Table 1.

Comparative Analysis of the proposed scheme with related research.

References	Mechanism	Data Security	Data Integrity	Data Privacy	Availability	Non-Repudiation
Popoola et al. [22] (2021)	Federated Learning	Model is trained locally on devices	Poison attacks affect data integrity	Only local model gradients trained at the device are shared with the network	Device availability is not addressed in this study	Non-repudiation is not addressed in this study
Hussain et al. [23] (2021)	Dual Machine Learning	Data transmitted to centralized server is exposed to man-in-the-middle attacks	Machine Learning models train using compromised data	Data in transmission is exposed to man-in-the-middle attacks	Arithmetic operations are performed over an untrusted cloud server exposing computation process	Records of infected device are not maintained
Trajanovski et al. [24] (2021)	Honeypot	Delayed identification of compromised devices does not address data security	Delayed identification of compromised devices does not address data integrity	Delayed identification of compromised devices does not address data privacy	The research does not address device availability	Records of infected device are not maintained
Vinayakumar et al. [25] (2020)	Deep Learning using DNS Query	Man-in-the-middle attacks compromise data upload for model training	Man-in-the-middle attacks transmit corrupt data in transmission	Pseudo IDs preserve the privacy of users	The research does not address device availability requirement	Records of infected device are not maintained
Hayat et al. [26] (2022)	Machine Learning and Blockchain	Data is securely stored in Blockchain	Malicious devices are preregistered in the Blockchain network, transmitting compromised data to the Machine Learning model	Privacy of users are maintained by verifying identities at both the Edge and the cloud layer using dual signatures and identifiers	Malicious devices are ejected from the network	The study does not address recording of compromised devices.
Lekssays et al. [27] (2021)	Blockchain	Data is securely stored in Blockchain	Blockchain validates devices allowed to transmit data	Privacy of data is not addressed in the study	The study does not prevent spreading of botnet script	The study does not address recording of compromised devices
Sun et al. [28] (2021)	Blockchain and Encryption	Data storage in Blockchain prevents data manipulation	Public key-based authentication prevents corrupt data upload	The study does not address Data Privacy	The study does not prevent spreading of botnet script	Device information is stored in Blockchain for traceability
Xu et al. [29] (2021)	Blockchain and Smart Contracts	Consensus algorithm ensures stored data security	Infected IoT bots transmit data for anomaly detection	Secret keys provided to authorized members access data.	The study does not prevent spreading of botnet script	Device information is stored in Blockchain for traceability
Proposed scheme	Digital Twin and Blockchain	Authorized and registered Digital Twins share data	Synchronization between the Digital Twin and Packet Auditor verifies data transmission	Inspection of Packet Headers enables inspection of encrypted IP packets	Certificate revocation of Digital Twins prevents Botnet from spreading	IP address of infected devices are stored in the Blockchain