Phishing uses deception to gain information or access. It can be more than a misleading email: an attacker may combine messages, calls, and a convincing impersonation of trusted staff to capture credentials. Layered controls matter because preventing every attempt is not a realistic goal. section 1.4
Forms include:
- Vishing by voice and smishing by SMS. section 1.4
- Spear phishing aimed at specific people and whaling aimed at executives. section 1.4
- Business email compromise made more credible with tactics such as voice cloning. section 1.4
Track attempts over time to judge whether exposure is rising. In one 12-month exercise, recorded attempts climb from 23 in January to 90 in December. That trend supports prioritizing email filtering and employee awareness training; the count alone does not establish how many attacks succeeded. section 2.3.1
Listing 2.1 Using trend analysis to create a simple line plot
import pandas as pd #1
import matplotlib.pyplot as plt #2
# Example dataset: Phishing attempts recorded over 12 months
data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], #3
'Phishing_Attempts': [23, 25, 30, 35, 40, 45, 50, 60, 7
5, 80, 85, 90]} #3
df = pd.DataFrame(data) #4
# Plotting the trend
plt.figure(figsize=(10, 6)) #5
plt.plot(df['Month'], df['Phishing_Attempts'], marker='o',
linestyle='-', color='blue') #6
plt.title('Monthly Trend of Phishing Attempts') #7
plt.xlabel('Month') #8
plt.ylabel('Number of Phishing Attempts') #9
plt.grid(True) #10
plt.show() #11
The Python listing loads monthly attempt counts into a pandas DataFrame and plots them with Matplotlib, turning a sequence of counts into a visible trend. section 2.3.1
A phishing email as an entry point
At a financial institution, a phishing email compromised an employee’s credentials. Investigators correlated login records, network traffic, and server logs to trace access and data exfiltration; more than 10,000 customers’ personal information was affected. The response added multifactor authentication for remote access, stronger email filtering, awareness training, and improved monitoring. section 9.5.1
