Course 2024-2025 a.y.

20651 - ARTIFICIAL INTELLIGENCE FOR SECURITY

Cross-institutional study L. Bocconi - Politecnico Milano

Course taught in English

Class timetable
Exam timetable
Go to class group/s: 25
CYBER (6 credits - I sem. - OB  |  ING-INF/05)
Course Director:
MARK JAMES CARMAN

Classes: 25 (I sem.)
Instructors:
Class 25: MARK JAMES CARMAN


Mission & Content Summary

MISSION

The use of Artificial Intelligence and in particular machine learning techniques has become prevalent in, and integral to, many cybersecurity applications including threat prediction, detection, and prevention. Understanding the capabilities and limitations of this technology is thus critical for assessing and managing cyber risk within an organization. This course introduces students to the main AI technologies used for classification, clustering and anomaly detection with practical applications in areas including malware analysis, and network security.

CONTENT SUMMARY

Lecture sessions: 

 

  • Lecture 1: Introduction to AI for security

  • Lecture 2: Introduction to Classification

  • Lecture 3: Classification Theory

  • Lecture 4: Linear classifiers

  • Lecture 5: Classification in Practice

  • Lecture 6: Classification in Practice (continued)

  • Lecture 7: Non-linear classification algorithms

  • Lecture 8: Non-linear classification algorithms (continued)

  • Lecture 9: Neural Networks

  • Lecture 10: Ensembles and Deep Learning

  • Lecture 11: Clustering algorithms

  • Lecture 12: Hierarchical clustering

  • Lecture 13: Univariate Anomaly detection

  • Lecture 14: Multivariate Anomaly detection and Time Series

 

Practical sessions: 

 

  • Practical 1: Introduction to Python

  • Practical 2: Spam detection

  • Practical 3: Fraud Detection

  • Practical 4: Model analysis and hyper-parameter selection

  • Practical 5: Intrusion detection

  • Practical 6: Intrusion Detection (continued)

  • Practical 7: Advanced classification techniques

  • Practical 8: Introduction to clustering

  • Practical 9: Evaluating clustering

  • Practical 10: Anomaly detection


Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...

     

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...

      


Teaching methods

  • Lectures
  • Practical Exercises

DETAILS

  • Lezioni
  • Esercitazioni pratiche

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
     x
  • Collaborative Works / Assignment (report, exercise, presentation, project work etc.)
 x    

ATTENDING AND NOT ATTENDING STUDENTS

  • Student group project presentations

  • Written Exam

 


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

To be defined

Last change 21/11/2024 11:40