AI Security & Hacking Learning Path

Teaches the fundamentals of penetration testing for AI/LLM-based applications through self-paced video instruction and guided hands-on labs. Students will learn to detect and exploit common AI vulnerabilities such as prompt injection and sensitive information disclosure.

Learn how to make safer LLM apps by attacking various chatbot applications using prompt injections to understand security failures. This course, in collaboration with Giskard, teaches industry-proven red teaming techniques to proactively test, attack, and improve the robustness of your LLM applications.

Fortinet offers a free curriculum of courses covering security-driven networking, adaptive cloud security, AI-driven security operations, and zero-trust network access.

Explore the use of system resource usage data to reveal advanced attacker techniques and uncover hardware supply chain interdiction.

Deep learning has solved tons of interesting real-world problems in recent years. Apache Spark has emerged as the most important and promising Machine Learning tool and currently a stronger challenger of the Hadoop ecosystem. In this course, you’ll learn about the major branches of AI and get familiar with several core models of Deep Learning in its natural way. This comprehensive 3-in-1 course is a fast-paced guide to implementing practical hands-on examples, streamlining Deep Learning with Apache Spark. You’ll begin by exploring Deep Learning Neural Networks using some of the most popular industrial Deep Learning frameworks. You’ll apply built-in Machine Learning libraries within Spark, also explore libraries that are compatible with TensorFlow and Keras. Next, you’ll create a deep network with multiple layers to perform computer vision and improve cybersecurity with Deep Reinforcement Learning. Finally, you’ll use a generative adversarial network for training and create highly distributed algorithms using Spark.By the end of this course, you'll develop fast, efficient distributed Deep Learning models with Apache Spark.Contents and Overview This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with Apache Spark, covers deploying efficient deep learning models with Apache Spark. The tutorial begins by explaining the fundamentals of Apache Spark and deep learning. You will set up a Spark environment to perform deep learning and learn about the different types of neural net and the principles of distributed modeling (model- and data-parallelism, and more). You will then implement deep learning models (such as CNNs, RNNs, LTS Ms) on Spark, acquire hands-on experience of what it takes, and get a general feeling for the complexity we are dealing with. You will also see how you can use libraries such as Deeplearning4j to perform deep learning on a distributed C

Welcome to Building Machine Learning & NLP Models for Cyber Security course. This is a comprehensive project based course where you will learn how to build intrusion detection system, predict vulnerability score, and classify cyber threat using machine learning models like Random Forest Classifier, Logistic Regression, MLPs Regressor, Decision Tree Regressor, KNN, XG Boost, Naive Bayes, and K Means Clustering. This course is a perfect combination between machine learning and cyber security, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in system security. In the introduction session, you will learn about machine learning and natural language processing applications in cyber security, specifically how it can help to enhance risk management and strengthen overall security. Then, in the next section, we will learn how intrusion detection models work. This section will cover data collections, data preprocessing, feature selection, splitting data into training and testing sets, model selection, model training, detecting intrusion, model evaluation, deployment, and monitoring. Afterward, we will download cyber security datasets from Kaggle, it is a platform that offers many high quality datasets from various sectors. Once everything is all set, then, we will start the project, firstly, we will clean the dataset by removing all missing values and duplicates, after we make sure the data is clean and ready to use, we will start exploratory data analysis, firstly we are going to analyze the relationship between protocol type and intrusion, which will enable us to understand how different communication protocols contribute to intrusion risk, following that, we are also going to analyze intrusion rate for each browser type, which will allow us to uncover potential vulnerabilities associated with specific browsers, then, we are going to calculate the average login attempts and failed logins for both normal and intru