Kalendar, Marija

Preferred name

Kalendar, Marija

Official Name

Kalendar, Marija

Main Affiliation

marijaka@feit.ukim.edu.mk

Now showing 1 - 10 of 17

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Memory-Centric Approach of Network Processing in a Modified RISC-based Processing Core
(Future Technologies Conference 2016, FTC 2016, 2016-12)
Efnusheva, Danijela
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The persistent rapid and vast growth of Internet's population, considering number of users, servers, links, and many new applications, has led to exponential network traffic increase, stimulating the increased demand for greater capacity of the communication network. While the fiber optic links are capable to achieve multi-gigabit bandwidth, the router's network processing hardware still remains the bottleneck for communication in networks. Therefore, in this paper we investigate the applicability of a novel memory-centric approach of network processing in a modified RISC-based processing core. The proposed processing core provides direct access to memory resources, without the use of general-purpose registers (GPRs) and cache memory, and also implements memory aliasing to specific IP header fields, thus providing easier manipulation of network packet headers. The results of Ipv4/IPv6 network processing simulations verify that the proposed network processor core achieves comparable performances to the Intel's IXP RISC micro engine.
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FPGA implementation of IPv6 header processor
(Anhalt University of Applied Sciences, 2021-03)
Todorov, Zdravko
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Efnusheva, Danijela
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Cholakoska, Ana
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Federated Learning for Network Intrusion Detection in Ambient Assisted Living Environments
(Institute of Electrical and Electronics Engineers (IEEE), 2023-07)
Cholakoska, Ana
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Gjoreski, Hristijan
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Rakovic, Valentin
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Given the Internet of Things’ rapid expansion and widespread adoption, it is of great concern to establish secure interaction between devices without worsening the quality of their performance. The use of machine learning techniques has been shown to improve detection of anomalous behavior in these types of networks, but their implementation leads to poor performance and compromised privacy. To better address these shortcomings, federated learning (FL) has been introduced. FL enables devices to collaboratively train and evaluate a shared model while keeping personal data on site (e.g., smart homes, intensive care units, hospitals, and so on), thus minimizing the possibility of an attack and fostering real-time distribution of models and learning. This article investigates the performance of FL in comparison to deep learning (DL) with respect to network intrusion detection in ambient assisted living environments. The results demonstrate comparable performances of FL with DL while achieving improved data privacy and security.
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"FPGA Design of IP Packet Filter based on SNORT rules"
(10th International Conference on Information Society and Technology, ICIST 2020,, 2020-03)
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Efnusheva, Danijela
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Cholakoska, Ana
Contemporary technology advances increase the pace of rapid expansion of the number of computers and devices connected to the Internet on а daily basis. Therefore, one of the highest priority issues that need to be considered in this enormous network is the network security. The greater the number of connected users and devices, the attempts to invade privacy and data of connected users becomes more and more tempting to hostile users. Thus, Network Intrusion Detection Systems (NIDS) become more and more necessary in any network device connected to the Internet, and are taking the lead in the battle against intruders. This paper addresses the network security issues by implementing NIDS style hardware implementation network packet filter intended to provide fast packet processing and filtering. The hardware is based on several NIDS rules that can be programmed in the system's memory, thus enabling modularity and flexibility. The designed hardware unit is described in VHDL and implemented in a Virtex7 VC709 FPGA board. The simulation timing diagrams and FPGA synthesis (implementation) reports are discussed and analyzed in this paper.
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A Survey of Bias in Healthcare: Pitfalls of Using Biased Datasets and Applications
(Springer, Cham, 2023-07-09)
Velichkovska, Bojana
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Gjoreski, Hristijan
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Osmani, Venet
Artificial intelligence (AI) is widely used in medical applications to support outcome prediction and treatment optimisation based on collected patient data. With the increasing use of AI in medical applications, there is a need to identify and address potential sources of bias that may lead to unfair decisions. There have been many reported cases of bias in healthcare professionals, medical equipment, medical datasets, and actively used medical applications. These cases have severely impacted the quality of patients’ healthcare, and despite awareness campaigns, bias has persisted or in certain cases even exacerbated. In this paper, we survey reported cases of different forms of bias in medical practice, medical technology, medical datasets, and medical applications, and analyse the impact these reports have in the access and quality of care provided for certain patient groups. In the end, we discuss possible pitfalls of using biased datasets and applications, and thus, provide the reasoning behind the need for robust and equitable medical technologies.
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Managing real time IoT data with cloud computing services
(ETAI society of R. N. Macedonia, 2018)
Vasilevski, Dejan
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Cholakoska,Ana
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Efnusheva, Danijela
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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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FPGA design of IP packet filter based on SNORT rules
(Informaciono Drushtvo Srbije, 2020-03)
Efnusheva, Danijela
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Cholakoska, Ana
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Differentially Private Federated Learningfor Anomaly Detection in eHealth Networks
(ACM, 2021-09-21)
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Pfitzner, Bjarne
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Rakovic, Valentin
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Arnrich, Bert
Increasing number of ubiquitous devices are being used in the medical field to collect patient information. Those connected sensors can potentially be exploited by third parties who want to misuse personal information and compromise the security, which could ultimately result even in patient death. This paper addresses the security concerns in eHealth networks and suggests a new approach to dealing with anomalies. In particular we propose a concept for safe in-hospital learning from internet of health things (IoHT) device data while securing the network traffic with a collaboratively trained anomaly detection system using federated learning. That way, real time traffic anomaly detection is achieved, while maintaining collaboration between hospitals and keeping local data secure and private. Since not only the network metadata, but also the actual medical data is relevant to anomaly detection, we propose to use differential privacy (DP) for providing formal guarantees of the privacy spending accumulated during the federated learning.
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Machine Learning based Anomaly Detection in Ambient Assisted Living Environments
(2021-09)
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Rakovic, Valentin
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Pfitzner, Bjarne
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Arnrich, Bert

Kalendar, Marija

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