TY - GEN
T1 - Deep Learning for Detecting Network Attacks
T2 - 35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2021
AU - Zou, Qingtian
AU - Singhal, Anoop
AU - Sun, Xiaoyan
AU - Liu, Peng
N1 - Funding Information:
This work was supported by NIST 60NANB20D180.
Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.
PY - 2021
Y1 - 2021
N2 - Network attack is still a major security concern for organizations worldwide. Recently, researchers have started to apply neural networks to detect network attacks by leveraging network traffic data. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new end-to-end approach to automatically generate high-quality network data using protocol fuzzing, and train the deep learning models using the fuzzed data to detect the network attacks that exploit the logic flaws within the network protocols. Our findings show that fuzzing generates data samples that cover real-world data and deep learning models trained with fuzzed data can successfully detect real network attacks.
AB - Network attack is still a major security concern for organizations worldwide. Recently, researchers have started to apply neural networks to detect network attacks by leveraging network traffic data. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new end-to-end approach to automatically generate high-quality network data using protocol fuzzing, and train the deep learning models using the fuzzed data to detect the network attacks that exploit the logic flaws within the network protocols. Our findings show that fuzzing generates data samples that cover real-world data and deep learning models trained with fuzzed data can successfully detect real network attacks.
UR - http://www.scopus.com/inward/record.url?scp=85112687268&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-81242-3_13
DO - 10.1007/978-3-030-81242-3_13
M3 - Conference contribution
AN - SCOPUS:85112687268
SN - 9783030812416
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 221
EP - 234
BT - Data and Applications Security and Privacy XXXV - 35th Annual IFIP WG 11.3 Conference, DBSec 2021, Proceedings
A2 - Barker, Ken
A2 - Ghazinour, Kambiz
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 July 2021 through 20 July 2021
ER -