Using Explainable AI for Neural Network-Based Network Attack Detection

被引：0

作者：

Zou, Qingtian ^{[1
]}

Zhang, Lan ^{[2
]}

Sun, Xiaoyan ^{[3
]}

Singhal, Anoop ^{[4
]}

Liu, Peng ^{[5
,6
]}

机构：

[1] Penn State Univ, State Coll, PA 16803 USA

[2] No Arizona Univ, Flagstaff, AZ 86011 USA

[3] Worcester Polytech Inst, Dept Comp Sci, Worcester, MA 01609 USA

[4] NIST, Comp Secur Div, Gaithersburg, MD 20899 USA

[5] Penn State Univ, CyberSecur, State Coll, PA 16803 USA

[6] Penn State Univ, Cyber Secur Lab, State Coll, PA 16803 USA

来源：

COMPUTER | 2024年 / 57卷 / 05期

关键词：

Compendex;

D O I：

10.1109/MC.2023.3342602

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Neural network (NN)-based network intrusion detection systems (NIDSs) are becoming popular these days due to their notable advantages. This article reviews the current application of explainable artificial intelligence techniques and tools for explaining the behavior of the NIDS.

引用

页码：78 / 85

页数：8

共 14 条

[1] Arya V, 2019, Arxiv, DOI arXiv:1909.03012
[2] Bastani O, 2019, Arxiv, DOI [arXiv:1705.08504, DOI 10.48550/ARXIV:1705.08504]
[3] Bastani O, 2018, ADV NEUR IN, V31
[4] Jacobs Arthur S., 2022, CCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, P1537, DOI 10.1145/3548606.3560609
[5] Mudrakarta PK, 2018, Arxiv, DOI arXiv:1805.05492
[6] Lakkaraju H, 2017, Arxiv, DOI [arXiv:1707.01154, DOI 10.48550/ARXIV.1707.01154]
[7] Lapuschkin S, 2016, J MACH LEARN RES, V17
[8] Phillips P.J., 2021, NISTIR 8312-Four Principles of Explainable Artificial Intelligence
[9] "Why Should I Trust You?" Explaining the Predictions of Any Classifier
Ribeiro, Marco Tulio
Singh, Sameer
Guestrin, Carlos
[J]. KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, : 1135 - 1144
[10] Shrikumar A, 2017, PR MACH LEARN RES, V70

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