TY - GEN
T1 - Detecting Cyber-Adversarial Videos in Traditional Social media
AU - Du, Bingyan
AU - Singhal, Pranay
AU - Benjamin, Victor
AU - Li, Weifeng
N1 - Funding Information:
V. CONCLUSION The CTI industry is continuing to grow and incorporate new data sources as evidenced by academic literature involving text analyses of DarkNet communities, as well as an increasing number of products/services offered by businesses to perform DarkNet monitoring. However, one common missing element among these previous efforts is analysis of hacking-related videos. Further, previous emphasis has been placed on the DarkNet while ignoring cybercriminal activity in traditional social media. This research is a step towards developing better understanding of cyber-adversarial behaviors on traditional social media, particularly within video formats. Future research will continue exploring this space by developing more robust video classification models, and also by analyzing what types of hacking videos become the most widely disseminated and drive community engagement among cyber-adversaries. ACKNOWLEDGMENT This research was supported in part by NSF CNS-1936370.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/9
Y1 - 2020/11/9
N2 - Cyber-threat intelligence (CTI) has matured and grown into its own industry within recent years. Many CTI efforts involve scrutinizing text-based conversations in DarkNet forums and markets. However, hackers commonly share knowledge and other information through video formats that have been largely ignored. Further, cybercriminals are increasingly making use of mainstream social media to transmit hacking knowledge and assets, but this has gone unexplored in literature. In this research-in-progress, a video classifier to detect cybercriminal content in mainstream social media is designed and implemented. A collection of hacking and non-hacking videos was retrieved from a popular social media website to serve as a testbed. Feature sets included video metadata as well as features engineered from the videos themselves, including object detection and aesthetic qualities. This study demonstrates a methodological proof-of-concept that can enable future research that further investigates cyber-adversarial video contents, which have remained largely unexplored to this day. This study also contributes to literature regarding cyber-adversarial contents in mainstream social media.
AB - Cyber-threat intelligence (CTI) has matured and grown into its own industry within recent years. Many CTI efforts involve scrutinizing text-based conversations in DarkNet forums and markets. However, hackers commonly share knowledge and other information through video formats that have been largely ignored. Further, cybercriminals are increasingly making use of mainstream social media to transmit hacking knowledge and assets, but this has gone unexplored in literature. In this research-in-progress, a video classifier to detect cybercriminal content in mainstream social media is designed and implemented. A collection of hacking and non-hacking videos was retrieved from a popular social media website to serve as a testbed. Feature sets included video metadata as well as features engineered from the videos themselves, including object detection and aesthetic qualities. This study demonstrates a methodological proof-of-concept that can enable future research that further investigates cyber-adversarial video contents, which have remained largely unexplored to this day. This study also contributes to literature regarding cyber-adversarial contents in mainstream social media.
KW - Cybersecurity
KW - DarkNet
KW - Video Analytics
UR - http://www.scopus.com/inward/record.url?scp=85098960002&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098960002&partnerID=8YFLogxK
U2 - 10.1109/ISI49825.2020.9280476
DO - 10.1109/ISI49825.2020.9280476
M3 - Conference contribution
AN - SCOPUS:85098960002
T3 - Proceedings - 2020 IEEE International Conference on Intelligence and Security Informatics, ISI 2020
BT - Proceedings - 2020 IEEE International Conference on Intelligence and Security Informatics, ISI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Intelligence and Security Informatics, ISI 2020
Y2 - 9 November 2020 through 10 November 2020
ER -