2021
DOI: 10.48550/arxiv.2101.09878
|View full text |Cite
Preprint
|
Sign up to set email alerts
|
Ajesh Koyatan Chathoth,
Abhyuday Jagannatha,
Stephen Lee

Abstract: Internet of Things (IoT) devices are becoming increasingly popular and are influencing many application domains such as healthcare and transportation. These devices are used for real-world applications such as sensor monitoring, real-time control. In this work, we look at differentially private (DP) neural network (NN) based network intrusion detection systems (NIDS) to detect intrusion attacks on networks of such IoT devices. Existing NN training solutions in this domain either ignore privacy considerations o… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 27 publications
0
1
0
Order By: Relevance
“…However, like in the previous cases, privacy-preserving techniques are not integrated. More related to our proposal, [30] recently proposed two DP-based continuous learning methods that consider heterogeneous privacy requirements for different FL clients in an IDS system. However, the approach is based on a non-IoT-specific dataset (CSE-CIC-IDS2018), and different DP techniques are not compared.…”
Section: Related Workmentioning
confidence: 99%
“…However, like in the previous cases, privacy-preserving techniques are not integrated. More related to our proposal, [30] recently proposed two DP-based continuous learning methods that consider heterogeneous privacy requirements for different FL clients in an IDS system. However, the approach is based on a non-IoT-specific dataset (CSE-CIC-IDS2018), and different DP techniques are not compared.…”
Section: Related Workmentioning
confidence: 99%