short-paper

Intrusion Detection via Multilayer Perceptron using a Low Power Device

Authors:

Felipe de Almeida Florencio,

Edward David Moreno,

Hendrik Macedo,

Ricardo J. P. de B. Salgueiro,

Filipe Barreto do Nascimento,

Flavio Arthur Oliveira SantosAuthors Info & Claims

EATIS '18: Proceedings of the Euro American Conference on Telematics and Information Systems

Article No.: 11, Pages 1 - 5

https://doi.org/10.1145/3293614.3293642

Published: 12 November 2018 Publication History

Abstract

This work investigates the use of Multi-layered Perceptron Networks (MLP) for attack detection, using the Arduino embedded system as a case study. This paper also investigates techniques to reduce the computational cost of ANN (Artificial Neural Networks), taking into account the low cost and low consumption requirements in order to ensure the feasibility of its implementation. As a result, we evaluated the MLP networks using metrics such as accuracy, precision, and coverage, as well as the classifier performance running on Arduino through time measurements (microseconds).

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Cited By

Tyagi SPingulkar SShaikh A(2023)Multi-Class Network Intrusion Detection Using Deep Neural Networks Tuned on Imbalanced Data2023 IEEE International Carnahan Conference on Security Technology (ICCST)10.1109/ICCST59048.2023.10474236(1-5)Online publication date: 11-Oct-2023
https://doi.org/10.1109/ICCST59048.2023.10474236
Kabla AThamrin AAnbar MManickam SKaruppayah S(2022)PeerAmbush: Multi-Layer Perceptron to Detect Peer-to-Peer BotnetSymmetry10.3390/sym1412248314:12(2483)Online publication date: 23-Nov-2022
https://doi.org/10.3390/sym14122483

Index Terms

Intrusion Detection via Multilayer Perceptron using a Low Power Device
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems

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Published In

cover image ACM Other conferences

EATIS '18: Proceedings of the Euro American Conference on Telematics and Information Systems

November 2018

297 pages

ISBN:9781450365727

DOI:10.1145/3293614

Co-chairs:
Gabriel Paillard
Federal University of Ceará (UFC), Brazil
,
Francisco Milton Mendes Neto
Rural Federal University of the Semiarid (UFERSA), Brazil
,
Program Chairs:
Emanuel Ferreira Coutinho
Federal University of Ceará (UFC), Brazil
,
Rogério Patrício Chagas do Nascimento
Federal University of Sergipe (UFS), Brazil

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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EATIS: Euro American Association on Telematics and Information Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 November 2018

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EATIS '18

EATIS '18: Euro American Conference on Telematics and Information Systems

November 12 - 15, 2018

Fortaleza, Brazil

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Overall Acceptance Rate 17 of 64 submissions, 27%

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Cited By

Tyagi SPingulkar SShaikh A(2023)Multi-Class Network Intrusion Detection Using Deep Neural Networks Tuned on Imbalanced Data2023 IEEE International Carnahan Conference on Security Technology (ICCST)10.1109/ICCST59048.2023.10474236(1-5)Online publication date: 11-Oct-2023
https://doi.org/10.1109/ICCST59048.2023.10474236
Kabla AThamrin AAnbar MManickam SKaruppayah S(2022)PeerAmbush: Multi-Layer Perceptron to Detect Peer-to-Peer BotnetSymmetry10.3390/sym1412248314:12(2483)Online publication date: 23-Nov-2022
https://doi.org/10.3390/sym14122483

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