Moses - Machine Learning for Computer Security

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Module / Version:
#41101 / #1

Validity:
Seit SoSe 2023

Default display language:
English

Module title:
Machine Learning for Computer Security

Credits:
6

Responsible person:
Rieck, Konrad

Grading:
benotet

Type of exam:
Schriftliche Prüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Softwaretechnik und Theoretische Informatik

Area of expertise:
34353000 FG Maschinelles Lernen und IT-Sicherheit

Prüfungsausschuss:
No information

Kontakt

Office:
No information

Contact person:
Rieck, Konrad

E-mail address:
rieck@tu-berlin.de

Website:

https://mlsec.tu-berlin.de

Learning Outcomes

Students have a comprehensive understanding of how machine learning is applied to security problems. They are able to design feature spaces for security data and select appropriate learning concepts and algorithms. They can develop learning-based systems for threat detection, malware analysis, and vulnerability discovery. They are also aware of security threats, such as poisoning and evasion, and know about countermeasures to mitigate them. They understand ethical implications of applying learning-based systems in practice.

Content

Principles of machine learning for computer security; feature spaces and embeddings; threat and intrusion detection; malware analysis; vulnerability discovery; deanonymization attacks; poisoning and evasion of security systems; unsupervised and supervised learning concepts for security; algorithms for classification, anomaly detection, clustering, and dimension reduction on security data; experimental design principles

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Machine Learning for Computer Security	IV		SoSe	English	4

Workload and Credit Points

Machine Learning for Computer Security (IV):

Workload description	Multiplier	Hours	Total
			180.0h(~6 LP)
Attendance	15.0	4.0h	60.0h
Pre/post processing	15.0	4.0h	60.0h
Exercise tasks	10.0	4.0h	40.0h
Preparation exam	1.0	20.0h	20.0h

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

This integrated course consists of a lecture combined with exercise assignments. The latter require independently solving programming exercises with the guidance of a teaching assistant.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

- Highly recommended: programming skills in Python; basic knowledge in security - Helpful, but not obligatory: Basic knowledge in machine learning.

Mandatory requirements for the module test application:

No information

Module completion

Grading

graded

Type of exam

Written exam

Language

English

Duration/Extent

90 minutes

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Sommersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Registration is not required, but stating the interest to participate in the lecture is welcome for the planning of resources. This is done by registering for the course at the teaching platform ISIS.

Recommended literature
No recommended literature given

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
Computer Engineering (M. Sc.)	1	5	SoSe 2023	SoSe 2023
Computer Science (Informatik) (M. Sc.)	1	6	SoSe 2023	SoSe 2023
Elektrotechnik (M. Sc.)	1	3	SoSe 2023	SoSe 2023
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)	1	2	SoSe 2023	SoSe 2023

Miscellaneous