CYBERCRIME AND FRAUD DETECTION

As cybercrime continues to be a growing threat to critical-business infrastructure, global economies, and financial stability, there is a need for vigilance across all sectors, geographies, and industries. There are many techniques, tools, and technologies that telcos/oil&gas/financial services organizations can employ to protect their infrastructure, data, and people from a compromise. On occasion, it appears as if there is a surfeit of such techniques, tools, and technologies—and the number of available solutions is overwhelming to even the largest global organizations, and even more so to those who do not have mature, well-funded, and well-staffed security organizations. As global threats continue to increase in volume and complexity, it is often important to make certain we are doing the basics well: this course exactly aims to provide the basics of cybercrime technology and economics and fraud analysis/detection.

Although they are not prerequisites, the following courses are warmly recommended: -Cybersecurity Essentials LOFT at LUISS Guido Carli -Rapidminer Academy: Certification of Professional Machine Learning, Professional Data Engineering, Professional Application and Use cases

Knowledge and understanding: The student - by participating in the lectures and practical activities of the course - will have developed the ability to understand thethreats, risks, countermeasures in the field of cybercrime. Moreover, the students will understand the basic techniques to analyze frauds, with qualitative and quantitative methods: fraud analysis/detection cases will offer the opportunity to build an analytical capacity to cope with cybercrime-related frauds. At the end of the course there will be a written test. Applying knowledge and understanding: The student - acquiring the correct tools and method - will be able to identify cybercrime phenomenons, interpret fraud dataset, apply graph algorithms and present the results, but also analyze easy practical cases. At the end of the course there will be a written test. Making judgements: The student, through the use of the methodologies acquired during the course, will have acquired problem analysis skills and the ability to identify the information necessary for their solution. Specifically, critical and computational thinking, problem solving, self-management, teamwork, relationship and communication skills will be adequately developed, which enhance and make the disciplinary skills more usable Communication skills: At the end of the course the student will be able to use the business and technical vocabulary of cybercrime and fraud analysis, addressing the legal issues at hand with terminological accuracy. Through the various activities that will take place during the course – lessons with discussion, laboratories, workshops – the student will be able to put these communication skills into practice in various contexts, by adapting the terms used to the interlocutor in the specific case, thus gaining advanced rhetorical skills necessary for his/her professional career. Learning skills: The technical-cybersecurity knowledge acquired during the course will allow the student to autonomously understand and interpret cyberattacks techniques and machine learning techniques to be adapted to the specific reference context. The student will develop a solid knowledge of the fundamental aspects of the subject that will allow her/him to continue to deepen the topics addressed independently and to undertake the various post-graduate professional training courses.

The course will consist of twenty‑four two‑hour sessions covering: Cybercrime basics Tools and techniques of cybercrime Costs and harms of cybercrime Criminal marketplaces Cybercrime offenders and offender pathways Cybercrime prevention Regulation and policy Cybercrime and the legal framework Fraud analysis and detection: Setting the stage on fraud detection, prevention, and analytics, starting by defining fraud and then zooming into fraud detection and prevention. An essential introduction to data for fraud analysis: discussion of the basic ingredients of any fraud analytical model, including data quality, data types, and data preparation. Overview of fundamental data analysis techniques used to explore, summarize, and visualize fraud‑related data, with a focus on transparency, interpretability, and operational usability. Introduction to graph theory and its application to fraud detection and prevention, replacing traditional data mining techniques. Exploration of how networks and relational structures can reveal patterns, links, and anomalies in fraud cases. Social network analysis as a special case of graph‑based approaches, emphasizing how link structure and centrality can support investigative and analytic workflows.

Title:- Optional: G .Me, OSINT in the intelligence era, vol. 1

Traditional and Reverse teaching, through experiential classes with project-oriented approach

Grade is assessed on a Final Term Test (100%), which is a multiple choice test on theory and an exercise on a Neo4j fraud graph. The student has to demonstrate knowledge of the theoretical notions of teaching, knowing how to apply them in practical cases demonstrating that she/he has achieved the method of study and the learning ability necessary to continue the study of the subject autonomously. Lab sessions will often include homework assignments, due before the next lab. I will select students to present their solutions in class. Correct submissions will earn (3/total_assignments) points towards the final exam grade, where "total_assignments" refers to the total number of lab assignments given throughout the course. Students can improve their grades with projects homeworks: - Based on programming sw to analyse given datasets for frauds/crime; - Based on applying OSINT techniques based on one or more tools

Top level mark in the course /good knowledge in some computer systems topics (although out of the scope of the course) constitute a prerequisite for thesis assignment.