A Review on Honeypot-based Botnet Detection Models for Smart Factory

Since the Swiss Davos Forum in January 2017, the most searched keywords related to the Fourth Revolutionary Industry are AI technology, big data, and IoT. In particular, the manufacturing industry seeks to advance information and communication technology (ICT) to build a smart factory that integrates the management of production processes, safety, procurement, and logistics services. Such smart factories can effectively solve the problem of frequent occurrences of accidents and high fault rates. An increasing number of cases happening in smart factories due to botnet DDoS attacks have been reported in recent times. Hence, the Internet of Thing security is of paramount importance in this emerging field of network security improvement. In response to the cyberattacks, smart factory security needs to gain its defending ability against botnet. Various security solutions have been proposed as solutions. However, those emerging approaches to IoT security are yet to effectively deal with IoT malware, also known as Zero-day Attacks. Botnet detection using honeypot has been recently studied in a few researches and shows its potential to detect Botnet in some applications effectively. Detecting botnet by honeypot is a detection method in which a resource is intentionally created within a network as a trap to attract botnet attackers with the purpose of closely monitoring and obtaining their behaviors. By doing this, the tracked contents are recorded in a log file. It is then used for analysis by machine learning. As a result, responding actions are generated to act against the botnet attack. In this work, a review of literature looks insight into two main areas, i.e. 1) Botnet and its severity in cybersecurity, 2) Botnet attacks on a smart factory and the potential of the honeypot approach as an effective solution. Notably, a comparative analysis of the effectiveness of honeypot detection in various applications is accomplished and the application of honey in the smart factories is reviewed.

@article{Seungjin2020,
  title     = {A Review on Honeypot-based Botnet Detection Models for Smart Factory},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {6},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Lee Seungjin and Azween Abdullah and NZ Jhanjhi},
  doi       = {10.14569/IJACSA.2020.0110654},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110654}
}