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Network Traffic Analysis and Control by Application of Machine Learning
(Springer, Cham, 2023-07-09)
Bogoevski, Zlate
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Jovanovski, Ivan
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Velichkovska, Bojana
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Efnusheva, Danijela
The purpose of this paper is to analyses how networks work, how data is transmitted, what information we get from each router during data transmission, getting to know the basics of machine learning and how to create models that will learn how networks work. By applying machine learning methods, results are obtained that show us the shape of a network. With different methods we can get information about how we can plan the network, in terms of expanding the network if the capacity of the links is almost full or when one of the links has predispositions to go from an active state to an inactive one. The results show satisfactory outcomes through the use of three different machine learning models that were capable of accurately detecting the functionality of a port, calculating its utilization and learning when the utilization hits a threshold of above 75%.
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Analysis of Early Cancer Diagnosis Using Machine Learning
(Springer, Cham, 2024)
Gjosheva, Marija
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Bogoevski, Zlate
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Velichkovska, Bojana
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Efnusheva, Danijela
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Cancer is a group of diseases with similar symptoms, all involving uncontrolled growth and reproduction of cells. With around 8 million deaths each year, it is the second leading cause of death worldwide in developing countries and the first in the developed world. In contemporary medicine, early cancer diagnosis for every known type is essential. Machine learning has the potential to completely transform the process and increase the number of lives saved. In order to make predictions, computers develop complex data models and search for patterns. Early cancer diagnosis could undergo a revolution because of machine learning. This research’s goal is to outline the issue surrounding cancer diagnoses in patients and all the difficulties they experience. A suitable strategy will be to model the risk of cancer and patient outcomes given the growing trend of employing machine learning technics in cancer research. A specific model has been developed that, if applied appropriately, can reduce the number of lost lives and, at the same time, increase the number of individuals capable of coping with this disease. The results indicate that the created model can be used by professionals to identify lung cancer with efficiency. If the prediction is accurate, the doctor may be able to develop a better treatment plan and provide the patient with an early diagnosis. The study's findings show that the number of patients has been rising recently, yet early detection is crucial because it can help avert serious complications.
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Network Anomaly Detection using Federated Learning for the Internet of Things
(2022)
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Jakimovski, Bojan
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Pfitzner, Bjarne
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Arnrich, Bert
The widespread use of IoT devices has contributed greatly to the continuous digitisation and modernisation of areas such as healthcare, facility management, transportation, and household. These devices allow for real-time mobile sensing, use input and then simplify and automate everyday tasks. However, like all other devices connected to a network, IoT devices are also subject to anomalous behaviour primarily due to security vulnerabilities or malfunction. Apart from this, they have limited resources and can hardly cope with such anomalies and attacks. Therefore, early detection of anomalies is of great importance for the proper functioning of the network and the protection of users’ personal data above all. In this paper, deep learning and federated learning algorithms are applied in order to detect anomalies in IoT network tra c. The results obtained show that all the models achieve high accuracy, with the FL models providing slight worse results compared to the DL models. However, with the increase in the amount of user data, the model based on federated learning is expected to have better results over time.
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A Monitoring System Design for Smart Agriculture
(Springer, 2022)
Bogoevski, Zlate
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Todorov, Zdravko
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Gjosheva, Marija
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Efnusheva, Danijela
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Cholakoska, Ana
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Analysis and modelling of a ML-based NIDS for IoT networks
(2022)
Karanfilovska, Martina
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Kochovska, Teodora
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Todorov, Zdravko
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Cholakoska, Ana
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Analysis of Smart Home Security by Applying Machine Learning Algorithms
(ETAI society of R. N. Macedonia, 2021-09)
Senchuk, Irina
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Cholakoska, Ana
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Efnusheva, Danijela
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Network Security Analysis by Applying Machine Learning Algorithms
(ETAI society of R. N. Macedonia, 2021-09)
Shushlevska, Martina
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Cholakoska, Ana
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Efnusheva, Danijela
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FPGA Implementation of Computer Network Security Protection with Machine Learning
(IEEE, 2021-09-12)
Todorov, Zdravko
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Efnusheva, Danijela
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Nikolic, Tatjana
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Analysis of Machine Learning Classification Techniques for Anomaly Detection with NSL-KDD Data Set
(Springer International Publishing, 2021)
Cholakoska, Ana
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Shushlevska, Martina
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Todorov, Zdravko
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Efnusheva, Danijela
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Survey of Security Issues, Requirements, Challenges and Attacks in Internet of Things
(Springer International Publishing, 2021)
Cholakoska, Ana
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Karanfilovska, Martina
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Efnusheva, Danijela

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