Machine Learning and Emerging Technologies for Intrusion Detection

Move beyond analytics to the algorithms, tools, and cyber incident response playbooks that power today’s best IDPS deployments.  In Johns Hopkins Engineering’s Machine Learning and Emerging Technology for Intrusion Detection course, created by senior APL engineer Jason Crossland, you will complete the final course in the Intrusion Detection certificate program.  

Each hands-on project will discuss and present various concepts of machine learning principles and algorithms used, anonymization, de-anonymization, and how “the onion router” (ToR) is used to obfuscate and hide a person’s Internet Protocol (IP) address.  We will conclude this course with a discussion on cybersecurity incident response (IR) management principles, prioritization approaches, and activities. The top five challenges for cyber IR management will be presented, and a case study reinforcing the importance of organizations having a cyber incident response plan will be provided. 
Imagine if no technologies, capabilities, or solutions existed to enhance IDPS activities or free cyber defenders, incident responders, and cybersecurity service providers (CSSPs) to focus on advanced and critical tasks such as identifying, detecting, protecting, responding, and recovering. Commercial IDPSs and related cybersecurity tools would advance more slowly, leaving our nation further behind cyber adversaries in adopting advanced tools, technologies, and processes needed to project cyber strength at scale.  

This course follows an industry-standard hacking methodology and builds critical skills across the entire hacking lifecycle—from reconnaissance to exploitation and post-access activities. Through interactive assignments, guided instruction, reflective essays, monthly live seminars with the instructor, and case studies, you will learn: 

• Train and tune ML models—supervised, unsupervised, and hybrid—to slash false-positives and surface true threats in host- and network-based systems 
• Demystify clustering and neural-network principles so you can choose the right algorithm for each data set and explain your decision to leadership
• Decode and defend against Tor traffic. Learn why encrypted, onion-routed flows slip past traditional sensors and design detection logic that respects privacy while blocking abuse 
• Map NIST-aligned incident-response steps into actionable playbooks, sequencing the people and technologies you just refined 

Each module ends with a portfolio project—training a live classifier, mapping anonymized traffic, or drafting a rapid-response workflow—and all deliverables ladder into a faculty-judged capstone where you redesign an enterprise IDPS architecture using your new ML pipelines and ToR insights. 

Become equipped to deploy smarter models, outwit obfuscated attackers, and lead response teams with confidence. 

No Risk: Satisfaction Guaranteed

Feel confident in your learning journey! If the certificate content is too advanced, not advanced enough, or simply doesn’t meet your expectations, we’ve got you covered with our money-back guarantee. Just contact our team within 7 days from purchase to receive a full refund—no questions asked.

Prerequisites

The Certificate is designed for cybersecurity professionals and experienced students. A baseline understanding of cybersecurity concepts will be useful, but there are no formal prerequisites. 

Course Delivery and Support

The courses are delivered entirely online through the industry-leading Canvas Learning Management System. This system is supported by the same instructional design team behind Johns Hopkins’ renowned Engineering for Professionals program, which serves thousands of online graduate students each year. Upon registration, you will receive an email with instructions to create your Hopkins Canvas account and access the videos, readings, files and quizzes.