ANTONIO GRECO | ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY

cod. 0622700094

ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY

	0622700094
	DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
	EQF7
	COMPUTER ENGINEERING
	2021/2022

CURRICULUM INFORMATICA INDUSTRIALE Cohort 2020/2021

	YEAR OF COURSE 2
	YEAR OF DIDACTIC SYSTEM 2017
	SPRING SEMESTER

TEACHING UNITS

SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
ING-INF/05	3	24	LESSONS	OPTIONAL SUBJECTS
ING-INF/05	3	24	LAB	OPTIONAL SUBJECTS

PROFESSORS

	VINCENZO CARLETTI T Curriculum
	ANTONIO GRECO Curriculum

	Objectives
	THE GOAL OF THE COURSE IS TO PROVIDE THE STUDENT WITH ability to design and implement security applications based on the use of artificial intelligence techniques, attack techniques based on adversarial machine learning and related countermeasures. KNOWLEDGE AND UNDERSTANDING Biometric recognition: algorithms for the recognition of real and fake (silicone) fingerprints, recognition and verification of the authenticity of signatures, algorithms for identifying the speaker. Adversarial Machine Learning: attack techniques and defense techniques for AI recognition systems (voice, face, fingerprint, signature). Multimodal biometric techniques: biometric recognition by voice, face, fingerprint, signature. Cybersecurity applications: intrusion detection in computer networks, automatic malware detection. Intelligent video surveillance systems: detection of intrusions and anomalous behavior. APPYING KNOWLEDGE AND UNDERSTANDING ABILITY TO Design and implement software solutions based on artificial intelligence in the areas of cybersecurity, biometric analysis, intelligent video surveillance. Design and implement simple attack simulations to verify the degree of vulnerability of a system

THE GOAL OF THE COURSE IS TO PROVIDE THE STUDENT WITH ability to design and implement security applications based on the use of artificial intelligence techniques, attack techniques based on adversarial machine learning and related countermeasures.

KNOWLEDGE AND UNDERSTANDING
Biometric recognition: algorithms for the recognition of real and fake (silicone) fingerprints, recognition and verification of the authenticity of signatures, algorithms for identifying the speaker. Adversarial Machine Learning: attack techniques and defense techniques for AI recognition systems (voice, face, fingerprint, signature). Multimodal biometric techniques: biometric recognition by voice, face, fingerprint, signature. Cybersecurity applications: intrusion detection in computer networks, automatic malware detection. Intelligent video surveillance systems: detection of intrusions and anomalous behavior.

APPYING KNOWLEDGE AND UNDERSTANDING
ABILITY TO Design and implement software solutions based on artificial intelligence in the areas of cybersecurity, biometric analysis, intelligent video surveillance. Design and implement simple attack simulations to verify the degree of vulnerability of a system

	Prerequisites
	IN ORDER TO ACHIEVE THE GOALS OF THE COURSE, THE KNOWLEDGE OF THE C AND PYTHON PROGRAMMING LANGUAGE IS REQUIRED.

	Contents
	Introduction to the course (LECTURE / PRACTICE / LABORATORY HOURS 2/0/0) Biometric recognition: algorithms for the recognition of real and fake (silicone) fingerprints. Fingerprint Liveness Detection. Recognition and verification of the authenticity of signatures, algorithms for the identification of the speaker. (LECTURE / PRACTICE / LABORATORY HOURS 8/6/0) Adversarial Machine Learning: attack techniques and defense techniques for recognition AI systems (voice, face, fingerprint, signature). (LECTURE / PRACTICE / LABORATORY HOURS 8/6/0) Multimodal biometric techniques: biometric recognition by voice, face, fingerprint, signature. (LECTURE / PRACTICE / LABORATORY HOURS 4/2/0) Cybersecurity applications: intrusion detection in computer networks, automatic malware detection. (LECTURE / PRACTICE / LABORATORY HOURS 4/2/0) Intelligent video surveillance systems: detection of intrusions and anomalous behavior. (LECTURE / PRACTICE / LABORATORY HOURS 4/2/0) TOTAL LECTURE / PRACTICE / LABORATORY HOURS 30/18/0

Contents

Introduction to the course (LECTURE / PRACTICE / LABORATORY HOURS 2/0/0)

Biometric recognition: algorithms for the recognition of real and fake (silicone) fingerprints. Fingerprint Liveness Detection. Recognition and verification of the authenticity of signatures, algorithms for the identification of the speaker. (LECTURE / PRACTICE / LABORATORY HOURS 8/6/0)

Adversarial Machine Learning: attack techniques and defense techniques for recognition AI systems (voice, face, fingerprint, signature). (LECTURE / PRACTICE / LABORATORY HOURS 8/6/0)

Multimodal biometric techniques: biometric recognition by voice, face, fingerprint, signature. (LECTURE / PRACTICE / LABORATORY HOURS 4/2/0)

Cybersecurity applications: intrusion detection in computer networks, automatic malware detection. (LECTURE / PRACTICE / LABORATORY HOURS 4/2/0)

Intelligent video surveillance systems: detection of intrusions and anomalous behavior. (LECTURE / PRACTICE / LABORATORY HOURS 4/2/0)

TOTAL LECTURE / PRACTICE / LABORATORY HOURS 30/18/0

	Teaching Methods
	THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY exercitations. DURING THE IN-CLASS EXERCITATIONS THE STUDENTS ARE DIVIDED INTO TEAMS AND ARE ASSIGNED SOME PROJECT-WORKS TO BE DEVELOPED ALONG THE DURATION OF THE COURSE. THE PROJECTS INCLUDE ALL THE CONTENTS OF THE COURSE AND IS ESSENTIAL BOTH FOR THE ACQUISITION OF THE RELATIVE ABILITIES AND COMPETENCES, AND FOR DEVELOPING AND REINFORCING THE ABILITY TO WORK IN A TEAM. IN THE LABORATORY EXERCITATIONS THE STUDENTS IMPLEMENT THE ASSIGNED PROJECTS USING ROS. IN ORDER TO PARTICIPATE TO THE FINAL ASSESSMENT AND TO GAIN THE CREDITS CORRESPONDING TO THE COURSE, THE STUDENT MUST HAVE ATTENDED AT LEAST 70% OF THE HOURS OF ASSISTED TEACHING ACTIVITIES.

Teaching Methods

THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY exercitations. DURING THE IN-CLASS EXERCITATIONS THE STUDENTS ARE DIVIDED INTO TEAMS AND ARE ASSIGNED SOME PROJECT-WORKS TO BE DEVELOPED ALONG THE DURATION OF THE COURSE. THE PROJECTS INCLUDE ALL THE CONTENTS OF THE COURSE AND IS ESSENTIAL BOTH FOR THE ACQUISITION OF THE RELATIVE ABILITIES AND COMPETENCES, AND FOR DEVELOPING AND REINFORCING THE ABILITY TO WORK IN A TEAM. IN THE LABORATORY EXERCITATIONS THE STUDENTS IMPLEMENT THE ASSIGNED PROJECTS USING ROS.

IN ORDER TO PARTICIPATE TO THE FINAL ASSESSMENT AND TO GAIN THE CREDITS
CORRESPONDING TO THE COURSE, THE STUDENT MUST HAVE ATTENDED AT LEAST 70% OF THE HOURS OF ASSISTED TEACHING ACTIVITIES.

	Verification of learning
	THE EXAM AIMS AT EVALUATING, AS A WHOLE: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, THE ABILITY TO APPLY THAT KNOWLEDGE TO SOLVE PROGRAMMING PROBLEMS REQUIRING THE USE OF TECHNIQUES FOR AUTONOMOUS ROBOT NAVIGATION; INDEPENDENCE OF JUDGMENT, COMMUNICATION SKILLS AND THE ABILITY TO LEARN. THE EXAM INCLUDES TWO STEPS: THE FIRST ONE CONSISTS IN AN ORAL EXAMINATIONS AND IN THE DISCUSSION OF MID TERM PROJECTS REALIZED DURING THE COURSES. THE SECOND STEP CONSISTS IN THE REALIZATION OF A FINAL TERM PROJECT: THE STUDENTS, PARTITIONED INTO TEAMS, ARE REQUIRED TO REALIZE A SYSTEM, FINALIZED TO A COMPETITION AMONG THE TEAMS, DESIGNING AND METHODOLOGICAL CONTRIBUTIONS OF THE STUDENTS, TOGETHER WITH THE SCORE ACHIEVED DURING THE COMPETITION, ARE CONSIDERED FOR THE EVALUATION. THE AIM IS TO ASSESS THE ACQUIRED KNOWLEDGE AND ABILITY TO UNDERSTANDING, THE ABILITY TO LEARN, THE ABILITY TO APPLY KNOWLEDGE, THE INDEPENDENCE OF JUDGMENT, THE ABILITY TO WORK IN A TEAM. IN THE FINAL EVALUATION, EXPRESSED IN THIRTIETHS, THE EVALUATION OF THE INTERVIEW AND OF THE MID TERM PROJECTS WORK WILL ACCOUNT FOR 40% WHILE THE FINAL TERM PROJECT WILL ACCOUNT FOR 60%. THE CUM LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN APPLY THE KNOWLEDGE AUTONOMOUSLY EVEN IN CONTEXTS OTHER THAN THOSE PROPOSED IN THE COURSE.

Verification of learning

THE EXAM AIMS AT EVALUATING, AS A WHOLE: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, THE ABILITY TO APPLY THAT KNOWLEDGE TO SOLVE PROGRAMMING PROBLEMS REQUIRING THE USE OF TECHNIQUES FOR AUTONOMOUS ROBOT NAVIGATION; INDEPENDENCE OF JUDGMENT, COMMUNICATION SKILLS AND THE ABILITY TO LEARN.

THE EXAM INCLUDES TWO STEPS: THE FIRST ONE CONSISTS IN AN ORAL EXAMINATIONS AND IN THE DISCUSSION OF MID TERM PROJECTS REALIZED DURING THE COURSES. THE SECOND STEP CONSISTS IN THE REALIZATION OF A FINAL TERM PROJECT: THE STUDENTS, PARTITIONED INTO TEAMS, ARE REQUIRED TO REALIZE A SYSTEM, FINALIZED TO A COMPETITION AMONG THE TEAMS, DESIGNING AND METHODOLOGICAL CONTRIBUTIONS OF THE STUDENTS, TOGETHER WITH THE SCORE ACHIEVED DURING THE COMPETITION, ARE CONSIDERED FOR THE EVALUATION.
THE AIM IS TO ASSESS THE ACQUIRED KNOWLEDGE AND ABILITY TO UNDERSTANDING, THE ABILITY TO LEARN, THE ABILITY TO APPLY KNOWLEDGE, THE INDEPENDENCE OF JUDGMENT, THE ABILITY TO WORK IN A TEAM.

IN THE FINAL EVALUATION, EXPRESSED IN THIRTIETHS, THE EVALUATION OF THE INTERVIEW AND OF THE MID TERM PROJECTS WORK WILL ACCOUNT FOR 40% WHILE THE FINAL TERM PROJECT WILL ACCOUNT FOR 60%. THE CUM LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN APPLY THE KNOWLEDGE AUTONOMOUSLY EVEN IN CONTEXTS OTHER THAN THOSE PROPOSED IN THE COURSE.

	Texts
	LECTURE NOTES provided by the instructor THE TEACHING MATERIAL IS AVAILABLE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS.

	More Information
	THE COURSE IS HELD IN ENGLISH

BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2022-11-21]

ANTONIO GRECO | ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY