Cited by 10Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingInternet of Things and AI

IoT botnet detection using deep learning

Rabhi, Sana, Abbes, Tarek, Zarai, Faouzi·2023·2023 International Wireless Communications and Mobile Computing (IWCMC)

Abstract

The Internet of Things (IoT) is an emerging technology which goal is to render the real world more intelligent, and it is expanding exponentially. This expansion poses a whole bunch of challenges, where the leading challenge is security. The focus of this research is on IoT botnet attacks, which is becoming a major attack for iot networks, detecting the Mrai botnet. We develop a mining based detection technique employing deep learning with different neural network models. We use the iot-23 dataset to determine if the behavior is legitimate or malicious and in a second phase, the most efficient model is used to predict a variant of mirai botnet; Hakai. Developped models enable us to achieve high accuracy that is superior to 90%..

Integrity signals

Registry

Crossref · 10.1109/iwcmc58020.2023.10182422

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Rabhi, S., Abbes, T., & Zarai, F.
Affiliations
University of Sfax (TN)
Venue
2023 International Wireless Communications and Mobile Computing (IWCMC)
Publisher
IEEE
Published
2023-06-19
Pages
1107-1111
OpenAlex
W4384945980
Cited by
10
References
1
Citations by year
2026: 2 · 2025: 4 · 2024: 3 · 2023: 1
FWCI
3.02
Type
conference paper
Language
en
Open access
closed
Locations
International Wireless Communications and Mobile Computing Conference (IWCMC) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords
IoT botnet detection, Mirai botnet, Internet of Things security, deep learning, neural network model, IoT-23 dataset, network traffic analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review