0.0 Android_Adware
3 0.0 0.0 0.0 0.0 Android_Adware
4 0.0 0.0 0.0 0.0 Android_Adware

[5 rows x 86 columns]

Last 5 Rows of the Dataset:
Unnamed: 0 Flow ID Source IP
355625 405 172.217.7.14-10.42.0.211-80-38405-6 172.217.7.14
355626 406 10.42.0.211-10.42.0.1-7632-53-17 10.42.0.211
355627 407 10.42.0.211-104.192.110.245-45970-443-6 10.42.0.211
355628 408 10.42.0.211-10.42.0.1-51982-53-17 10.42.0.211
355629 409 10.42.0.211-10.42.0.1-9320-53-17 10.42.0.211

     Source Port   Destination IP   Destination Port   Protocol  \

355625 80 10.42.0.211 38405.0 6.0
355626 7632 10.42.0.1 53.0 17.0
355627 45970 104.192.110.245 443.0 6.0
355628 51982 10.42.0.1 53.0 17.0
355629 9320 10.42.0.1 53.0 17.0

              Timestamp   Flow Duration   Total Fwd Packets  ...  \

355625 17/06/2017 01:29:11 126711 1 ...
355626 17/06/2017 01:30:33 48012 1 ...
355627 17/06/2017 01:29:45 20028018 11 ...
355628 17/06/2017 01:29:45 347926 1 ...
355629 17/06/2017 01:30:33 125473 1 ...

     min_seg_size_forward  Active Mean   Active Std   Active Max  \

355625 32.0 0.0 0.0 0.0
355626 32.0 0.0 0.0 0.0
355627 20.0 367528.0 0.0 367528.0
355628 20.0 0.0 0.0 0.0
355629 32.0 0.0 0.0 0.0

     Active Min   Idle Mean   Idle Std    Idle Max    Idle Min   Label

355625 0.0 0.0 0.0 0.0 0.0 Benign
355626 0.0 0.0 0.0 0.0 0.0 Benign
355627 367528.0 19660490.0 0.0 19660490.0 19660490.0 Benign
355628 0.0 0.0 0.0 0.0 0.0 Benign
355629 0.0 0.0 0.0 0.0 0.0 Benign

[5 rows x 86 columns]

Number of Unique Values per Column:
Unnamed: 0 14158
Flow ID 176955
Source IP 2536
Source Port 51343
Destination IP 4746
...
Idle Mean 39823
Idle Std 8887
Idle Max 39789
Idle Min 39784
Label 4
Length: 86, dtype: int64
}

Second_part_code : {import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler, StandardScaler

----------------------------------------------------------------------------------

1. LOAD DATA

df = pd.read_csv('Android_Malware.csv', low_memory=False)

----------------------------------------------------------------------------------

2. BASIC CLEANING

(A) Drop or rename the 'Unnamed: 0' column if it is just an extra index.

if 'Unnamed: 0' in df.columns:
df.drop(columns=['Unnamed: 0'], inplace=True)

(B) Strip any leading/trailing spaces in column names (some columns have spaces).

df.columns = [col.strip() for col in df.columns]

(C) Check for duplicates and remove them, if it makes sense for your domain.

df.drop_duplicates(inplace=True)

(D) Convert columns that should be numeric but are read as object.

Identify which columns are supposed to be numeric. Below is an example list

based on your DataFrame info. Adjust as needed.

columns_to_convert = [
'CWE Flag Count',
'Down/Up Ratio',
'Fwd Avg Bytes/Bulk',
'Fwd Avg Packets/Bulk',
'Fwd Avg Bulk Rate',
'Bwd Avg Bytes/Bulk',
'Bwd Avg Packets/Bulk',
'Bwd Avg Bulk Rate'
]

for col in columns_to_convert:
if col in df.columns:
df[col] = pd.to_numeric(df[col], errors='coerce')
# errors='coerce' turns invalid parsing into NaN

(E) Re-check for missing values after conversions

missing_values = df.isnull().sum()
print("Missing values after type conversions:\n", missing_values)

(F) Handle missing values.

Given that the dataset is quite large and only a few rows have NaNs,

a common approach is to drop rows with any missing values. Alternatively,

you can fill them with means/medians if that makes sense for your domain.

df.dropna(inplace=True)

(G) Remove or fix invalid numeric values (e.g., negative flow durations).

For instance, if Flow Duration < 0 is invalid, drop those rows:

if 'Flow Duration' in df.columns:
df = df[df['Flow Duration'] >= 0]

----------------------------------------------------------------------------------

3. FEATURE SELECTION (OPTIONAL)

----------------------------------------------------------------------------------

Often we drop columns that are IDs, IP addresses, or Timestamps that aren’t

meant to be used as numeric features (they won’t help classification/regression

models in typical flow-based analyses). Keep them if you want time-based splits.

columns_to_drop = ['Flow ID', 'Source IP', 'Destination IP', 'Timestamp']
for col in columns_to_drop:
if col in df.columns:
df.drop(columns=[col], inplace=True)

----------------------------------------------------------------------------------

4. NORMALIZATION / SCALING

----------------------------------------------------------------------------------

Typically you separate out your target variable (e.g., 'Label').

Then apply scaling to numeric features only.

if 'Label' in df.columns:
labels = df['Label']
df.drop(columns=['Label'], inplace=True)
else:
labels = None

Example: Using MinMaxScaler OR StandardScaler on numeric features

numeric_cols = df.select_dtypes(include=[np.number]).columns

You can choose either scaler depending on your approach:

scaler = MinMaxScaler() # Scales each feature to [0, 1]

scaler = StandardScaler() # Transforms each feature to have 0 mean, unit variance

df[numeric_cols] = scaler.fit_transform(df[numeric_cols])

(Re)Attach the label column at the end

df['Label'] = labels

----------------------------------------------------------------------------------

5. CLASS IMBALANCE VISUALIZATION

----------------------------------------------------------------------------------

Plot a bar chart of the counts for each label.

plt.figure(figsize=(6,4))
sns.countplot(data=df, x='Label', order=df['Label'].value_counts().index)
plt.title("Class Distribution (Label)")
plt.xlabel("Label")
plt.ylabel("Count")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Alternatively, just using pandas:

df['Label'].value_counts().plot(kind='bar')

plt.show()

Now you have a cleaned, scaled dataframe, and a visualization of class imbalance.

----------------------------------------------------------------------------------

Print final info

print("\nDataFrame shape after preprocessing:", df.shape)
print("\nData Sample:")
print(df.head())}

Second_part_code_output : {
Missing values after type conversions:
Flow ID 1
Source IP 0
Source Port 0
Destination IP 0
Destination Port 0
..
Idle Mean 4
Idle Std 4
Idle Max 4
Idle Min 4
Label 0
Length: 85, dtype: int64

DataFrame shape after preprocessing: (351512, 81)

Data Sample:
Source Port Destination Port Protocol Flow Duration Total Fwd Packets
0 0.609253 -0.330827 -0.513036 -0.498843 -0.094286
1 -0.204837 -0.330827 -0.513036 -0.498860 -0.094286
2 0.708350 -0.330827 -0.513036 -0.476016 0.009675
3 0.708350 -0.330827 -0.513036 -0.500116 -0.064583
4 0.708406 -0.330827 -0.513036 0.412872 0.009675

Total Backward Packets Total Length of Fwd Packets
0 -0.049280 -0.034712
1 -0.049280 -0.034712
2 0.007811 0.017058
3 -0.054471 -0.034712
4 -0.023330 -0.012693

Total Length of Bwd Packets Fwd Packet Length Max Fwd Packet Length Min
0 -0.040684 -0.577216 -0.215206
1 -0.040684 -0.577216 -0.215206
2 0.002234 1.009148 -0.215206
3 -0.040684 -0.577216 -0.215206
4 -0.020243 0.018012 -0.215206

... min_seg_size_forward Active Mean Active Std Active Max Active Min
0 ... 0.004657 -0.184645 -0.076601 -0.181619 -0.172968
1 ... 0.004657 -0.184645 -0.076601 -0.181619 -0.172968
2 ... 0.004654 -0.184645 -0.076601 -0.181619 -0.172968
3 ... 0.004654 -0.184645 -0.076601 -0.181619 -0.172968
4 ... 0.004654 -0.184645 -0.076601 -0.181619 -0.172968

Idle Mean Idle Std Idle Max Idle Min Label
0 -0.279116 -0.11001 -0.28415 -0.268497 Android_Adware
1 -0.279116 -0.11001 -0.28415 -0.268497 Android_Adware
2 -0.279116 -0.11001 -0.28415 -0.268497 Android_Adware
3 -0.279116 -0.11001 -0.28415 -0.268497 Android_Adware
4 -0.279116 -0.11001 -0.28415 -0.268497 Android_Adware

[5 rows x 81 columns]}

Pensiero

Balancing the dataset

I'm mapping out a plan to read the dataset, identify class imbalances, and apply SMOTE or oversampling. Visualizations will show class distribution changes.

Balancing the data

I’m planning to read the dataframe, exclude the label column, and apply SMOTE for balancing. Then, I’ll visualize class distributions before and after the balancing process.

Visualizing class distribution

I’m showcasing the class distribution pre and post-SMOTE with y.value_counts() and a bar chart. Considering categorical feature encoding using one-hot or label encoding, even though they’re already numeric.

Thought about class imbalance analysis for 7 seconds

Risposta

Below is a step-by-step guide and code example showing how you can:

  1. Identify class imbalance (by examining label distribution).
  2. Apply a balancing technique (SMOTE in this example).
  3. Compare class distributions before and after balancing.

1. Deep Analysis of the Existing Codes and Outputs

First_part_code & Output

  • You loaded the dataset, examined dimensions, column names, data types, missing values, descriptive statistics, and took a peek at the first/last rows.
  • Key Observations:
    • The dataset has 355630 rows and 86 columns initially.
    • There are 4 unique labels in the Label column (e.g., Android_Adware, Benign, etc.).
    • A few columns have missing values (like Flow ID, some packet length stats, etc.).
    • A small fraction of rows are missing these values compared to the total 355,630 rows.

Second_part_code & Output

  • You preprocessed and scaled the data:
    1. Dropped Unnamed: 0 and other ID-like columns (Flow ID, Source IP, Destination IP, Timestamp).
    2. Converted certain columns to numeric (CWE Flag Count, Down/Up Ratio, etc.).
    3. Dropped rows containing any remaining NaNs.
    4. Removed invalid rows (e.g., negative Flow Duration).
    5. Scaled all numeric columns with StandardScaler.
  • After these steps, the final DataFrame shape is (351512, 81). The Label column remains.
  • A quick countplot for Label shows the class distribution. We can see if there is an imbalance by comparing the number of samples from each label category.

From these steps, you have a clean, numeric dataset in a variable df. The last column is Label. The second code snippet’s final printout shows the shape (351512, 81) and a sample with standardized numerical values plus the label.

2. Identifying Class Imbalance

Even if you have a bar chart from sns.countplot or you used df['Label'].value_counts(), let’s make it explicit in code. Suppose after all your preprocessing (i.e., after you run the second code), you have:

python
print("Class distribution before balancing:")
print(df['Label'].value_counts())

This gives you something like:

text
Android_Adware    <some_number>
Benign            <some_number>
...
Name_of_other_classes ...

If you see that one or more classes are far more frequent than the others, you have an imbalance.

3. Applying a Balancing Technique (SMOTE Example)

Important Considerations for SMOTE:

  • SMOTE works by synthesizing new samples in the feature space for the minority class(es).
  • It requires that your features (X) be numeric. After your second code, you have already scaled numeric columns, so SMOTE can be applied directly.
  • You must separate your data into features (X) and labels (y) before applying SMOTE.

Below is a standalone code snippet you can run after you finish the preprocessing in Second_part_code. If you already have your final df with numeric columns + Label, you can just proceed:

python
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter

# If you haven't already done this in the second_part_code:
# df = ...  # your preprocessed DataFrame 
# Ensure 'Label' is the actual class column.

# ---------------------------------------------------
# 1) Separate features (X) and label (y)
# ---------------------------------------------------
X = df.drop('Label', axis=1)
y = df['Label']

print("Class distribution BEFORE balancing:")
print(Counter(y))

# Optionally, visualize
plt.figure(figsize=(6,4))
sns.countplot(x=y, order=y.value_counts().index)
plt.title("Class Distribution Before SMOTE")
plt.xlabel("Label")
plt.ylabel("Count")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# ---------------------------------------------------
# 2) Apply SMOTE
# ---------------------------------------------------
from imblearn.over_sampling import SMOTE

smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# ---------------------------------------------------
# 3) Compare distributions AFTER balancing
# ---------------------------------------------------
print("\nClass distribution AFTER SMOTE:")
print(Counter(y_res))

# Bar plot after SMOTE
plt.figure(figsize=(6,4))
sns.countplot(x=y_res, order=y_res.value_counts().index)
plt.title("Class Distribution After SMOTE")
plt.xlabel("Label")
plt.ylabel("Count")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Explanation of the SMOTE Steps:

  1. X = df.drop('Label', axis=1)
    • We remove the Label column from df to get numeric features.
  2. y = df['Label']
    • Assign the Label column to y.
  3. smote.fit_resample(X, y)
    • SMOTE will generate new (synthetic) minority-class examples.
    • X_res, y_res are the new balanced feature set and labels.

4. Observing the Results

  • Before SMOTE, a Counter(y) or y.value_counts() likely shows a strong imbalance (e.g., maybe Android_Adware has 150,000+ samples, while some other class might have far fewer).
  • After SMOTE, all classes are matched to the size of the largest existing class. So now, training your models on X_res, y_res should mitigate the negative effects of imbalance.

5. Further Tips

  1. Train/Test Split:
    Typically, you apply SMOTE only to your training set, not to the test set. You want to keep the real-world distribution in your test set. The usual approach:

    python
    from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                    stratify=y, 
                                                    test_size=0.3, 
                                                    random_state=42)
smote = SMOTE(random_state=42)
X_train_res, y_train_res = smote.fit_resample(X_train, y_train)
# Keep X_test, y_test as is (unchanged).

  2. Alternative Balancing Methods:

    • Random Over-Sampling (simply replicates minority samples)
    • Random Under-Sampling (removes samples from the majority class)
    • SMOTE variants: ADASYN, Borderline-SMOTE, etc.

  3. Check Model Performance:
    After re-balancing your training set, compare how your ML model performs (precision, recall, F1-score, ROC-AUC, etc.) on the original distribution in the test set.

  4. Pipeline:
    If you use scikit-learn Pipelines, you can combine scaling + SMOTE + model training in a single pipeline for reproducibility.

6. Conclusion

  • You have now identified class imbalance via label distribution counts/plots.
  • SMOTE (or other oversampling methods) can address this imbalance by synthetically expanding the minority classes.
  • Always validate performance on an untouched test set that reflects the real-world distribution.

Use the provided code snippet in step 3 to see the before/after class counts and to integrate SMOTE into your pipeline. That’s it! You’ve addressed class imbalance following your existing cleaning and scaling steps.

Condividi questa Q&A