2021 IEEE International Conference on Cyber Security and Resilience

Full Program

Summary:

The rapid development of IoT applications and their use in various fields of everyday life has resulted in an escalated number of different possible cyber-threats and has consequently raised the need of securing IoT devices. Collecting Cyber-Threat Intelligence (e.g. zero-day vulnerabilities or trending exploits) from various online sources and utilising it to proactively secure IoT systems or prepare mitigation scenarios has proven to be a promising direction. In this work we focus on social media monitoring and investigate real-time Cyber-Threat Intelligence detection from the Twitter stream. Initially we compare and extensively evaluate six different machine-learning based classification alternatives trained with technical vulnerability reports and tested with real-world data from the Twitter stream to identify the best-fitting solution. Subsequently based on our findings we propose a novel social media monitoring system tailored to the IoT domain; the system allows users to identify recent/trending vulnerabilities and exploits on IoT devices.

Author(s):

Sofia Alevizopoulou    
University of the Peloponnese
Greece

Paris Koloveas    
University of the Peloponnese
Greece

Christos Tryfonopoulos    
University of the Peloponnese
Greece

Paraskevi Raftopoulou    
University of the Peloponnese
Greece

 


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