Expected learning outcomes
The candidate possesses advanced knowledge in detection and prevention of intrusions in modern computer systems and networks.
The candidate possesses thorough knowledge about theory and scientific methods relevant for intrusion detection.
The candidate is capable of applying his/her knowledge in new fields of intrusion detection and prevention.
The candidate is capable of analyzing existing theories, methods and interpretations in the field of intrusion detection and working independently on solving theoretical and practical problems.
The candidate can use relevant scientific methods in independent research and development in intrusion detection.
The candidate is capable of performing critical analysis of various literature sources and applying them in structuring and formulating scientific reasoning in the field of intrusion detection and prevention.
The candidate is capable of carrying out an independent limited research or development project in intrusion detection under supervision, following the applicable ethical rules.
The candidate is capable of analyzing relevant professional and research ethical problems in the field of intrusion detection.
The candidate is capable of applying his/her knowledge and skills in new fields, in order to accomplish advanced tasks and projects.
The candidate can work independently and is familiar with terminology in the field of intrusion detection and prevention.
The candidate is capable of discussing professional problems, analyses and conclusions in the field of intrusion detection and prevention, both with specialists and with general audience.
The candidate is capable of contributing to innovation and innovation processes.
1. Definition and classification of IDS systems
2. Basic elements of attacks against data networks and their detection
3. Misuse-based IDS
4. Anomaly-based IDS
5. Testing IDS and measuring their performances
Teaching Methods (additional text)
The course will be made accessible for both campus and remote students. Every student is free to choose the pedagogic arrangement form that is best fitted for her/his own requirement. The lectures in the course will be given on campus and are open for both categories of students. All the lectures will also be available on Internet through GUC’s learning management system (ClassFronter).
Form(s) of Assessment
Written exam, 3 hours
Evaluation of Project(s)
Form(s) of Assessment (additional text)
Written Exam, 3 hours (counts 70% of the final mark)
Project evaluation (counts 30% of the final mark)
Both parts must be passed.
Alphabetical Scale, A(best) – F (fail)
Evaluated by the lecturer. An external examiner will be used every 4th year. Next time in the school-year 2017/2018.
Ordinary re-sit examination.
1. Rebecca Gurley Bace, Intrusion Detection, Macmillan, 2000.
2. Jack Koziol, Intrusion Detection with SNORT, SAMS, 2003.
3. David J. Marchette, Computer Intrusion Detection and Network Monitoring - A Statistical Viewpoint, Springer Verlag, 2001.
4. Richard Bejtlich, Extrusion Detection - Security Monitoring for Internal Intrusions, Addison-Wesley, 2005.
5. Stephen Northcutt, Judy Novak, Network Intrusion Detection, 3rd edition, New Riders, 2003.
Replacement course for
IMT5151 Intrusion detection and prevention
In case there will be less than 5 students that will apply for the course, it will be at the discretion of Studieprogramansvarlig whether the course will be offered or not and if yes, in which form.