short-paper

Verification Based Scheme to Restrict IoT Attacks

Authors:

Barjinder Kaur,

Sajjad Dadkhah,

Shahrear Iqbal,

Ali A. GhorbaniAuthors Info & Claims

BDCAT '21: Proceedings of the 2021 IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies

Pages 63 - 68

https://doi.org/10.1145/3492324.3494170

Published: 13 January 2022 Publication History

Abstract

In recent years, with the increased usage of the Internet of Things (IoT) devices, cyber-attacks have become a serious threat over the Internet. These devices have low memory capacity and processing power, which makes them easy targets for attackers. The research community has proposed different approaches to deal with emerging variants of attacks on IoT devices using various machine learning techniques. However, these approaches rely heavily on the classifier’s categorization of a given record while ignoring its confidence. This paper proposes a verification-based scheme to reject IoT attacks by utilizing the classifier’s confidence. At the same time, existing studies are evaluated using traditional cross-validation approaches (e.g., k-fold), thus, not tested against unknown attacks. We propose using the leave-one-attack-out (LOAO) cross-validation scheme to evaluate the generalizability of the application to unknown attacks. The experiments are performed on Med BIoT, a publicly available dataset consisting of three IoT attacks. The system’s robustness is evaluated in terms of Receiver Operating Curves (ROC) and Equal Error rates (EERs). The results indicate a lower false-positive rate of 12.6% using the proposed verification-based approach in comparison to k-fold cross-validation.

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Cited By

Neto EDadkhah SFerreira RZohourian ALu RGhorbani A(2023)CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT EnvironmentSensors10.3390/s2313594123:13(5941)Online publication date: 26-Jun-2023
https://doi.org/10.3390/s23135941
Neto EDadkhah SGhorbani A(2022)Collaborative DDoS Detection in Distributed Multi-Tenant IoT using Federated Learning2022 19th Annual International Conference on Privacy, Security & Trust (PST)10.1109/PST55820.2022.9851984(1-10)Online publication date: 22-Aug-2022
https://doi.org/10.1109/PST55820.2022.9851984

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Verification Based Scheme to Restrict IoT Attacks
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BDCAT '21: Proceedings of the 2021 IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies

December 2021

133 pages

ISBN:9781450391641

DOI:10.1145/3492324

Copyright © 2021 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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Published: 13 January 2022

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BDCAT '21: 2021 IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies

December 6 - 9, 2021

Leicester, United Kingdom

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Cited By

Neto EDadkhah SFerreira RZohourian ALu RGhorbani A(2023)CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT EnvironmentSensors10.3390/s2313594123:13(5941)Online publication date: 26-Jun-2023
https://doi.org/10.3390/s23135941
Neto EDadkhah SGhorbani A(2022)Collaborative DDoS Detection in Distributed Multi-Tenant IoT using Federated Learning2022 19th Annual International Conference on Privacy, Security & Trust (PST)10.1109/PST55820.2022.9851984(1-10)Online publication date: 22-Aug-2022
https://doi.org/10.1109/PST55820.2022.9851984

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