Deep Learning Enabled Secure IoT Handover Authentication for Blockchain Networks

【Author】 Salim, Mikail Mohammed; Shanmuganathan, Vimal; Loia, Vincenzo; Park, Jong Hyuk

【Source】HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES

【影响因子】6.558

【Abstract】Blockchain is an emerging key technology for safeguarding telecommunications networks against rogue base stations. Combining Internet of Things (IoT) devices with a decentralized network secures data transmission from machines to support cloud-based smart city applications. Critical applications such as Smart Healthcare deploy portable IoT devices such as blood pressure monitors, pacemakers, and electrocardiogram (ECG)-supported smartwatches to provide personalized services to users. Devices frequently move between different base stations to improve their coverage in hotspots and wireless link quality. Immutable ledgers in decentralized base stations ensure that data transmission from base stations to data centers is secure; still, it does not guarantee that the received data is from an authorized device. In the IoT layer, impersonation attacks involve a malicious user spoofmg an honest user and transmitting manipulated data to the base station. Attackers impersonate legitimate machines during the handover authentication process when devices move from one base station to another. This paper proposes a fast, efficient handover authentication (HO-Auth) scheme using deep learning to authenticate devices and build a user profile-based system for immediate authorization. The channel state information (CSI) of a user's movement pattern trains the model and detects malicious users spoofmg as honest users. The simulation-based analysis shows an initial profile accuracy of 0.91 in identifying a malicious device. The detection accuracy increases to 0.94 as the profile is retrained based on the movement of the user. The scheme ensures that the blockchain decentralized networks receive data from valid devices, protecting cloud applications from corrupt data.

【Keywords】Handover Authentication; Internet of Things; Deep Learning; IoT Security

【发表时间】2021 2022-05-15

【收录时间】2022-01-02

【文献类型】

【主题类别】

--