TY - GEN
T1 - Predicting Consequences of Cyber-Attacks
AU - Datta, Prerit
AU - Lodinger, Natalie
AU - Namin, Akbar Siami
AU - Jones, Keith S.
N1 - Funding Information:
As Table III indicates, Linear-SVC model had the best performance for both the tf-idf and Doc2Vec methods with 0.6 and 0.57 accuracy, respectively. It is worth noting that ACKNOWLEDGMENT This research work is supported by National Science Foundation (NSF) under Grant No: 1564293.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/10
Y1 - 2020/12/10
N2 - Cyber-physical systems posit a complex number of security challenges due to interconnection of heterogeneous devices having limited processing, communication, and power capabilities. Additionally, the conglomeration of both physical and cyber-space further makes it difficult to devise a single security plan spanning both these spaces. Cyber-security researchers are often overloaded with a variety of cyber-alerts on a daily basis many of which turn out to be false positives. In this paper, we use machine learning and natural language processing techniques to predict the consequences of cyberattacks. The idea is to enable security researchers to have tools at their disposal that makes it easier to communicate the attack consequences with various stakeholders who may have little to no cybersecurity expertise. Additionally, with the proposed approach researchers' cognitive load can be reduced by automatically predicting the consequences of attacks in case new attacks are discovered. We compare the performance through various machine learning models employing word vectors obtained using both tf-idf and Doc2Vec models. In our experiments, an accuracy of 60% was obtained using tf-idf features and 57% using Doc2Vec method for models based on LinearSVC model.
AB - Cyber-physical systems posit a complex number of security challenges due to interconnection of heterogeneous devices having limited processing, communication, and power capabilities. Additionally, the conglomeration of both physical and cyber-space further makes it difficult to devise a single security plan spanning both these spaces. Cyber-security researchers are often overloaded with a variety of cyber-alerts on a daily basis many of which turn out to be false positives. In this paper, we use machine learning and natural language processing techniques to predict the consequences of cyberattacks. The idea is to enable security researchers to have tools at their disposal that makes it easier to communicate the attack consequences with various stakeholders who may have little to no cybersecurity expertise. Additionally, with the proposed approach researchers' cognitive load can be reduced by automatically predicting the consequences of attacks in case new attacks are discovered. We compare the performance through various machine learning models employing word vectors obtained using both tf-idf and Doc2Vec models. In our experiments, an accuracy of 60% was obtained using tf-idf features and 57% using Doc2Vec method for models based on LinearSVC model.
UR - http://www.scopus.com/inward/record.url?scp=85103848493&partnerID=8YFLogxK
U2 - 10.1109/BigData50022.2020.9377825
DO - 10.1109/BigData50022.2020.9377825
M3 - Conference contribution
AN - SCOPUS:85103848493
T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
SP - 2073
EP - 2078
BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
A2 - Wu, Xintao
A2 - Jermaine, Chris
A2 - Xiong, Li
A2 - Hu, Xiaohua Tony
A2 - Kotevska, Olivera
A2 - Lu, Siyuan
A2 - Xu, Weijia
A2 - Aluru, Srinivas
A2 - Zhai, Chengxiang
A2 - Al-Masri, Eyhab
A2 - Chen, Zhiyuan
A2 - Saltz, Jeff
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Big Data, Big Data 2020
Y2 - 10 December 2020 through 13 December 2020
ER -