Detect-IoT: A Comparative Analysis of Machine Learning Algorithms for Detecting Compromised IoT Devices

Author:

Siwakoti Yuba R.1ORCID,Rawat Danda B.1ORCID

Affiliation:

1. Howard University, Washington DC, District of Columbia, United States

Funder

U.S. National Science Foundation

DoD Center of Excellence in AI and Machine Learning (CoE-AIML) at Howard University with the U.S. Army Research Laboratory

Publisher

ACM

Reference16 articles.

1. 2020. Amnesia:33 Identify and Mitigate the Risk From Vulnerabilities Lurking in Millions of IoT OT and IT Device. https://www.forescout.com/research-labs/amnesia33/ 2020. Amnesia:33 Identify and Mitigate the Risk From Vulnerabilities Lurking in Millions of IoT OT and IT Device. https://www.forescout.com/research-labs/amnesia33/

2. 2021. Data extraction laboratory - VARIoT . Retrieved May 10, 2023 from https://www.variot.eu/2021/10/07/data-extraction-laboratory/ 2021. Data extraction laboratory - VARIoT. Retrieved May 10, 2023 from https://www.variot.eu/2021/10/07/data-extraction-laboratory/

3. iDetect for vulnerability detection in internet of things operating systems using machine learning

4. Manish Bhurtel , Yuba R Siwakoti , and Danda B Rawat . 2022 . Phishing Attack Detection with ML-Based Siamese Empowered ORB Logo Recognition and IP Mapper. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 1--6. Manish Bhurtel, Yuba R Siwakoti, and Danda B Rawat. 2022. Phishing Attack Detection with ML-Based Siamese Empowered ORB Logo Recognition and IP Mapper. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 1--6.

5. SMOTE: Synthetic Minority Over-sampling Technique

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