199.082 Machine Learning Security
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2018W, VU, 2.0h, 3.0EC, to be held in blocked form


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise

Aim of course

Artificial intelligence (AI) and machine learning (ML) techniques are being increasingly deployed in cyber-security settings. Examples of critical applications include network anomaly detection, biometric authentication, spam detection, and data analytics based financial fraud detection. At the same time, advanced ML algorithms also give attacker’s an advantage, setting up a complex interplay between attackers and defenders. An important example is in the area of web privacy; it has been shown sophisticated attackers can use advanced inference techniques to compromise the identity of web users. In response, web users can intentionally add ``noise” to their online behaviors to evade advanced recognition attacks, borrowing tools from the literature on differential privacy.

At the same time, as ML techniques become more sophisticated, they themselves are vulnerable to attack. These include stealthy training data poisoning attacks, and so-called ``adversarial input perturbations” which have to been shown to be particularly pernicious for deep neural networks. For these reasons, there is growing interest in techniques to develop and deploy verifiably safe and secure ML systems, adopting and adapting techniques from the software security domain. A final vulnerability involves the fact that modern ML systems and especially deep learning systems are trained and executed in the cloud, raising concerns about the privacy of the user’s data. New solutions are being developed to address these privacy concerns.

Subject of course

This is a visiting professor course of the Vienna PhD School of Informatics.

Additional information

This course will be held by Siddharth Garg, New York University/Tandon School of Engineering.

Course schedule:
The course will be held from January 7 - 15, 2019.

Details will be presented in the introductory lecture on Jan 7, 2pm, lecture room EI4



Course dates

Mon14:00 - 16:0007.01.2019EI 4 Reithoffer HS Introductory lecture
Tue10:00 - 12:0008.01.2019EI 2 Pichelmayer HS - ETIT Lecture - Machine Learning Security
Tue14:00 - 16:0008.01.2019EI 8 Pötzl HS - QUER Lecture - Machine Learning Security
Wed09:00 - 11:0009.01.2019EI 2 Pichelmayer HS - ETIT Lecture - Machine Learning Security
Wed14:00 - 16:0009.01.2019EI 2 Pichelmayer HS - ETIT Lecture - Machine Learning Security
Thu10:00 - 12:0010.01.2019EI 5 Hochenegg HS Lecture - Machine Learning Security
Thu14:00 - 16:0010.01.2019EI 4 Reithoffer HS Lecture - Machine Learning Security
Fri11:00 - 13:0011.01.2019EI 10 Fritz Paschke HS - UIW Lecture - Machine Learning Security
Fri14:00 - 16:0011.01.2019EI 3 Sahulka HS - UIW Lecture - Machine Learning Security
Mon10:00 - 12:0014.01.2019EI 5 Hochenegg HS Lecture - Machine Learning Security
Course is held blocked

Course registration

Begin End Deregistration end
01.10.2018 00:00 10.01.2019 23:59

Registration modalities

Please register in TISS.


Study CodeObligationSemesterPrecon.Info
786 881 Computer Sciences Mandatory elective
880 FW Elective Courses - Computer Science Elective
PhD Vienna PhD School of Informatics Not specified


No lecture notes are available.


  • Attendance Required!