TY - GEN
T1 - Usefulness of Honeypots Towards Data Security
T2 - 2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023
AU - Silaen, Kalpin Erlangga
AU - Meyliana, Meyliana
AU - Warnars, Harco Leslie Hendric Spits
AU - Prabowo, Harjanto
AU - Hidayanto, Achmad Nizar
AU - Anggreainy, Maria Susan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defense mechanisms intensified leading to a renewed focus on the utilization of honeypots to improve data security. This Systematic Literature Review (SLR) examines the usage and contributions of honeypots in data security field across diverse sectors and environments. Analyzing 38 selected articles from 2018 to 2023, the study highlights the growing adoption of honeypots as tools for detecting and preventing data breaches, exfiltration, and other security incidents. Our finding found that honeypots, with their deceptive capabilities, have proven could be deployed in sectors like healthcare, cloud computing, and industrial control systems and effective in detection and prevention data breaches, exfiltration, and other data breaches, making them as a crucial tool in the data security protection. The future of honeypots lies in their integration with machine learning and AI, to predict and counteract sophisticated. Moreover, evolving honeypots into tools for deterrence and corrective action opens new avenues for cybersecurity strategies. Their ability to act as deterrents by increasing perceived risks for attackers, coupled with their role in aiding recovery from data breaches through detailed forensic analysis, positions honeypots as a cornerstone in the development of resilient data security frameworks. This study paves the way for a more adaptive, proactive, and comprehensive approach to data security, highlighting the indispensable role of honeypots in the ever-evolving landscape of cybersecurity.
AB - The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defense mechanisms intensified leading to a renewed focus on the utilization of honeypots to improve data security. This Systematic Literature Review (SLR) examines the usage and contributions of honeypots in data security field across diverse sectors and environments. Analyzing 38 selected articles from 2018 to 2023, the study highlights the growing adoption of honeypots as tools for detecting and preventing data breaches, exfiltration, and other security incidents. Our finding found that honeypots, with their deceptive capabilities, have proven could be deployed in sectors like healthcare, cloud computing, and industrial control systems and effective in detection and prevention data breaches, exfiltration, and other data breaches, making them as a crucial tool in the data security protection. The future of honeypots lies in their integration with machine learning and AI, to predict and counteract sophisticated. Moreover, evolving honeypots into tools for deterrence and corrective action opens new avenues for cybersecurity strategies. Their ability to act as deterrents by increasing perceived risks for attackers, coupled with their role in aiding recovery from data breaches through detailed forensic analysis, positions honeypots as a cornerstone in the development of resilient data security frameworks. This study paves the way for a more adaptive, proactive, and comprehensive approach to data security, highlighting the indispensable role of honeypots in the ever-evolving landscape of cybersecurity.
KW - data security
KW - honeypot
KW - machine learning
KW - systematic literature review
UR - http://www.scopus.com/inward/record.url?scp=85189934363&partnerID=8YFLogxK
U2 - 10.1109/IWAIIP58158.2023.10462777
DO - 10.1109/IWAIIP58158.2023.10462777
M3 - Conference contribution
AN - SCOPUS:85189934363
T3 - IWAIIP 2023 - Conference Proceeding: International Workshop on Artificial Intelligence and Image Processing
SP - 422
EP - 427
BT - IWAIIP 2023 - Conference Proceeding
A2 - Jusman, Yessi
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 December 2023 through 2 December 2023
ER -