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2021-01-28
Salib, E. H., Aboutabl, M. S..  2020.  Hands-on Undergraduate Labs on Anonymity Cryptographic Algorithms. 2020 IEEE Frontiers in Education Conference (FIE). :1—9.

This is an innovative practice full paper. In past projects, we have successfully used a private TOR (anonymity network) platform that enabled our students to explore the end-to-end inner workings of the TOR anonymity network through a number of controlled hands-on lab assignments. These have saisfied the needs of curriculum focusing on networking functions and algorithms. To be able to extend the use and application of the private TOR platform into cryptography courses, there is a desperate need to enhance the platform to allow the development of hands-on lab assignments on the cryptographic algorithms and methods utilized in the creation of TOR secure connections and end-to-end circuits for anonymity.In tackling this challenge, and since TOR is open source software, we identify the cryptographic functions called by the TOR algorithms in the process of establishing TLS connections and creating end-to-end TOR circuits as well tearing them down. We instrumented these functions with the appropriate code to log the cryptographic keys dynamically created at all nodes involved in the creation of the end to end circuit between the Client and the exit relay (connected to the target server).We implemented a set of pedagogical lab assignments on a private TOR platform and present them in this paper. Using these assignments, students are able to investigate and validate the cryptographic procedures applied in the establishment of the initial TLS connection, the creation of the first leg of a TOR circuit, as well as extending the circuit through additional relays (at least two relays). More advanced assignments are created to challenge the students to unwrap the traffic sent from the Client to the exit relay at all onion skin layers and compare it with the actual traffic delivered to the target server.

2020-05-11
Nikolov, Dimitar, Kordev, Iliyan, Stefanova, Stela.  2018.  Concept for network intrusion detection system based on recurrent neural network classifier. 2018 IEEE XXVII International Scientific Conference Electronics - ET. :1–4.
This paper presents the effects of problem based learning project on a high-school student in Technology school “Electronic systems” associated with Technical University Sofia. The problem is creating an intrusion detection system for Apache HTTP Server with duration 6 months. The intrusion detection system is based on a recurrent neural network classifier namely long-short term memory units.