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to be adopted for tackle this issue.
Various ways and means to address this concern of IAM are going to be one of the prime focuses for the chapter. The following section analyzes how cloud enabled IoT solutions have attempted to handle IAM related matters in IoT ecosystem.
3.4 IoT Cloud Related Developments
Many efforts have been made by research community to efficiently handle the security related matters of IoT ecosystem. The authors of the chapter on Open Web Application Security Project (OWASP) [3] have listed and described most prominent 10 vulnerabilities associated with architecture of IoT. These features include important features like interfaces of entities related to the IoT architecture which are known as not secured, aspects like physical security of the system, inappropriately configured security configuration matters, insecure associated software and firmware.
In 2017 WAVE [4] was proposed. As best known to us, this was a novel and first approach using blockchain based and decentralized authorization in IoT environment. This made use of fine grained access control policies in conjunction with having smart contracts for event triggering functionality. However, functioning of blockchain nodes on constrained IoT devices was a troublesome matter. Hence to address this some trusted gateways were put to use for the devices in order to perform interaction to the blockchain network.
Several methods have been proposed by various researchers which require a detailed analysis for judging their efficiency and applicability along with related pros and cons. In the subsequent paras we are deliberation on prominent methods proposed since 2017 and also summarize their central ideas with a comparison among them.
Depending on the ad hoc nature of the IoT devices of the ecosystem for their access control, a requirement was felt for distributed IoT. Accordingly, capability-based access control (CBAC) proposed by Hussein et al. [5] proved to be well suited for the IoT environment compared to traditionally known access control models (Table 3.1).
Table 3.1 Comparison of access control method for IoT.
| Proposal year | Elementary method | Type of encryption | Key generation method | Access control | Mutual authentication | Anonymity of data | Integrity of data | Reference |
| 2017 | Decentralized | Symmetric | XOR Operation Based | Y | Y | N | Y | [5][6] |
| 2018 | Centralized | Asymmetric | Random No Generation & Hash Function | Y | N | Y | Y | [7][8] |
| 2018 | Centralized | Asymmetric | Elliptic curve cryptography | Y | Y | N | Y | [9] |
| 2018 | Centralized | Asymmetric | Fuzzy Extractor Gen Algorithm | Y | Y | N | Y | [10] |
| 2018 | Decentralized | Asymmetric | Elliptic curve cryptography | Y | Y | N | Y | [11] |
| 2019 | Centralized | Symmetric | Physical Unclonable Function And Fuzzy Extractor | Y | Y | Y | Y | [12] |
A computationally light weight authentication mechanism has been proposed by Aman et al. [6]. There is no need of any central server for storage mechanism of secret keys. This is based on Physical Unclonable Functions (PUFs) mechanism. Though it may be difficult to impersonate physical properties of the associated IoT devices but swapping of such devices with a malicious one could not be ruled out. The main drawback of the system was that, the mechanism required storing corresponding authentication credential details centrally. Accordingly, PUF based credentials kept is always a potential location for single point of failure.
In Vijaykumar et al. [7] introduced comparatively an updated authentication mechanism with respect to IoT devices. Better maintenance of privacy was achieved by using minimal information regarding used devices. This method used short group signatures and RSA algorithm. In order to have secured communication and associated generation and also distribution of encryption keys this is found to be a suitable mechanism. Anonymity feature for the signature holder’s identity is provisioned by short group signature mechanism. Prominent drawback of this mechanism is that it needed secret key to be stored in all associated devices for anonymizing identity. Limited storage availability with IoT devices was not in favor of this method. Above all this scheme needed very higher number of message exchange for authentication, thereby introducing unnecessary latency.
In Gong et al. [8] made use of remote validation mechanism for identifying node trust as well as for monitoring their behavior. This proposal consists of a model for measurement of trust where behavior of the sensing node data for transmission is considered. Hashing operation is used to prepare a threshold. Its storage was unable to address single point of failure. Observed drawback in a real time environment was pertaining to remote authentication deployment was unable to validate trust level of a node. Similarly, the mechanism could not provide tracking information regarding the associated nodes. Subsequently in order to generate trust threshold, a hash operation is performed and then the hash-value is stored at local server. The aforesaid means is found to be insecure with respect to vulnerability relating to single point of failure. High time and space complexity for processing resultantly makes it unsuitable for resource limited IoT devices.
In [9] the authors have proposed a specific mechanism which was intended for group authentication. Devices were required to be pre authenticated before being deployed in the system. This mechanism comprised of three aspects, namely the device, the group leader and subscriber server. It is mandatory for the device to be pre-registered and also pre-authenticated with the subscriber server for participating in the IoT network. The main drawback was single