Enhancing the capacity of data collection tools to detect, prepare and respond to emerging CBRNe threats through engaging with end-users

  • Roberto Mugavero  ,
  • Pietro Costanzo  ,
  • William Thorossian  
  • University of Rome "Tor Vergata", Department of Electronic Engineering – DIE
  • e,b  University of the Republic of San Marino, Center for Security Studies – CUFS
  • a,b,c Observatory on Security and CBRNEe Defense - OSDIFE, Via del Politecnico, 1 - 00133, Rome, Italy
Cite as
Mugavero R., Costanzo P., Thorossian W. (2021). Enhancing the capacity of data collection tools to detect, prepare and respond to emerging CBRNEe threats through engaging with end-users. Proceedings of the 11th International Defence and Homeland Security Simulation Worskhop (DHSS 2021), pp. 1-8. DOI: https://doi.org/10.46354/i3m.2021.dhss.001
 Download PDF

Abstract

An Intelligence Platform for Chemical, Biological, Radiological, Nuclear and explosives (CBRNe) Events and Asymmetric Threats has been developed as a pilot prototype to meet the needs of organizations, specialist, experts, professionals from the intelligence, law-enforcement, military, chemical, biological, radiological/nuclear and health domains.
The main goal was to provide a tool able to collect open source information in an asymmetric threats environment, with a focus on CBRNe events and terrorism, and with a pilot focus on COVID-19 related information, and to generate outcomes that can help analysis of trends, threats and intelligence sources, with application across security, academia and health fields.
The developed IT solution is a flexible and innovative instrument offering support to CBRNe risk and Asymmetric threat knowledge management by monitoring a wide range of information sources, using normalized terminologies, based on tuned ontology and able to enhance interaction and communication between different international entities (semantic interoperability). The experimentation aimed to provide a lite tool that can be adopted at different levels, including where less skills and economic resources are available (local units of complex organizations, public administrations in developing Countries, SMEs, ONGs, media).
The activities have been carried out by the Observatory on Security and CBRNe Defence OSDIFE - Italy, in cooperation with the University of Rome “Tor Vergata” - Department of Electronic Engineering - Italy, the State University of the Republic of San Marino - Center for Security Studies, the Flinders University - Australia and Expert AI - Italy.

References

  1. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37-37.
  2. Ruan, D., Chen, G., Kerre, E. E., & Wets, G. (Eds.). (2005). Intelligent data mining: techniques and applications (Vol. 5). Springer Science & Business Media.
  3. Serrano, L., Bouzid, M., Charnois, T., Brunessaux, S., & Grilheres, B. (2013, November). Events extraction and aggregation for open source intelligence: From text to knowledge. In 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (pp. 518-523). IEEE.
  4. Liu, B. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge: Cambridge University Press. doi:10.1017/CBO9781139084789
  5. Costanzo, P., D’Onofrio, F., & Friedl, J. (2015). Big data and the Italian legal framework: Opportunities for police forces. In Application of big data for national security (pp. 238-249). Butterworth-Heinemann.
    Lindstrom, M. (2016). Small Data: The Tiny Clues that Uncover Huge Trends. St. Martin’s Press.
  6. Bergamaschi, S., Cappelli, A., Circiello, A., & Varone, M. (2017, June). Conditional random fields with semantic enhancement for named-entity recognition. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (pp. 1-7).
  7. Garcia-Silva, A., Palma, R., & Gomez-Perez, J. M. (2017, October). Semantic Technologies and Text Analysis in Support of Scientific Knowledge Reuse. In 2017 IEEE 13th International Conference on e-Science (e-Science) (pp. 454-455). IEEE.
  8. Deliu, I., Leichter, C., & Franke, K. (2017, December). Extracting cyber threat intelligence from hacker forums: Support vector machines versus convolutional neural networks. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 3648-3656). IEEE.
  9. Ranade, P., Mittal, S., Joshi, A., & Joshi, K. (2018, November). Using deep neural networks to translate multi-lingual threat intelligence. In 2018 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 238-243). IEEE.
  10. Ghazi, Y., Anwar, Z., Mumtaz, R., Saleem, S., & Tahir, A. (2018, December). A supervised machine learning based approach for automatically extracting high-level threat intelligence from unstructured sources. In 2018 International Conference on Frontiers of Information Technology (FIT) (pp. 129-134). IEEE.
  11. VoPham, T., Hart, J. E., Laden, F., & Chiang, Y. Y. (2018). Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology. Environmental Health, 17(1), 1-6.
  12. Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International journal of information management, 39, 156-168.
  13. Kim, N., Lee, S., Cho, H., Kim, B. I., & Jun, M. (2018, January). Design of a cyber threat information collection system for cyber attack correlation. In 2018 International Conference on Platform Technology and Service (PlatCon) (pp. 1-6). IEEE.
  14. Wang, M. H., Tsai, M. H., Yang, W. C., & Lei, C. L. (2018, April). Infection categorization using deep autoencoder. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1-2). IEEE.
  15. Wang, R., Ji, W., Liu, M., Wang, X., Weng, J., Deng, S., ... & Yuan, C. A. (2018). Review on mining data from multiple data sources. Pattern Recognition Letters, 109, 120-128.
  16. de la Torre-Abaitua, G., Lago-Fernández, L. F., & Arroyo, D. (2019). A compression based framework for the detection of anomalies in heterogeneous data sources. arXiv preprint arXiv:1908.00417.
  17. Pellet, H., Shiaeles, S., & Stavrou, S. (2019). Localising social network users and profiling their movement. Computers & Security, 81, 49-57.
  18. Choraś, M., Pawlicki, M., Kozik, R., Demestichas, K., Kosmides, P., & Gupta, M. (2019, August). SocialTruth project approach to online disinformation (fake news) detection and mitigation. In Proceedings of the 14th International Conference on Availability, Reliability and Security (pp. 1-10).