Faculty of Computer Science and Engineering
OrgUnit's Publications
(Articles)
Results 1-6 of 6 (Search time: 0.001 seconds).
| Preview | Title | Author(s) | Issue Date | Type | |
|---|---|---|---|---|---|
| 1 | Rapid Aspect-Oriented Assessment of Relational Database Design Assignments | Ajanovski, Vangel V. | 21-Sep-2022 | Proceeding article | |
| 2 | Inclusive Higher Education during the Covid-19 Pandemic | Zdravkova, Katerina ; Krasniqi, Venera | Sep-2021 | Proceeding article | |
| 3 | Fog Computing for Personal Health: Case Study for Sleep Apnea Detection | Ace Dimitrievski; Snezana Savoska; Trajkovikj, Vladimir | 29-May-2020 | Proceeding article | |
| 4 | Estimation of Blood Pressure from Arterial Blood Pressure using PPG Signals | Mladenovska, Teodora; Madevska Bogdanova, Ana ; Kostoska, Magdalena ; Koteska, Bojana ; Ackovska, Nevena | Aug-2023 | Proceeding article | |
| 5 | Epidemic spreading model of COVID-19 | Basnarkov, Lasko | 24-May-2020 | Article | |
| 6 | dbLearn*: Open-Source System and a Set of Practices for Conducting Iterative Exercises and Exams in a Databases Course | Ajanovski, Vangel | 6-Oct-2021 | Proceeding article |
OrgUnit's Researchers publications
(Dept/Workgroup Publication)
Subject
- 1 lightweight cryptography, cryptanalysis, known plaintext attack, machine learning, deep learning
- 1 Machine learning, DES, Cryptanalysis, Neural networks, Datasets
- 1 Our research focuses on attacking Ascon, a lightweight block cipher presented as a candidate in the NIST Lightweight Cryptography Standardization Process. This block cipher provides authenticated encryption with associated data functionalities. We propose a cryptanalysis model based on deep learning (DL), where the goal is to predict plaintext bits given knowledge of the ciphertext and other publicly known cipher input parameters. Our experiments show that such knownplaintext attacks can be successfully executed on a round reduced version of the cipher stripped of the finalization phase. This, in turn, validates the theoretical results. Cryptographic algorithms are complex for the purpose of security and cannot be easily broken by an ML model in their regular form (not reduced). We explore multiple dataset generation techniques, model design, and training hyperparameters.
- 1 quasigroup string transformation, classification of quasigroups
- 1 quasigroup, string transformation, parastrophe, parastrophic fractal and parastrophic non-fractal quasigroup
- 1 recursively differentiable quasigroups, orthogonality
Date issued
Fulltext
Results 1-7 of 7 (Search time: 0.01 seconds).
| Preview | Title | Author(s) | Issue Date | Type | |
|---|---|---|---|---|---|
| 1 | Application of Machine Learning in DES Cryptanalysis | Andonov, Stefan ; Dobreva, Jovana; Lumburovska, Lina; Pavlov, Stefan; Dimitrova, Vesna ; Popovska Mitrovikj, Aleksandra | 24-Sep-2020 | Proceedings | |
| 2 | Classification of quasigroups by image patterns | Dimitrova, Vesna ; Markovski, Smile | 2007 | Proceedings | |
| 3 | Cryptanalysis of Round-Reduced ASCON powered by ML | Jankovikj, Dushica; Mihajloska Trpceska, Hristina; Dimitrova, Vesna | 5-May-2022 | Proceedings | |
| 4 | Cryptanalysis of Round-Reduced ASCON powered by ML | Jankovikj, Dushica; Mihajloska Trpceska, Hristina; Dimitrova, Vesna | 2022 | Proceedings | |
| 5 | On Recursive Derivates of k-ary Operations | Mileva, Alexandra; Dimitrova, Vesna | 2017 | Proceedings | |
| 6 | Parastrophic quasigroup string processing | Popovska-Mitrovikj, Aleksandra; Bakeva, Verica ; Dimitrova, Vesna | 2011 | Proceedings | |
| 7 | Security Penetration Test On Fcse's It Services | Bozhinovski, Radoslav; Dimitrova, Vesna ; Jakimovski, Boro ; Ristov, Sashko | 2013 | Proceedings |
Organization name
Faculty of Computer Science and Engineering
Parent OrgUnit
City
Skopje
Country
Macedonia, the Former Yugoslav Republic of