Explore an introduction to security in deep learning, focusing on intrusion detection systems (IDS) and common machine learning pitfalls. Delve into the complexities of security data, the FASTT system, and issues of target leakage. Examine real-world examples like the "dog or muffin" problem, stop sign recognition, and wolf vs. husky classification to understand the challenges in training data and the importance of distinguishing correlation from causation. Learn about the KDD-99 dataset and malware classification as a complex security application. Discover techniques to prevent overfitting and gain insights into hardware considerations for deep learning in security contexts.