A heat map uses color intensity to make values in a table easier to compare. In security analytics, it can reveal relationships in login data or show where a classifier makes mistakes. The color draws attention; the underlying values still determine the interpretation. section 11.4 section 12.4.1
Read the cells according to what the map measures:
- A correlation heatmap displays values from −1 to 1. Compare login hour with failed login attempts to investigate whether failures tend to increase at particular times; a strong relationship can prompt a review of authentication controls. section 11.4
- A grayscale confusion-matrix heatmap displays classification counts. Darker cells contain higher counts: diagonal cells are correct predictions, while off-diagonal cells reveal errors needing attention. section 12.4.1
- On a security analyst dashboard, a heatmap can show real-time alert counts by severity, helping analysts see where attention may be needed. section 2.2.3
Check a risk classifier
For articles classified as high or low risk, compare the true labels in the confusion matrix’s rows with predicted labels in its columns. An off-diagonal dark cell makes a recurring type of misclassification visible, rather than hiding it within an overall performance figure. section 12.4.1
Listing 11.1 Hidden relationships
#Import libraries import seaborn as sns #1 import pandas as pd #2 import matplotlib.pyplot as plt #3 #Example dataset: Login hours and failed attempts data = {'Login_Hour': [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3], #4 'Failed_Login_Attempts': [2, 3, 4, 5, 3, 6, 7, 5, 8, 9, 12, 13, 10, 8, 7, 5, 2, 3, 4, 5]} #5 df = pd.DataFrame(data) #6 #Calculate correlation matrix correlation_matrix = df.corr() #7 #Plot heatmap plt.figure(figsize=(8, 6)) #8 sns.heatmap(correlation_matrix, annot=True, cmap='Grays', cbar=True) #9 plt.title('Correlation Between Login Hours and Failed \ Login Attempts') #10 plt.show() #11
The Python listing builds a correlation matrix from synthetic login-hour and failed-attempt data, then plots it with annotated cells using seaborn. section 11.4
Use the pattern to decide what to investigate or report, not as a decision by itself. In a stakeholder report, pair a heat map with a concise explanation of the trend or area needing attention. section 11.4 section 3.2.4
