@inproceedings{8b7896e6fff245e2b012ceb982cc13c8,
title = "Predicting Consequences of Cyber-Attacks",
abstract = "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.",
author = "Prerit Datta and Natalie Lodinger and Namin, {Akbar Siami} and Jones, {Keith S.}",
note = "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: {\textcopyright} 2020 IEEE.; null ; Conference date: 10-12-2020 Through 13-12-2020",
year = "2020",
month = dec,
day = "10",
doi = "10.1109/BigData50022.2020.9377825",
language = "English",
series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2073--2078",
editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, {Xiaohua Tony} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",
booktitle = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
}