Atanasovski, Vladimir
Preferred name
Atanasovski, Vladimir
Official Name
Atanasovski, Vladimir
Main Affiliation
Email
vladimir@feit.ukim.edu.mk
24 results
Now showing 1 - 10 of 24
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of Two-Tier LTE Network with Randomized Resource Allocation and Proactive Offloading(Springer Science and Business Media LLC, 2016-07-02); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine Learning Based Classification of IoT Traffic(Brno University of Technology, 2023-06); With the rapid expansion and widespread adoption of the Internet of Things (IoT), maintaining secure connections among active devices can be challenging. Since IoT devices are limited in power and storage, they cannot perform complex tasks, which makes them vulnerable to different types of attacks. Given the volume of data generated daily, detecting anomalous behavior can be demanding. However, machine learning (ML) algorithms have proven successful in extracting complex patterns from big data, which has led to active applications in IoT. In this paper, we perform a comprehensive analysis, including 4 ML algorithms and 3 neural networks (NNs), and propose a pipeline which analyzes the influence data reduction (loss) has on the performance of these algorithms. We use random undersampling as a data reduction technique, which simulates reduced network traffic data. The pipeline investigates several degrees of data loss. The results show that models trained on the original data distribution obtain accuracy that verges on 100%. XGBoost performs best from the classic ML algorithms. From the deep learning models, the 2-layered NN provides excellent results and has sufficient depth for practical application. On the other hand, when the models are trained on the undersampled data, there is a decrease in performance, most notably in the case of NNs. The most prominent change is seen in the 4-layered NN, where the model trained on the original dataset detects attacks with a success of 93.53%, whereas the model trained on the maximally reduced data has a success of only 39.39%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, eWALL: An Open-Source Cloud-Based eHealth Platform for Creating Home Caring Environments for Older Adults Living with Chronic Diseases or Frailty(Springer Nature, 2017-07-28) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Research Challenges, Trends and Applications for Multi-Sensory Devices in Future Networked Systems(Springer Nature, 2017-05-24); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hybrid access control with cell range expansion for LTE-A heterogeneous networks(IEEE, 2016); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Power optimization of LTE-800 and coexistence with DVB-T services(Elsevier BV, 2018-08); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Radio Spectrum: Evaluation approaches, coexistence issues and monitoring(Elsevier BV, 2017-07); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Visions Towards 5G: Technical Requirements and Potential Enablers(Springer Science and Business Media LLC, 2015-05-09); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Generic Multiuser Coordinated Beamforming for Underlay Spectrum Sharing(Institute of Electrical and Electronics Engineers (IEEE), 2016-06); ; ; Mahonen, Petri
- «
- 1 (current)
- 2
- 3
- »