TY - GEN
T1 - A hybrid KRR-ML approach to predict malicious email campaigns
AU - Almukaynizi, Mohammed
AU - Shah, Malay
AU - Shakarian, Paulo
N1 - Funding Information:
ACKNOWLEDGMENT Some of the authors are supported by the Office of Naval Research (ONR) Neptune program. Paulo Shakarian is supported by the Office of the Director of National Intelligence (ODNI) and the Intelligence Advanced Research Projects Activity (IARPA) via the Air Force Research Laboratory (AFRL) contract number FA8750-16-C-0112. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements,
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/27
Y1 - 2019/8/27
N2 - Great success has been witnessed in the last few years for approaches combining Machine Learning (ML) with Knowledge Representation and Reasoning (KRR) to predict cybersecurity events. These approaches benefited from the high accuracy of ML, and the inherent transparency of KRR. In this paper, we develop a multi-layered, hybrid system that benefits from both approaches. When the developed system is fused with an existing statistical forecasting model, it demonstrates an average recall improvement of more than 14% while maintaining precision.
AB - Great success has been witnessed in the last few years for approaches combining Machine Learning (ML) with Knowledge Representation and Reasoning (KRR) to predict cybersecurity events. These approaches benefited from the high accuracy of ML, and the inherent transparency of KRR. In this paper, we develop a multi-layered, hybrid system that benefits from both approaches. When the developed system is fused with an existing statistical forecasting model, it demonstrates an average recall improvement of more than 14% while maintaining precision.
UR - http://www.scopus.com/inward/record.url?scp=85078824342&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078824342&partnerID=8YFLogxK
U2 - 10.1145/3341161.3343531
DO - 10.1145/3341161.3343531
M3 - Conference contribution
AN - SCOPUS:85078824342
T3 - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
SP - 895
EP - 898
BT - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
A2 - Spezzano, Francesca
A2 - Chen, Wei
A2 - Xiao, Xiaokui
PB - Association for Computing Machinery, Inc
T2 - 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
Y2 - 27 August 2019 through 30 August 2019
ER -