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Master Intrusion Detection for Cybersecurity . Gain expertise in intrusion detection systems and advanced network analysis to effectively respond to cybersecurity threats.
Instructor: Jason Crossland
Included with
Recommended experience
Intermediate level
This specialization requires a foundational understanding of cybersecurity principles and familiarity with basic network concepts and technologies.
Recommended experience
Intermediate level
This specialization requires a foundational understanding of cybersecurity principles and familiarity with basic network concepts and technologies.
Explore the principles of intrusion detection systems and their role in enhancing cybersecurity defenses across various environments.
Gain hands-on experience with machine learning techniques to improve threat detection and incident response strategies in networks.
Develop a comprehensive understanding of Tor networking and its implications for privacy, security, and intrusion detection.
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November 2024
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The specialization "Intrusion Detection" is designed for postgraduate students seeking to enhance their expertise in cybersecurity with a focus on intrusion detection and incident response. Through three comprehensive courses, you will delve into the essential principles and advanced techniques necessary for effectively safeguarding networks and systems against cyber threats.
In the first course, "Introduction to Intrusion Detection Systems (IDS)," you'll learn the foundational concepts of IDS, including how to identify and respond to security incidents. The second course, "Advanced Network Analysis and Incident Response," builds on this knowledge by exploring sophisticated network analysis techniques and incident response strategies, enabling you to handle complex security challenges. The final course, "Machine Learning and Emerging Technologies in Cybersecurity," emphasizes the integration of machine learning techniques within cybersecurity frameworks, equipping you with cutting-edge skills in threat detection and prevention.
By completing this Specialization, you will be well-prepared to take on roles in cybersecurity, focusing on protecting organizations from evolving threats and ensuring robust security measures.
Applied Learning Project
In this specialization on Intrusion Detection, learners will engage in hands-on projects that delve into the principles of anomaly detection and the application of machine learning techniques in cybersecurity. One key project involves completing a problem set that challenges learners to analyze a dataset using defined parameters, including bias, weights, and an activation threshold. Participants will interpret data points to classify network behavior accurately, honing their skills in critical thinking and technical analysis. Additionally, learners will explore various intrusion detection methods, gaining practical experience with tools and techniques essential for identifying and mitigating threats in real-world environments. This project emphasizes practical problem-solving while critically assessing the effectiveness and limitations of different intrusion detection strategies.
Understand the roles of HIDS and NIDS, their key components, and applications across cybersecurity contexts.
Install and configure IDS solutions on VMs to detect real-time threats in host and network environments.
Compare signature-based and anomaly-based detection methods and configure IDS rules accordingly.
Use quantitative techniques to assess IDS effectiveness, analyzing data to select optimal solutions for security needs.
Understand the differences between network situational awareness and traditional NIDS for effective incident detection.
Gain proficiency in using GOTS and COTS tools for network packet analysis and troubleshooting networking challenges.
Learn to conduct ROC analysis on IDS data and interpret event graphs and precision-recall metrics for better decision-making.
Explore the NIST Cybersecurity Framework and SANS Incident Response Cycle to effectively manage and respond to cyber incidents.
Explore advanced machine learning techniques, including neural networks and clustering, for improved threat detection in cybersecurity.
Understand the integration of machine learning algorithms into Intrusion Detection Systems (IDS) for enhanced security measures.
Gain knowledge of The Onion Router (ToR) architecture and its applications, focusing on privacy and anonymous communication.
Learn to utilize Security Onion tools for effective incident response within high-volume enterprise environments, enhancing cybersecurity strategy.
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
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The specialization is designed to be completed at your own pace, but on average, it is expected to take approximately 3 months to finish if you dedicate around 5 hours per week. However, as it is self-paced, you have the flexibility to adjust your learning schedule based on your availability and progress.
You are encouraged to take the courses in the recommended sequence to ensure a smoother learning experience, as each course builds on the knowledge and skills developed in the previous ones. However, you are not required to follow a specific order, and you can take the courses in the order that best suits your needs and prior knowledge.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
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