Tijana obtained her PhD degree in Software Engineering from the Faculty of Ogranizational Sciences, University of Belgrade (Serbia). Her doctoral dissertation "Software tool for investigating structural regression algorithms based on GCRF model" was defended in 2018. During her PhD studies, she was a visiting researcher at the Temple University (Philadelphia, USA), in the Center for Data Analytics and Biomedical Informatics, headed by professor Zoran Obradovic.
Tijana Markovic (former Vujicic), is currently a postdoc in the Division of Computer Science and Software Engineering (CSE). Her main research interests are applied machine learning and artificial intelligence. During her PhD, Tijana was working on structured machine learning for social networks, and more concretely on Gaussian conditional random fields extensions for directed graphs. In the current project, she applies machine learning for intrusion detection and anomaly classification.
Check more on Google Scholar
Simulation Environment for Modular Automation Systems (Oct 2022) Björn Leander, Tijana Markovic, Aida Causevic, Tomas Lindström , Hans Hansson, Sasikumar Punnekkat 48th Annual Conference of the Industrial Electronics Society (IECON 2022)
Comparative Evaluation of Machine Learning Algorithms for Network Intrusion Detection and Attack Classification (Jul 2022) Miguel Leon Ortiz, Tijana Markovic, Sasikumar Punnekkat International Conference on Neural Network (IJCNN)
Feature encoding with autoencoder and differential evolution for network intrusion detection using machine learning (Jul 2022) Miguel Leon Ortiz, Tijana Markovic, Sasikumar Punnekkat Genetic and Evolutionary Computation Conference (GECCO 2022)
Random Forest Based on Federated Learning for Intrusion Detection (Jun 2022) Tijana Markovic, Miguel Leon Ortiz, David Buffoni , Sasikumar Punnekkat International Converence on Artificial Intelligence Applications and innovations (AIAI 2022)