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Pregled bibliografske jedinice broj: 1003253

Neural Networks for File Fragment Classification


Vulinović, Kristijan; Ivković, Lucija; Petrović, Juraj; Skračić, Kristian; Pale, Predrag
Neural Networks for File Fragment Classification // MIPRO, 2019 Proceedings of the 42nd International Convention / Skala, Karolj (ur.).
Rijeka: GRAFIK, 2019. str. 1395-1399 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Neural Networks for File Fragment Classification

Autori
Vulinović, Kristijan ; Ivković, Lucija ; Petrović, Juraj ; Skračić, Kristian ; Pale, Predrag

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
MIPRO, 2019 Proceedings of the 42nd International Convention / Skala, Karolj - Rijeka : GRAFIK, 2019, 1395-1399

Skup
42nd International Convention for Information and Communication Technology, Electronics and Microelectronics - MIPRO

Mjesto i datum
20.05.2019., 24.05.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
File fragment classification ; file type detection ; artificial neural network ; convolutional neural network ; feed forward neural network ; file type forensics

Sažetak
Abstract - File fragment classification is an important step in file forensics in which filetypes are assumed based on their available content fragments. Methods typically used for this task utilize machine learning techniques on features like byte frequency distributions and fragment entropy measures. In this paper, a contribution to this field is made through exploration of novel approaches to the problem including feedforward artificial neural networks and convolution networks. Feedforward neural networks were trained with byte histograms and with byte-pair histograms, while convolution neural networks were trained with blocks consisting of 512 bytes of data obtained from the GovDocs1 dataset. The results suggest convolution neural networks are not as promising for this problem as feedforward artificial neural networks, and feedforward artificial neural networks showing great results.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike i računarstva, Zagreb