AI for Network & Cybersecurity Engineers - 10 Real Projects

Refresh your Python fundamentals with data types, collections, loops, functions, and object oriented programming, highlighted through networking automation and ai project examples.

Master numpy and pandas for efficient numeric and tabular data handling, including arrays, data frames, and CSV workflows for network security analytics.

Learn how Matplotlib transforms large network data into bar, line, scatter, pie, and histogram visuals, enabling quick insight from cpu, memory, and traffic trends.

learn to structure data for ai by defining datasets and features, and using training, testing, and validation splits, with feature engineering and cleaning to boost quality.

Explore the basics of machine learning and its place in artificial intelligence, covering supervised, unsupervised, and reinforcement learning with network traffic, data preparation, training, testing, and deployment.

Explore linear regression, decision trees, k means clustering, and k nearest neighbors within supervised and unsupervised learning, with networking use cases like latency prediction, traffic analysis, and intrusion detection.

Explore how neural networks and deep learning learn patterns from data to predict network threats, covering input to output layers, weights, biases, activation, and backpropagation.

Learn how to train and evaluate models using accuracy, confusion matrix, and classification report to interpret true/false positives and precision, recall, and F1 score in cyber security.

Learn to save models to files with Joblib or pickle, and load them to make predictions without retraining; use model.save and load_model in Keras and TensorFlow to move to production.

Refresh essential networking fundamentals, including IP addressing, subnetting, routing, switching, and firewall rules. Explore IPv4 versus IPv6 and practical Python tools for handling addresses.

Explore common security threats such as DoS attacks, port scans, and brute force, with real-world examples and AI detection indicators like packets per second and unusual port activity.

Transform raw log data into a clean csv dataset with pandas by parsing logs, structuring a data frame, and deriving event type, status, and source IP for ai training.

Leverage data collection tools such as Scapy, Netmiko, Paramiko, Nornir, Napalm, and REST APIs to capture packets, device logs, and statistics for AI training and dataset creation.

Classify network traffic with a random forest using Scapy-derived features (packet length, source port, destination port) to distinguish tcp, udp, and icmp on pcap data.

Detect anomalies in network traffic using unsupervised learning with k-means clustering on captured packets with Scapy, pandas, and sklearn, labeling normal and abnormal flows and visualizing results.

Analyze firewall logs to identify frequently blocked source IPs using a decision tree classifier in a supervised learning workflow, with train/test split, data preprocessing, and model evaluation.

Predicts whether a router or switch configuration is baseline compliant by converting text to numerical features with CountVectorizer and classifying with a decision tree, saving the model and vectorizer.

Detect port scan attacks with logistic regression on synthetic data, distinguishing normal from scan traffic using destination ports, packet count, and average packet size; evaluate via confusion matrix and accuracy.

Build and evaluate a random forest intrusion detection system using synthetic normal and attack traffic, with features like duration, source bytes, destination bytes, failed logins, and protocol.

Train a linear regression model on synthetic network data to predict latency from packet size, hop count, delay, and bandwidth, and assess performance with MAE, MSE, and R2.

Detect phishing URLs using a tf-idf vectorizer with character-level 3–5 gram analysis, save the model, and apply logistic regression. Evaluate with train-test split, confusion matrix, and accuracy; test new URLs.

Explore auto network threat classification using a convolutional neural network, built with TensorFlow, featuring data preprocessing, scaling, 4x4 image reshaping, and evaluation with confusion matrix and accuracy.

Develop an LSTM-based time-series model to forecast network bandwidth using a sliding-window lookback of 10, scale data with minmaxscaler, train with a TensorFlow sequential network, and evaluate with MSE.