A Machine Learning-Based Cross-Layer DoS Attack Detection Technique for IoT

Title

A Machine Learning-Based Cross-Layer DoS Attack Detection Technique for IoT

Creator

Paul, Aditi; Chaudhary, Shirashti; Sinha, Somnath

Description

The most potent and common attacks on Internet of Things (IoT) are Denial of Service (DoS) attacks. Unfortunately, because the attack occurs on numerous layers, a single layer detection method is insufficient and ineffective to counteract these attacks. The current work focuses on the detection of cross-layer DoS assaults using Machine Learning-based multiclass classifiers. Three attacks against Routing Protocol (RPL) and Transmission Control Protocol (TCP) are detected using three ML Classifiers (KNN, Gradient Boosting, Random Forest). The novelty of the study is the design of cross-layer attack datasets using feature engineering technique. The performances of the classifiers are analyzed in presence of both balanced and imbalanced datasets. The results show that Gradient Boosting classifier has highest accuracy of up to 98% with deviation of up to 97%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1073;pp.11-23

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Cross-layer DoS attacks; IoT security; ML classifier; RPL attacks; Sinkhole attack

Coverage

Paul A., Banasthali Vidyapith, Rajasthan, Tonk, 304022, India; Chaudhary S., Banasthali Vidyapith, Rajasthan, Tonk, 304022, India; Sinha S., CHRIST (Deemed to be University), Bengaluru, 560029, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981975702-2;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-5703-9_2

https://www.scopus.com/pages/publications/105000326371?origin=resultslist