Technical gazette, Vol. 29 No. 4, 2022.
Preliminary communication
https://doi.org/10.17559/TV-20210523121614
Real-Time Cyber Attack Detection Over HoneyPi Using Machine Learning
Birkan Alhan
; Faculty of Engineering and Architecture, Istanbul Gelisim University, İstanbul, Turkey
Serkan Gönen
; Faculty of Engineering and Architecture, Istanbul Gelisim University, İstanbul, Turkey
Gökçe Karacayilmaz
orcid.org/0000-0001-8529-1721
; Forensic Sciences, Hacettepe University, Ankara, Turkey
Mehmet Ali Barişkan
; Faculty of Engineering and Architecture, Istanbul Gelisim University, İstanbul, Turkey
Ercan Nurcan Yilmaz
orcid.org/0000-0001-9859-1600
; Gazi University, Faculty of Technology, Ankara, Turkey
Abstract
The rapid transition of all areas of our lives to the digital environment has kept people away from their intertwined social lives and made them dependent on the isolated cyber environment. This dependency has led to increased cyber threats and, subsequently, cyber-attacks nationally or internationally. Due to the high cost of cybersecurity systems and the expert nature of these systems' management, the cybersecurity component has been mostly ignored, especially in small and medium-sized organizations. In this context, a holistic cybersecurity architecture is designed in which fully open source and free software and hardware-based Raspberry Pi devices with low-cost embedded operating systems are used as a honeypot. In addition, the architectural structure has an integrated, flexible, and easily configurable end-to-end security approach. It is suitable for different platforms by creating end-user screens with personalized software for network security guards and system administrators.
Keywords
Artificial Intelligence; Cyber Security; Honeypot; Internet of Things; LSTM; Naive Bayes
Hrčak ID:
279500
URI
Publication date:
17.6.2022.
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