NeuroTrust—Artificial-Neural-Network-Based Intelligent Trust Management Mechanism for Large-Scale Internet of Medical Things

Alabdulkareem

2020

Sensors

Self Cite

Recently, many platforms have outsourced tasks to numerous smartphone devices known as Mobile Crowd-sourcing System (MCS). The data is collected and transferred to the platform for further analysis and processing. These data needs to maintain confidentiality while moving from smartphones to the platform. Moreover, the limitations of computation resources in smartphones need to be addressed to balance the confidentiality of the data and the capabilities of the devices. For this reason, elliptic curve cryptography (ECC) is accepted, widespread, and suitable for use in limited resources environments such as smartphone devices. ECC reduces energy consumption and maximizes devices’ efficiency by using small crypto keys with the same strength of the required cryptography of other cryptosystems. Thus, ECC is the preferred approach for many environments, including the MCS, Internet of Things (IoT) and wireless sensor networks (WSNs). Many implementations of ECC increase the process of encryption and/or increase the space overhead by, for instance, incorrectly mapping points to EC with extra padding bits. Moreover, the wrong mapping method used in ECC results in increasing the computation efforts. This study provides comprehensive details about the mapping techniques used in the ECC mapping phase, and presents performance results about widely used elliptic curves. In addition, it suggests an optimal enhanced mapping method and size of padding bit to secure communications that guarantee the successful mapping of points to EC and reduce the size of padding bits.

Section: Related Workmentioning

confidence: 99%

iTrust—A Trustworthy and Efficient Mapping Scheme in Elliptic Curve Cryptography

Almajed

Alabdulkareem

2020

Sensors

Self Cite

“…Due to the heterogeneous environment of IoT, it is significant to implement robust approaches that maintain a secure environment by eliminating malicious nodes and also robust enough to keep resilience towards several potential attacks [4,8,32]. Trust is proposed as the most prominent lightweight mechanism that helps to maintain a secure environment by utilizing parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In [63], a generic IoT trust architecture is proposed that integrates trust into all these layers as an integral component to manage security. IoT faces several security challenges [36], e.g., authentication [3,31,37,38], access control [47,60], trust management in cross domain along with smart edge nodes [6,7,9,11,33], security management in IoT equipped with VANET nodes [10,27,35,45], policy enforcement [54], secure middleware [13], and confidentiality [50].…”

Section: Introductionmentioning

confidence: 99%

EdgeTrust - A Lightweight Data-centric Trust Management Approach for Green Internet of Edge Things

Awan

Din

et al. 2021

Preprint

Self Cite

Internet of Things (IoT) is bringing revolution into today’s world where devices in our surroundings become smart and perform daily-life activities and operations with more precision. The architecture of IoT is heterogeneous as it provides autonomy to nodes that they can communicate among other nodes and can also exchange information at any period. Due to the heterogeneous environment, IoT faces numerous security and privacy challenges, and one of the most significant challenges is the identification of malicious and compromised nodes. In this article, we have proposed a Machine Learning-based trust management approach for edge nodes. The proposed approach is a lightweight process to evaluate trust because edge nodes cannot perform complex computations. To evaluate trust, the proposed mechanism utilizes the knowledge and experience component of trust where knowledge is further based on several parameters. To eliminate the triumphant execution of good and bad-mouthing attacks, the proposed approach utilizes edge clouds, i.e., local data centers, to collect recommendations to evaluate indirect and aggregated trust. The trustworthiness of nodes is ranked between a certain limit where only those that satisfy the threshold value can participate in the network. To validate the performance of a proposed approach we have performed an extensive simulation in comparison with the existing approaches and the result shows the effectiveness of the proposed approach against several potential attacks.

“…Internet Things (IoT) [1] provides a diverse opportunity to automate distinct domains, which include wireless sensor networks [2,3], home appliances [4,5], smart cities [6][7][8], healthcare [9][10][11][12][13], security and surveillance [14][15][16], energy consumption [17][18][19], agriculture automation [20], and many more [21][22][23][24][25]. The concept of a smart irrigation system [26] is proposed in the Cloud-based Internet of Agriculture Things (IoAT) [27] in which sensors gather the findings of soil and transmit it towards the base station (BS) to take the required actions [28].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it estimates the final trust degree of a particular BS to compare it with the threshold value to make the final decision. The computation of finding aggregated trust is represented by Equation (11). is the number of available trust degree.…”

mentioning

confidence: 99%

AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things

Awan

Din

et al. 2020

Sensors

Self Cite

Internet of Things (IoT) provides a diverse platform to automate things where smart agriculture is one of the most promising concepts in the field of Internet of Agriculture Things (IoAT). Due to the requirements of more processing power for computations and predictions, the concept of Cloud-based smart agriculture is proposed for autonomic systems. This is where digital innovation and technology helps to improve the quality of life in the area of urbanization expansion. For the integration of cloud in smart agriculture, the system is shown to have security and privacy challenges, and most significantly, the identification of malicious and compromised nodes along with a secure transmission of information between sensors, cloud, and base station (BS). The identification of malicious and compromised node among soil sensors communicating with the BS is a notable challenge in the BS to cloud communications. The trust management mechanism is proposed as one of the solutions providing a lightweight approach to identify these nodes. In this article, we have proposed a novel trust management mechanism to identify malicious and compromised nodes by utilizing trust parameters. The trust mechanism is an event-driven process that computes trust based on the pre-defined time interval and utilizes the previous trust degree to develop an absolute trust degree. The system also maintains the trust degree of a BS and cloud service providers using distinct approaches. We have also performed extensive simulations to evaluate the performance of the proposed mechanism against several potential attacks. In addition, this research helps to create friendlier environments and efficient agricultural productions for the migration of people to the cities.