Semester.ly

Johns Hopkins University | EN.650.654

Computer Intrusion Detection

3.0

credits

Average Course Rating

(3.96)

Intrusion detection supports the on-line monitoring of computer system activities and the detection of attempts to compromise normal services. This course starts with an overview of intrusion detection tasks and activities. Detailed discussion introduces a traditional classification of intrusion detection models, applications in host-centered and distributed environments, and various intrusion detection techniques ranging from statistical analysis to biological computing. This course serves as a comprehensive introduction of recent research efforts in intrusion detection and the challenges facing modern intrusion detection systems. Students will also be able to pursue in-depth study of special topics of interest in course projects.

Fall 2012

(3.4)

Spring 2014

(3.91)

Spring 2015

(4.17)

Spring 2023

(4.34)

Fall 2012

Professor: Xiangyang Li

(3.4)

The best aspects of this course included the lab assignments and the practical content. The worst aspects of this course included the lack of organization and structure of the entire class. The professor was unclear and students did not learn enough content in the class to adequately perform wel on the assignments and essays. The course would improve if the content was more focused and if there was more emphasis on the lab assignments. Prospective students should be sure to go over the material carefully and be fully aware of the instructor’s grading system. 190

Spring 2014

Professor: Xiangyang Li

(3.91)

This course offered students a strong introduction to the role of information security in organizational strategy, and the professor did a great job of relaying content in a practical way using real world examples. The professor was also very responsible and patient with his students. Unfortunately, students complained about his lack of lecturing skills and enthusiasm. Also, the security related legislation about U.S. Federal Organization was difficult to understand, especially for international students. It was suggested that the professor cal on students to provoke discussion and be more energetic, that lectures be more organized and that tests be less intensive. If prospective students are not native English speakers they might experience chal enges with the essays.

Spring 2015

Professor: Xiangyang Li

(4.17)

The best aspects of the course included the practical exposure to detection techniques with DeterLab, and the enjoyable and informative final project. Several students had difficulty understanding the main points of the lectures as wel as identifying the specific questions being asked on the homework assignments. Because of the lack of organization and specificity, students struggled with test preparation and homework completion. Suggestions for improvement included more DeterLab assignments and other hands-on implementation projects. Prospective students should have a background in machine learning and data analytics, and should be prepared to do a lot of independent learning.

Spring 2023

Professor: Xiangyang Li

(4.34)