Course content

IDS/IPS definition and classification

Basic elements of attacks and their detection

Misuse detection systems (search algorithms and applications in IDS)

Anomaly detection systems (learning, intelligence, and machine learning basics: principles, measures, performance evaluation, method combinations, basics of artificial neural networks, clustering (hierarchical and partitional) and supervised learning in IDS)

Testing IDS and measuring their performances

Computational complexity-theoretic and information-theoretic IDS models and quality criteria

Intrusion detection in virtual networks.

Learning outcome

Knowledge:

Possesses advanced knowledge in detection/prevention of intrusions in computer systems and networks, in particular:  application of advanced search algorithms in intrusion detection, unsupervised and supervised learning methods used in these systems, computational complexity-theoretic modeling, information-theoretic modeling of intrusion detection/prevention systems, and intrusion detection in virtual networks.

Possesses thorough knowledge about theory and scientific methods relevant for intrusion detection.

Is capable of applying his/her knowledge in design and analysis of intrusion detection/prevention systems.

 Skills:

Is capable of analyzing existing theories, methods and interpretations in the field of intrusion detection and working independently on solving theoretical and practical problems.

Can use relevant scientific methods in independent research and development in intrusion detection.

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.

Is capable of carrying out an independent limited research or development project in intrusion detection under supervision, following the applicable ethical rules.

General competence:

Is capable of analyzing relevant professional and research ethical problems in the field of intrusion detection.

Is capable of applying his/her knowledge and skills in new fields, in order to accomplish advanced tasks and projects.

Can work independently and is familiar with terminology in the field of intrusion detection and prevention.

Is capable of discussing professional problems in the field of intrusion detection and prevention, both with specialists and with general audience.

Is capable of contributing to innovation and innovation processes.

Learning methods and activities

Forelesninger|Lab.øvelser|Oppgaveløsning

Utfyllende informasjon:

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 the learning management system.

Obligatoriske arbeidskrav:

None.

Further on evaluation

Utfyllende om kontinuasjon:

Ordinary re-sit examination in August.

Specific conditions

Recommended previous knowledge

It is desirable to possess basic knowledge about the TCP/IP protocol stack.

Course materials

Compulsory literature:

 Various papers, uploaded in the learning management system.
Recommended literature:
Books on intrusion detection and prevention, such as

 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.

Credit reductions