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Research on effective finding of network anomalies in Intrusion Detection Systems using ML/DL methods

In this paper, the process of testing datasets and ML/DL methods using the Weka platform in order to obtain results of their effectiveness and how high the accuracy of detecting network anomalies they provide is described. During the experiment, many ML/DL algorithms and the most common datasets (KDD99, NSL-KDD, UNSW-NB15) were analyzed, as a result of which it was revealed that the best result of detection accuracy and time required on the experiment was produced by the NSL-KDD dataset.

Author: Dmitrijs Bulaks

Supervisor: Jeļena Baranova

Degree: Bachelor

Year: 2024

Work Language: English

Small object search with RFID tags

This bachelor's thesis focuses on the development of a device for locating small-sized objects using RFID technology. An analysis of existing RFID technologies and their applications was conducted, which led to the selection of ISO 15693 and ISO 18000-3 standards for the development of the small-sized object locating device. Experimental results were obtained to study the detection capabilities of RFID tags under various conditions using the HBE-RFID REX setup. Structural, functional, and circuit diagrams of the device were developed based on the selected components: the TRF7970A RFID module from Texas Instruments, ID ANT310/310 antenna, ATXMEGA64D3 microcontroller, LM041L LCD screen, matrix 3x2 keyboard, LP9960115 battery, 2 LED indicators (red and green), and DC-DC buck converter (LM2596). Algorithms for the device's operation were developed for searching known tags, displaying saved tags, and searching for new tags, along with the corresponding software codes.

Author: Nikita Ostroveņecs

Supervisor: Aleksandrs Kraiņukovs

Degree: Bachelor

Year: 2024

Work Language: English

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