Python Script for Cyber Security

11. Cybersecurity

AI is also being used to improve cybersecurity in industrial settings by providing real-time monitoring and analysis of network systems. AI-powered systems can detect and respond to cyber threats, improving the overall security of industrial networks.

Example code in Python for detecting and responding to cyber threats:

import sys
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, classification_report
Load the dataset
df = pd.read_csv("cyber_data.csv")

Split the dataset into training and test sets
train_data, test_data, train_target, test_target = train_test_split(df.drop("class", axis=1), df["class"], test_size=0.2)

Train a Random Forest Classifier
clf = RandomForestClassifier()
clf.fit(train_data, train_target)

Predict on the test set
predictions = clf.predict(test_data)

Evaluate the performance of the model
print(confusion_matrix(test_target, predictions))
print(classification_report(test_target, predictions))

In this example, a Random Forest Classifier is trained on a dataset of cyber threats to detect and respond to such threats in real-time.