Natural language processing (NLP) uses computers to analyze large collections of human-language text and extract information relevant to cybersecurity. It helps teams sift threat intelligence from news articles, blogs, research papers, social media, logs, emails, and threat reports faster than manual review. section 12.4 section 10.3.4
A practical cybersecurity NLP workflow can:
- Scan thousands of articles and reports for keywords such as “malware,” “data breach,” and “vulnerability.” section 12.4.1
- Classify articles as high risk or low risk from the language they contain. section 12.4.1
- Evaluate classifications with precision, recall, F1-score, accuracy, and a confusion matrix rather than relying on raw predictions alone. section 12.4.1
Threat-article triage
A cybersecurity team monitors online feeds for emerging malware. An NLP model converts article text into TF-IDF features and applies logistic regression to assign high- or low-risk labels. In the reported evaluation, accuracy was 0.67; class-specific precision and recall differed, showing why leadership should inspect missed threats and false alarms before operationalizing the model. section 12.4.1
Listing 12.4 NLP exercise
# Import required libraries
import pandas as pd #1
from sklearn.feature_extraction.text import #1
TfidfVectorizer #1
from sklearn.model_selection import train_test_split #1
from sklearn.linear_model import LogisticRegression #1
from sklearn.metrics import classification_report, #1
confusion_matrix #1
import seaborn as sns #1
import matplotlib.pyplot as plt #1
#1
# Step 1: Generate a balanced dataset #1
data = { #2
'Article': [ #2
"Ransomware targets financial institutions", #2
"Security patch released for major software vulnerability", #2
"Hackers steal millions of customer data in breach", #2
"Low-risk malware detected in corporate emails", #2
"Phishing campaign aims to steal credentials", #2
"Database vulnerability in hospital systems", #2
"Encryption policies updated to prevent breaches", #2
"Zero-day vulnerability in common software", #2
"Mandatory security training after an attack", #2
"Phishing attacks target large organizations" #2
] * 2, # Duplicate entries to balance classes #2
'Label': ['high-risk', 'low-risk', 'high-risk', #2
'low-risk', 'high-risk', #2
'low-risk', 'low-risk', 'high-risk', 'low-risk',
'high-risk'] * 2 # Balanced labels
}
df = pd.DataFrame(data) #3
# Step 2: Convert labels to numeric
df['Label'] = df['Label'].apply(lambda x:
1 if x == 'high-risk' else 0) #4
# Step 3: Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
df['Article'], df['Label'], test_size=0.3,
random_state=42) #5
# Step 4: Apply TF-IDF and Logistic Regression
vectorizer = TfidfVectorizer(stop_words='english') #6
X_train_tfidf = vectorizer.fit_transform(X_train) #7
X_test_tfidf = vectorizer.transform(X_test) #7
model = LogisticRegression() #8
model.fit(X_train_tfidf, y_train) #8
# Step 5: Make predictions
y_pred = model.predict(X_test_tfidf) #9
# Step 6: Evaluate the model
print("Classification Report:\n",
classification_report(y_test, y_pred)) #10
conf_matrix = confusion_matrix(y_test, y_pred) #11
# Step 7: Plot the confusion matrix (Grayscale)
plt.figure(figsize=(6, 4)) #12
sns.heatmap(conf_matrix, annot=True, cmap='Greys', #12
fmt='d', #12
xticklabels=['Low-Risk', 'High-Risk'], #12
yticklabels=['Low-Risk', 'High-Risk']) #12
plt.title('Confusion Matrix (Grayscale)') #12
plt.xlabel('Predicted Labels') #12
plt.ylabel('True Labels') #12
plt.show() #12
Inspect the Python exercise for dataset creation, text vectorization, model training, and evaluation of cybersecurity article classifications. section 12.4.1
