2. Professors
  3. D'AMBROSIO CIRIACO
  4. Teaching

OPTIMIZATION FOR CYBERSECURITY

CIRIACO D'AMBROSIO OPTIMIZATION FOR CYBERSECURITY

0222800026
DEPARTMENT OF MANAGEMENT & INNOVATION SYSTEMS
EQF7
DATA SCIENCE E GESTIONE DELL'INNOVAZIONE
2024/2025

OBBLIGATORIO
YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2022
AUTUMN SEMESTER
CFUHOURSACTIVITY
535LESSONS
17LAB
RAFFAELE CERULLI T Curriculum
CIRIACO D'AMBROSIO Curriculum
ExamDate
CERULLI / D'AMBROSIO16/01/2025 - 10:00
CERULLI / D'AMBROSIO16/01/2025 - 10:00
CERULLI / D'AMBROSIO31/01/2025 - 10:00
CERULLI / D'AMBROSIO31/01/2025 - 10:00
CERULLI / D'AMBROSIO12/02/2025 - 10:00
CERULLI / D'AMBROSIO12/02/2025 - 10:00
CERULLI / D'AMBROSIO15/04/2025 - 10:00
Objectives
THE COURSE AIMS TO LET THE STUDENTS ACQUIRE THE KNOWLEDGE ON:
•FUNDAMENTALS OF MATHEMATICAL MODELING OF LINEAR OPTIMIZATION PROBLEMS
•ALGORITHMS AND SOFTWARE FOR SOLVING LINEAR PROGRAMMING PROBLEMS.
MOREOVER, THE STUDENT, AT THE END OF THE COURSE, WILL ACQUIRE THEORETICAL KNOWLEDGE AND PRACTICAL SKILLS TO FORMULATE AND SOLVE DECISION-MAKING PROBLEMS THROUGH APPROPRIATE MATHEMATICAL MODELS.

STUDENTS WILL ALSO DEVELOP:
• AUTONOMY IN MODELING COMPLEX DECISION-MAKING PROBLEMS
• ABILITY TO ASSESS THE COMPUTATIONAL COMPLEXITY OF THE DECISION-MAKING PROBLEMS
• ABILITY TO IDENTIFY THE MOST EFFECTIVE ALGORITHMS FOR PROBLEM SOLVING
• ABILITY TO DESIGN HEURISTIC ALGORITHMS TO OBTAIN ADMISSIBLE AND EFFICIENT (IN TERMS OF TIME) SOLUTIONS FOR THE CONSIDERED PROBLEMS
• ABILITY TO CORRECTLY TRANSFER MODELING AND OPTIMIZATION CONCEPTS TO DECISION MAKERS
• ABILITY TO USE SOFTWARE FOR SOLVING OPTIMIZATION PROBLEMS
• ATTITUDES TO LEARN ADVANCED NOTIONS IN THE FIELD OF OPTIMIZATION PROBLEMS IN ORDER TO ADDRESS CYBERSECURITY PROBLEMS.

DURING THE COURSE, THE STUDENT WILL FACE ANALYSIS, MODELING AND OPTIMIZATION TASKS RELATED TO COMPLEX SYSTEMS IN THE DOMAIN OF CYBERSECURITY.
Prerequisites
THERE ARE NOT SPECIFIC PRE-REQUIREMENTS FOR THIS COURSE.
Contents
1. LINEAR PROGRAMMING (PL) (7 HOURS OF LESSON AND 1 HOURS OF LABORATORY);

2 ALGORITHMS ALTERNATIVE TO THE SIMPLEX METHOD (9 HOURS OF LESSON AND 1 OF LABORATORY)
- SIMPLEX TABLEAU;

3. INTEGER LINEAR PROGRAMMING (PLI) (10 HOURS OF LESSON AND 2 HOURS OF LABORATORY)
- VARIABLES AND LOGICAL CONSTRAINTS; PROBLEMS WITH MULTIPLE OBJECTIVE FUNCTIONS;
- PRESENTATION OF THE MAIN COMBINATORIAL PROBLEMS;
- VALID INEQUALITIES.

4. SOLUTION APPROACHES FOR INTEGER LINEAR PROGNOSIS PROBLEMS: (9 HOURS OF LESSON AND 3 HOURS OF LABORATORY)
EXACT TYPE RESOLUTION METHODS AND HEURISTIC APPROACHES:
BRANCH AND BOUND;
- LOCAL SEARCH ALGORITHMS;
- GREEDY ALGORITHM;
- TABU SEARCH.
Teaching Methods
THE COURSE PROVIDES 42 HOURS OF ACTIVITIES (35 HOURS OF LECTURES AND 7 HOURS OF LABORATORY) WHICH TAKE PLACE IN THE CLASSROOM WITH THE AID OF PROJECTIONS; AT THE END OF THE PRESENTATION OF A TOPIC, VARIOUS APPLICATION EXAMPLES AND EXERCISES ARE PROVIDED. ATTENDANCE TO LESSONS IS NOT MANDATORY BUT STRONGLY RECOMMENDED.
Verification of learning
THE FINAL EXAM (ORAL EXAM) IS DESIGNED TO EVALUATE AS A WHOLE: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, AS WELL AS THE ABILITY TO APPLY SUCH KNOWLEDGE FOR THE RESOLUTION OF OPTIMIZATION PROBLEMS. THE ORAL EXAMINATION WILL COVER ALL THE TOPICS OF THE COURSE AND ASSESSMENT WILL TAKE INTO ACCOUNT THE KNOWLEDGE DEMONSTRATED BY THE STUDENT CONCERNING BOTH THE THEORETICAL AND APPLICATIVE ASPECTS FOR THE RESOLUTION OF THE OPTIMIZATION PROBLEMS. THE FINAL SCORE, WHEN THE EXAM IS PASSED, IS EXPRESSED ON THE BASIS OF THE SCALE FROM 18/30 (LIMITED KNOWLEDGE OF THE TOPICS) TO 30/30 LODE (THE CANDIDATE DEMONSTRATES SIGNIFICANT MASTERY OF THE CONTENTS).

Texts
- M.S. BAZARAA, J.J. JARVIS & H.D. SHERALI, LINEAR PROGRAMMING AND NETWORK FLOWS, FOURTH EDITION, JOHN WILEY, 2010.
- LECTURE NOTES
More Information
-THE COURSE LANGUAGE IS ITALIAN.
-PARTICIPATION TO THE LECTURES IS STRONGLY RECOMMENDED.
-THE EMAIL ADDRESS OF TEACHER IS: RAFFAELE@UNISA.IT
Lessons Timetable

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