91294 - Cybersecurity And Cybercrime

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Industry, innovation and infrastructure Sustainable cities Partnerships for the goals

Academic Year 2021/2022

Learning outcomes

CYBERSECURITY AND CYBERCRIME By the end of the course, students will be able to: 1) Understand the foundations of cybersecurity. 2) Understand the social implications and ethical tradeoffs of surveillance, behavioural prediction made possible by the application of Big Data in cybersecurity. 3) Demonstrate the ability to formulate specific study questions concerning cybersecurity and how to fight cybercrime. 4) Understand the main tools and practices related to preventing cyberterrorism and cybercrime. 5) Demonstrate the ability to communicate complex cybersecurity concepts to multidisciplinary teams, including students with backgrounds in computer science, information technologies (IT) and international affairs.

Course contents

This is one of the few classes dedicated to cybersecurity in the social sciences, hence it is quite experimental. The class will provide some theoretical as well as practical bases to understand what cybersecurity, cyberwarfare and cybercrime are. Students successfully completing the class will also be able to recognise and debunk most myths and lies that abound in this field. Given the topic, students are expected to have at least some familiarity with computer networks. Students who do not satisfy this condition are expected to individually catch up during the course of the semester.

Class Topics (tentative; it is subject to changes)

  1. Introduction to the Class: structure of the class, references, method of evaluation (i.e. exams); the topic; what we mean by CS; brief excursus on the theory of war, terrorism and asymmetric warfare. (G.Giacomello + G.P. Siroli)
  2. Network Theory & Analysis (Social Network Analysis with Gephi) (GG)
  3. Threat Modelling Analysis and Social Engineering (GG)
  4. Critical Infrastructures (GG)
  5. Cyberwar (theory)- cyberwarfare (application) vs.Infowar(fare) nation-state (strategic Level) (GPS)
  6. Cyberwarfare: the Actors (GPS)
  7. Asymmetric Warfare (state and non-state actors; resources) (GPS)
  8. Cyberweapons: Stuxnet and its siblings (Operational Level) (GPS)
  9. Information Warfare (IW) & Psychological Ops (GPS)
  10. Digital battlefield (Operational and Tactical levels; drones, autonomous systems; nuclear weapons vulnerabilities. (GPS)
  11. Military applications of Artificial Intelligence; Lethal Autonomous Weapons Systems (LAWS) (GG)
  12. NSA leaks & CIA leaks (GPS); Topics for the final paper due
  13. Surveillance (PRISM, TOR, GCHQ, Cambridge Analytica) (GPS)
  14. Myths and Reality of Cybercrime Today (GG)
  15. Myths and Reality of Cyberterrorism Today (GG)
  16. Privacy & Data Protection: Cryptography (symmetric and asymmetric keys) (GG)
  17. Cyber Arms Control & Disarmament (the international framework, UN/GGE, EU; Italy) (GPS)
  18. Laboratory 1: Wireshark, Win network commands, Kali (penetration) (GG+GPS)
  19. Laboratory 2: Tails & Tor (anonymization), Buscador (OSIN), Deep Web (GG+GPS)
  20. Conclusions and wrapping-up (GG+GPS)

Readings/Bibliography

Readings on various class topics will be assigned on  later on. 

There is a "reference" textbook, which however does not cover all the topics presented in class. Students will have to rely mainly on their notes.

Textbook: Aaron F. Brantly and Damien Van Puyvelde, Cybersecurity: Politics, Governance and Conflict in Cyberspace, Oxford: Polity Press 2019.

 

Teaching methods

The course is organized according to the model of the structured seminar. The course is composed by 10 hours taught online weekly in 2-hour slots that prepare the seminar and by classes organized as seminars that will be held in presence in 10 meetings (3 hours per meeting). Students are required to carefully read the assigned material before the class and active participation through presentations of existing scholarship and case studies will also be expected.Regardless of the health-related conditions and the specific organization of the course, students will be able to follow the lessons of the entire course remotely on MS TEAMS. The seminar organization is detailed in the program that follows.

Plus, lectures, two laboratories, discussions and Q&A sessions. Some "hands-on" work at home will be necessary. 

Students are also encouraged to take the class on "Big Data for the Social Sciences" in the same term, if possible.

Assessment methods

Final research project and paper. The paper is to be at least 6000 words --more specifically, if you write a theoretical/historical paper, 6000 words would not be enough, you would have to go for 8000; if you write a more technically-oriented paper, then 6000 words is probably about right).

You should deliver it, by email to both instructors, a week before the date you decide to take the exam (i.e. a week before the 'appello' of your choice). Final papers delivered later than the deadline will be penalised. 

Teaching tools

Software Tools: Gephi; Wireshark; Kali; Tails; Tor; Buscador; CyberCIEGE and others.

Students will only be briefly introduced to these tools. If students want to develop some personal dexterity with these tools, they should plant to practice at home on their own.

Office hours

See the website of Giampiero Giacomello

See the website of Gian Piero Siroli