🛡️ Anomaly Detection in Cybersecurity Using Machine Learning: An Application Example 🤖

Introduction:

Cybersecurity is increasingly crucial in today’s world. The rising cyber threats demand novel and effective methods. In this article, we’ll focus on anomaly detection in cybersecurity using machine learning.

Learning Objectives:

• Understand the fundamentals of machine learning 📚
• Learn machine learning techniques for anomaly detection 🛠️
• Develop an anomaly detection model using Python and Scikit-learn 🐍

import pandas as pd
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import LocalOutlierFactor
from sklearn.metrics import classification_report, confusion_matrix

Conclusion:

We highlighted the significance of machine learning techniques in cybersecurity and developed an anomaly detection model using network traffic data. Our model exhibited high accuracy and performance, making it suitable for real-world applications. For more information, check here.