Towards a Secure Signature Scheme Based on Multimodal Biometric Technology: Application for IOT Blockchain Network

Abstract

Blockchain technology has been commonly used in the last years in numerous fields, such as transactions documenting and monitoring real assets (house, cash) or intangible assets (copyright, intellectual property). The internet of things (IoT) technology, on the other hand, has become the main driver of the fourth industrial revolution, and is currently utilized in diverse fields of industry. New approaches have been established through improving the authentication methods in the blockchain to address the constraints of scalability and protection in IoT operating environments of distributed blockchain technology by control of a private key. However, these authentication mechanisms do not consider security when applying IoT to the network, as the nature of IoT communication with numerous entities all the time in various locations increases security risks resulting in extreme asset damage. This posed many difficulties in finding harmony between security and scalability. To address this gap, the work suggested in this paper adapts multimodal biometrics to strengthen network security by extracting a private key with high entropy. Additionally, via a whitelist, the suggested scheme evaluates the security score for the IoT system with a blockchain smart contract to guarantee that highly secured applications authenticate easily and restrict compromised devices. Experimental results indicate that our system is existentially unforgeable to an efficient message attack, and therefore, decreases the expansion of infected devices to the network by up to 49 percent relative to traditional schemes.

Biometrics for Internet-of-Things Security: A Review

“…Benefitted from the feature fusion, the multi-modal system increases recognition accuracy by more than 50% compared to that using only face or voice features. To strengthen network security, Hassen et al [ 47 ] extracted a private key from multi-modal biometrics (fingerprint and finger-vein) to authenticate and validate blockchain transactions. Experiment results demonstrate that the proposed method attains a high security level in defending spoofing and signature forgery with high throughput and low latency.…”

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

“…Physiological characteristics include those retrieved from the human body, such as iris [4], faces [5,6], retinas, veins [7], fingerprints [8][9][10][11], palm prints, finger knuckle print, and DNA, ECG [12][13][14][15], while behavioral characteristics include voice, stride, signature, and keystroke [16][17][18][19][20]. Biometrics traits are commonly employed in systems such as Security in IoT [21][22][23][24][25], e-banking [26][27][28][29], cloud security [30][31][32][33], access control [34][35][36][37], network security systems [38][39][40][41][42], and ID cards [43][44][45][46][47], and other applications related to IoT [48][49][50]…”

An Implementation of Trust Chain Framework with Hierarchical Content Identifier Mechanism by Using Blockchain Technology

“…Blockchain technology combined with a decentralized database offers the practical concept of realizing decentralized current MS. However, in the industrial sector, blockchain technology is only a concept and lacks implementation for the current MS [13][14][15]. It is still challenging to provide a consensus technique suitable for applications [16] in the industrial domain with reliable validation techniques.…”