Zobrazeno 1 - 10 of 10 pro vyhledávání: '"Dainius Čeponis"'
1
Akademický článek
Method for Attack Tree Data Transformation and Import Into IT Risk Analysis Expert Systems
Autor: Donatas Vitkus, Jonathan Salter, Nikolaj Goranin, Dainius Čeponis
Publikováno v: Applied Sciences, Vol 10, Iss 23, p 8423 (2020)
Information technology (IT) security risk analysis preventatively helps organizations in identifying their vulnerable systems or internal controls. Some researchers propose expert systems (ES) as the solution for risk analysis automation since risk a
Externí odkaz: https://doaj.org/article/38983a04cb014364bf1ce67ef682b2fe
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2
Akademický článek
Investigation of Dual-Flow Deep Learning Models LSTM-FCN and GRU-FCN Efficiency against Single-Flow CNN Models for the Host-Based Intrusion and Malware Detection Task on Univariate Times Series Data
Autor: Dainius Čeponis, Nikolaj Goranin
Publikováno v: Applied Sciences, Vol 10, Iss 7, p 2373 (2020)
Intrusion and malware detection tasks on a host level are a critical part of the overall information security infrastructure of a modern enterprise. While classical host-based intrusion detection systems (HIDS) and antivirus (AV) approaches are based
Externí odkaz: https://doaj.org/article/04db938172034b4596ef64036b62e941
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3
Akademický článek
Windows API hooking libraries research / Windows API funkcijų sekų perėmimo bibliotekų tyrimas
Autor: Lukas Radvilavičius, Dainius Čeponis
Publikováno v: Mokslas: Lietuvos Ateitis, Vol 3, Iss 1 (2011)
The paper describes methods how to apply Windows API hooking with third party libraries and solutions. In this research were used Windows API function SetWindowsHookEx, Detours and EasyHook libraries. Libraries methods, features and advantages were d
Externí odkaz: https://doaj.org/article/271abaff92f04a65bb684e30c7063dac
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5
Investigation of Dual-Flow Deep Learning Models LSTM-FCN and GRU-FCN Efficiency against Single-Flow CNN Models for the Host-Based Intrusion and Malware Detection Task on Univariate Times Series Data
Autor: Nikolaj Goranin, Dainius Čeponis
Publikováno v: Applied Sciences, Vol 10, Iss 2373, p 2373 (2020)
Applied Sciences
Volume 10
Issue 7
Intrusion and malware detection tasks on a host level are a critical part of the overall information security infrastructure of a modern enterprise. While classical host-based intrusion detection systems (HIDS) and antivirus (AV) approaches are based
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3e591f6fd671aff629557341a923340
https://www.mdpi.com/2076-3417/10/7/2373
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6
Akademický článek
An Empirical Determination of Optimum Artificial Intelligence Algorithm: Detection Using Signal-to-Noise Ratio Approach
Autor: Nana Kwame Gyamfi, Nikolaj Goranin, Dainius Ceponis
Publikováno v: IEEE Access, Vol 12, Pp 97712-97725 (2024)
This paper proposes a new metric to compare artificial intelligence (AI) algorithms for malware detection using system calls. With the increasing complexity of malware and the proliferation of AI algorithms, there is a need for a comprehensive metric
Externí odkaz: https://doaj.org/article/9c6beb7ac9b44a9689ac08781d2afd65
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7
Method for Attack Tree Data Transformation and Import Into IT Risk Analysis Expert Systems
Autor: Nikolaj Goranin, Donatas Vitkus, Jonathan Salter, Dainius Čeponis
Publikováno v: Applied Sciences
Volume 10
Issue 23
Applied Sciences, Vol 10, Iss 8423, p 8423 (2020)
Information technology (IT) security risk analysis preventatively helps organizations in identifying their vulnerable systems or internal controls. Some researchers propose expert systems (ES) as the solution for risk analysis automation since risk a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c191369ca5fb1a35ed228eb82aea241
http://dspace.vgtu.lt/handle/1/4119
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8
Evaluation of Deep Learning Methods Efficiency for Malicious and Benign System Calls Classification on the AWSCTD
Autor: Dainius Čeponis, Nikolaj Goranin
Publikováno v: Security and Communication Networks, Vol 2019 (2019)
The increasing amount of malware and cyberattacks on a host level increases the need for a reliable anomaly-based host IDS (HIDS) that would be able to deal with zero-day attacks and would ensure low false alarm rate (FAR), which is critical for the
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a482080176c1666a9bd44e4ed525bab3
https://doaj.org/article/c925b6c9d0954b24ae0f1cfeb5500e99
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10
Akademický článek
Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
Autor: Nana Kwame Gyamfi, Nikolaj Goranin, Dainius Ceponis, Habil Antanas Čenys
Publikováno v: Applied Sciences, Vol 13, Iss 21, p 11908 (2023)
Malware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms dependent on signatures for attack detection perform poorly due to high false negatives. Th
Externí odkaz: https://doaj.org/article/4038080383bd442da9943cffcf84e966
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