International Teaching | ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY
International Teaching ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY
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cod. 0622700094
ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY
| 0622700094 | |
| DEPARTMENT OF INFORMATION AND ELECTRICAL ENGINEERING AND APPLIED MATHEMATICS | |
| EQF7 | |
| COMPUTER ENGINEERING | |
| 2024/2025 |
| YEAR OF COURSE 2 | |
| YEAR OF DIDACTIC SYSTEM 2022 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/05 | 3 | 24 | LESSONS | |
| ING-INF/05 | 1 | 8 | LAB | |
| ING-INF/05 | 2 | 16 | EXERCISES |
| 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 ADVERSARIAL MACHINE LEARNING: ATTACK TECHNIQUES AND DEFENSE TECHNIQUES FOR AI RECOGNITION SYSTEMS (VOICE, FACE, FINGERPRINT, SIGNATURE). CYBERSECURITY APPLICATIONS: INTRUSION DETECTION IN COMPUTER NETWORKS, AUTOMATIC MALWARE DETECTION, NETWORK TRAFFIC ANOMALY DETECTION, STATIC CODE ANALYSIS. APPYING KNOWLEDGE AND UNDERSTANDING ABILITY TO DESIGN AND IMPLEMENT SOFTWARE SOLUTIONS BASED ON ARTIFICIAL INTELLIGENCE IN THE AREAS OF CYBERSECURITY. 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 MACHINE LEARNING AND THE C AND PYTHON PROGRAMMING LANGUAGE IS REQUIRED. |
| Contents | |
|---|---|
| DIDACTIC UNIT 1 - ADVERSARIAL MACHINE LEARNING (LECTURE/PRACTICE/LABORATORY HOURS 15/12/0) 1 (5 ORE LECTURE): INTRODUCTION TO ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY 2 (5 ORE LECTURE): ADVERSARIAL MACHINE LEARNING 3 (2 ORE PRACTICE): GRADIENT-BASED ADVERSARIAL ATTACKS IN PRACTICE 4 (3 ORE PRACTICE): DEEPFOOL AND CARLINI WAGNER ADVERSARIAL ATTACKS IN PRACTICE 5 (2 ORE PRACTICE): ADVERSARIAL DEFENSES IN PRATICE 6 (3 ORE LECTURE): BIOMETRICS SPOOFING 7 (2 ORE PRACTICE): FACE RECOGNITION ROBUSTNESS EVALUATION 8 (3 ORE PRACTICE): SPEAKER RECOGNITION ROBUSTNESS EVALUATION 9 (2 ORE LECTURE): GENERATIVE ADVERSARIAL NETWORKS IN CYBERSECURITY KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING THE CONCEPTS OF ADVERSARIAL MACHINE LEARNING, ADVERSARIAL ATTACKS AND ADVERSARIAL DEFENSE, OF THE BIOMETRICS SPOOFING TECHNIQUES AND OF THE GENERATIVE ADVERSARIAL NETWORKS FOR GENERATING SYNTHETIC SAMPLES APPLYING KNOWLEDGE AND UNDERSTANDING: CAPABILITY TO DESIGN AND GENERATE ATTACKS AND DEFENSES, AS WELL AS EVALUATING THEIR ROBUSTNESS, FOR BIOMETRICS RECOGNITION SYSTEMS. DIDACTIC UNIT 2 - MALWARE ANALYSIS (LECTURE/PRACTICE/LABORATORY HOURS 12/6/0) 10 (3 ORE LECTURE): INTRODUCTION TO MALWARE ANALYSIS WITH MACHINE LEARNING 11 (2 ORE PRACTICE): MALWARE ANALYSIS WITH MACHINE LEARNING IN PRACTICE 12 (2 ORE LECTURE): DEEP LEARNING METHODS FOR MALWARE ANALYSIS 13 (1 ORA PRACTICE): DEEP LEARNING METHODS FOR MALWARE ANALYSIS IN PRACTICE 14 (3 ORE LECTURE): MALWARE OBFUSCATION 15 (2 ORE PRACTICE): MALWARE OBFUSCATION IN PRACTICE 16 (2 ORE LECTURE): MACHINE LEARNING AND DEEP LEARNING FOR ANOMALY DETECTION 17 (1 ORA PRACTICE): MACHINE LEARNING AND DEEP LEARNING FOR ANOMALY DETECTION IN PRACTICE 18 (2 ORE LECTURE): STATIC CODE ANALYSIS KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING OF THE MAIN CHALLENGES RELATED TO THE ANALYSIS OF BINARY FILES TO DETECT MALWARE, ANOMALIES AND CODE VULNERABILITIES AND MACHINE LEARNING AND DEEP LEARNING METHODOLOGIES TO BE USED TO DETECT THEM. APPLYING KNOWLEDGE AND UNDERSTANDING: KNOWLEDGE AND UNDESTANDING OF COMMON TOOLS TO ANALYZE BINARY FILES, TRAFFIC NETWORK AND CODE AND USING THEM TOGETHER WITH MACHINE LEARNING AND DEEP LEARNING METHODOLOGIES TO REALIZE ROBUST DETECTION SYSTEMS. DIDACTIC UNIT 4 - FINAL PROJECT (LECTURE/PRACTICE/LABORATORY HOURS 0/0/3) 19 (3 ORE LABORATORIO): PROJECT WORK KNOWLEDGE AND UNDERSTANDING: UNDERSTANDING THE REQUIREMENTS OF THE FINAL PROJECT APPLYING KNOWLEDGE AND UNDERSTANDING: CAPABILITY TO DESIGN AND REALIZE IN GROUP A SYSTEM ROBUST TO MALICIOUS ATTACKS TOTAL LECTURE/PRACTICE/LABORATORY HOURS 27/18/3 |
| 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 EXERCISES 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 STATE OF THE ART TECHNOLOGIES. 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 ACHIEVEMENT OF THE TEACHING OBJECTIVES IS CERTIFIED BY PASSING AN EXAM WITH AN EVALUATION OUT OF THIRTY. THE EXAM INCLUDES THE DISCUSSION OF A PROJECT CARRIED OUT IN GROUPS (WITH GROUPS OF 3-4 PEOPLE) AND AN INDIVIDUAL ORAL INTERVIEW. THE REALIZATION OF THE PROJECT IS AIMED AT DEMONSTRATING THE ABILITY TO APPLY ARTIFICIAL INTELLIGENCE TECHNIQUES TO REAL CYBERSECURITY PROBLEMS (ADVERSARIAL MACHINE LEARNING, MALWARE ANALYSIS, INTRUSION DETECTION SYSTEMS). THE DISCUSSION OF THE PROJECT INCLUDES A PRACTICAL DEMONSTRATION OF THE REALIZED SYSTEM AND THE DEFENSE OF THE DESIGN CHOICES DESCRIBED IN THE PROJECT REPORT. THE ORAL INTERVIEW AIMS TO VERIFY THE LEVEL OF KNOWLEDGE AND UNDERSTANDING OF THE TOPICS COVERED IN THE COURSE, AS WELL AS THE STUDENT'S PRESENTATION ABILITY. |
| 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
