Internet of Things 11 (2020) 100227

Contents lists available at ScienceDirect

Internet of Things

journal homepage: www.elsevier.com/locate/iot

Review article

Survey on IoT security: Challenges and solution using

machine learning, artificial intelligence and blockchain

technology

Bhabendu Kumar Mohanta a , ∗, Debasish Jena a , Utkalika Satapathy a , Srikanta Patnaik b

a Department of Computer Science & Engineering, IIIT Bhubaneswar, Odisha 751003, India b Department of Computer Science and Engineering, SOA University, Bhubaneswar 751030, India

a r t i c l e i n f o

Article history:

Received 24 January 2020

Revised 8 May 2020

Accepted 12 May 2020

Available online 20 May 2020

Keywords:

IoT

Security

Machine learning

Artificial intelligence

Blockchain technology

a b s t r a c t

Internet of Things (IoT) is one of the most rapidly used technologies in the last decade

in various applications. The smart things are connected in wireless or wired for commu-

nication, processing, computing, and monitoring different real-time scenarios. The things

are heterogeneous and have low memory, less processing power. The implementation of

the IoT system comes with security and privacy challenges because traditional based ex-

isting security protocols do not suitable for IoT devices. In this survey, the authors initially

described an overview of the IoT technology and the area of its application. The primary

security issue CIA (confidentially, Integrity, Availability) and layer-wise issues are identi-

fied. Then the authors systematically study the three primary technology Machine learn-

ing(ML), Artificial intelligence (AI), and Blockchain for addressing the security issue in IoT.

In the end, an analysis of this survey, security issues solved by the ML, AI, and Blockchain

with research challenges are mention.

© 2020 Elsevier B.V. All rights reserved.

1. Introduction

Internet of Things (IoT) is a network of smart things that share information over the internet. The smart things are used

to deploy in a different environment to capture the information, and some events are triggered. The applications of IoT is a

smart city, smart home, Intelligent transportation system, agriculture, hospital, supply chain system, earthquake detection, a

smart grid system. As per CISCO estimated, the IoT devices connected will be 50 billion at the end of 2020. The grown of IoT

devices is rapidly changing as it crosses the total world population. The data generated by the IoT devices are enormous. In

traditional IoT, architecture is three types physical, network, and application layer. In the physical layer, devices are embed-

ded with some technology which way they sense the environment and also able to connect in wired or wireless to the other

device. Like in the smart home system fridge can place an order automatically to the registered retailer whenever the fruits

chamber empty it, and notification will be sent to the home users. The similarity in smart hospital patients can monitor in

an emergency through sensors and corresponding computing devices. As the sensors are low-end devices, less computation

power, and have heterogeneous properties. Implementation of IoT comes with lots of challenges. The standardization, inter-

operability, data storage, processing, trust management, identity, confidentiality, integrity, availability, security, and privacy

∗ Corresponding author. E-mail addresses: [email protected] (B.K. Mohanta), [email protected] (D. Jena), [email protected] (U. Satapathy).

https://doi.org/10.1016/j.iot.2020.100227

2542-6605/© 2020 Elsevier B.V. All rights reserved.

2 B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227

Table 1

Related surveys work on IoT security.

Reference paper Year Contribution

Jing et al. [3] 2014 The security issue of three layers of IoT and its corresponding solution are surveyed in this paper.

Ngu et al. [4] 2016 The IoT middleware based architecture is proposed and explained each layer details. The authors also described

the adaptability and security issues in the IoT middleware system.

Mosenia et al. [5] 2016 The authors in this survey explained the reference model and security threads present on the edge side of the

model. The paper also reviewed the countermeasure to address the possible solutions.

Lin et al. [6] 2017 The paper initially described the IoT and Cyber-Physical Systems (CPS) integration. The security and privacy

issues survey in detail. The edge/fog computing integration with IoT is also explained in this survey paper.

Yang et al. [7] 2017 The paper has done a survey on security and privacy issue on IoT applications and systems. The authors

reviewed the authentication protocol in the IoT system. The challenging security issue in four-layer architecture

based IoT application are explained in details.

Alaba et al. [8] 2017 The authors in this survey investigated the state of art security issues in IoT applications. The threats and

vulnerability of the system in terms of communications, architecture, and applications are extensively reviewed.

the paper concludes with the solution approach for different security issues.

Grammatikis et al. [9] 2018 The paper provides a detailed study of IoT security layer-wise. The suitable countermeasure and potential

threats model are discussed in detail.

Das et al. [10] 2018 The authors in this paper investigate the security and threat model in IoT applications. The paper mentioned

some of the issues in IoT systems like authentication, trust management, and access control. Some solution

approach was also addressed.

Di Martino et al. [11] 2018 This paper reviewed the different standardized architecture of IoT systems and the current solution approach in

terms of Security and Interoperability are explained.

Hassija et al. [12] 2019 The authors of this paper reviewed the security and threat in IoT applications. The different solution approach

using machine learning, fog computing, edge computing, and Blockchain was proposed.

Proposed paper 2020 The authors in this paper initially identified the necessary Infrastructure, Protocol, Application of the IoT

system. Then security issue is identified in the IoT model. Some emerging technique which can be used to solve

the security issues in IoT is identified. After a rigorous survey, the authors found that machine learning,

Blockchain, and Artificial intelligence are the current solution approach to solve the Security issue in IoT.

are some of the open challenges in various IoT applications [1] . The IoT is one of the most emerging technologies in the last

decade and its uses in numerous applications area. Security and privacy are still challenges in many applications area. Some

research work addressing security and privacy issue in IoT is already done. But as the new technology comes, which can ad-

dress so of the security issue in IoT. So in this work, authors have identified three leading technologies like ML, Blockchain,

and AI, which address different security issues.

1.1. Objective and contribution

The main objective of this survey is to find out the security and privacy challenges that exist in IoT applications. The

authors also identified some emerging technology that can address security issues present in the system. Here the main

goal is to find the research challenges and corresponding solution approach in IoT security.

The following are the contribution of the paper:

• The paper explained the IoT architecture and its enabling technology with challenges. • The security issues in the IoT system are identified as in-depth layer-wise. • An extensive survey on similar technologies like machine learning, artificial intelligence, and Blockchain technology inte-

gration with IoT security are performed.

• The research challenges and corresponding solution approach with emerging technology (ML, AI, Blockchain) are alsoexplained.

1.2. Paper organization

The rest of the paper organized as in Section 2 related work of security and privacy issues of IoT are identified, and

comparison was also made. The IoT architecture details and associated technology are described in Section 3 . The security

issues are explained in Section 4 . The different security issues address in IoT applications using Machine Learning, Artificial

intelligence, and Blockchain technology are explained in detail in Sections 5 –7 sequentially. An analysis of the entire survey

and future challenges are summarized in Section 8 . The paper concludes with a summary of the work done in Section 9 .

2. Related work

The authors explain the underlying system architecture and security issues in paper [2] . Previously some works related

to a security issue in IoT applications, infrastructure are already done. In Table 1 , a summary of some of the survey works

is mentioned. Although several works already exist in this regard from different perspectives, for implementation purposes,

there is no such study done. So in this survey, authors have identified the recent emerging technology (ML, AI, Blockchain),

which can be addressed security issues in IoT. Some of the work integration with recent technology and IoT has already

B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227 3

Fig. 1. Internet of things infrastructure.

been done. In this survey, the authors tried to give the details about the insight of that technology how it will solve security

challenges in IoT. This will helps the reader to understand the IoT infrastructure creation and implementing it securely.

3. Internet of things (IoT) infrastructure,protocol, application

Internet of Things (IoT) has lots of potentials to apply in different real-time applications. It integrates sensors, smart de-

vices, radiofrequency identification (RFID), and the Internet to build an intelligent system. As per Goldman Sachs estimated

28 billion smart things would be connected to a different network by 2020. The growth of IoT in the last decade in such

a way that it incorporates everything from sensors to cloud computing intermediate with fog/edge computing. The IoT has

different types of a network like a distributed, ubiquitous, grid, and vehicular. The applications of IoT made a huge impact

in day to day life like sensors deploy in the patient body to monitoring in critical condition, monitoring gas leakage in smart

kitchen, agriculture field, smart car parking, smart transportation, tracking goods details in supply chain system using sen-

sors in the vehicle. The sensors are resource constraint devices connected through wired or wirelessly across heterogeneous

networks. The IoT networks are possessed different security, privacy, and vulnerable to the attacker.

3.1. IoT infrastructure

IoT application consists of different smart things that collect, process, compute and communicate with other smart things.

IoT has three layers physical, network, and application layer. Recently industries are developed many things which are em-

bedded with intelligent things. As shown in Fig. 1 IoT infrastructure consists of not only sensors, but it also integrates with

some emerging technology. The IoT application is based on either IoT-Cloud or IoT-Fog-Cloud. The security issue like data

privacy [13] , machine to machine communication [14] , real-time monitoring [15] and IoT testbed [16] are need to be ad-

dressed for efficient IoT applications. The architecture of IoT may be centralized, distributed, decentralized structure. In IoT

application processing and computing in real-time is one of the most challenging issues. Cloud computing provides more

storage and assures security to the data. But recently, most of the real-time monitoring IoT application demand processing

and computing in the edge of the network. So that quick action can be taken like monitoring the health condition of the

serious patient, fire detection. When processing and computing are done on the edge of the network using fog devices, it

becomes more vulnerable to the attacker as their devices are lightweight device traditional security is not applicable. During

analytic data, a technique like a machine learning is recently used to make the IoT system more intelligent and independent

to make a decision. The different smart devices are connected to make an application using some standard protocols. The

security issue exists in IoT infrastructure, which needs to be addressed to build trust among end-users and make the system

temper-proof. The data interoperability [17] in the IoT system works using an intelligent algorithm.

3.2. Standard protocol

The basic IoT architecture is a four layer network. Each of these layer consists of some standard protocol as shown in

Table 2 .

3.2.1. MQTT

MQTT stands for transportation of MQ Telemetry. It is a straightforward and lightweight messaging protocol for pub-

lishing / subscribe, designed for restricted devices and low bandwidth, high latency, or unreliable networks. The design

principles are to minimize the requirements for network bandwidth and device resources while also trying to ensure reli-

ability and some degree of delivery assurance. These principles also result in making the protocol ideal for the emerging

world of low end connected devices “machine-to-machine” (M2 M) or “Internet of Things.”

4 B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227

Table 2

Protocols & attacks on IoT layers.

Protocols & possible attacks in IoT layers

Layer Protocol name Possible security attack

Application MQTT, CoAP, REST, AMQP Repudiation Attack, DDoS Attack, HTTP Flood Attack, SQL Injection

Attack, Cross-Site Scripting, Parameter Tampering, Slowloris Attack

Transport TCP, UDP, DCCP, SCTP, RSVP, QUIC SYN Flood, Smruf Attack,Injection Attack, Mitnick Attack, Opt-ack Attack

Network CLNS, DDP, EIGRP, ICMP, IGMP,

IPsec, IPv4, IPv6, OSPF, RIM

IP Address Spoofing, DoS Attack, Black Hole Attack, Worm Hole Attack,

Byzantine Attack, Resource Consumption Attack.

Pysical DSL, ISDN, IDA, USB, Bluetooth,

CAN, Ethernet

Access Control Attack, Physical damage 0r Destruction, Disconnection of

Physical Links

3.2.2. CoAP

Constrained Application Protocol (CoAP), as defined in RFC 7252, is a specialized Internet Application Protocol for re-

stricted devices. It allows those restricted devices called “nodes” to use similar protocols to communicate with the broader

Internet. CoAP is designed to be used by devices on the same network.

3.2.3. REST

REST stands for State Transfer Member. REST is an architecture based on web standards and uses the HTTP protocol.

It revolves around resources where each element is a resource, and a resource is accessed using standard HTTP methods

through a specific interface. Roy Fielding introduced REST in 20 0 0. A REST server offers access to resources in REST archi-

tecture, and REST user accesses and modifies resources. Here, URIs / global IDs classify each asset. REST uses a variety of

representations to describe a resource such as text, JSON, XML.

3.2.4. AMQP

An open standard for transferring business messages between applications or organizations is the Advanced Message

Queuing Protocol (AMQP). It connects systems, feeds business processes with the information they need, and transmits the

instructions that achieve their goals reliably forward.

3.2.5. TCP

Transmission Control Protocol (TCP) is a connection-oriented communications protocol that provides the facility to ex-

change messages in a network between computer devices.

3.2.6. UDP

A Transport Layer protocol is the User Datagram Protocol (UDP). UDP is part of the Internet Protocol suite, known as UDP

/ IP. Like TCP, this protocol is unstable and unconnected. There is thus no need to create a link before transferring data.

3.2.7. DCCP

DCCP provides a way for congestion-control mechanisms to be accessed without having to implement them at the ap-

plication layer. It allows flow-based semiconducting, as in the Transmission Control Protocol (TCP), but does not provide

reliable delivery on-order. Sequenced transmission across multiple streams is not possible in DCCP, as in the Stream Control

Transmission Protocol (SCTP). A DCCP link requires both the network acknowledgment and data traffic. Acknowledgments

notify a sender that their packets have arrived and whether they have been labeled with an Explicit Notification of Conges-

tion (ECN).

3.2.8. SCTP

The Stream Control Transmission Protocol (SCTP) is a computer networking communication protocol that operates at the

transportation layer and serves a similar role to the popular TCP and UDP protocols. It is defined in RFC 4960 by IETF.SCTP

incorporates some of the features of both UDP and TCP: it is message-oriented like UDP and ensures secure, in-sequence

congestion-controlled transmission of messages like TCP. It differs from those protocols by providing multi-homing and

redundant paths to increase resilience and reliability.

3.2.9. RSVP

The Resource Reservation Protocol (RSVP) is a transport layer [1] protocol designed to use the distributed infrastructure

model to reserve resources across a network. RSVP works over an IPv4 or IPv6 and sets up resource reservations for multi-

cast or unicast data flows, initiated by the recipient. It does not transmit data from applications but is similar to a control

protocol, such as the Internet Control Message Protocol (ICMP) or the Internet Group Management Protocol (IGMP). RSVP is

set out in RFC 2205.

B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227 5

3.2.10. QUIC

QUIC (pronounced’ quick’) is a general-purpose network layer protocol initially designed by Google’s Jim Roskind, intro-

duced and deployed in 2012, publicly announced in 2013 as an extended experiment and defined by the IETF. While still an

Internet-Draft, more than half of all Chrome web browser connections to Google’s servers use QUIC.[citation needed] Most

other web browsers don’t follow the protocol.

3.2.11. CLNS

Connectionless mode Network Service (CLNS) or simply Connectionless Network Service is an OSI Network Layer data-

gram service that does not require a circuit to be set up before data is transmitted, and routes messages to their destinations

independently of any other messages. CLNS is not an Internet service but offers f eatures similar to those offered by the In-

ternet Protocol (IP) and User Datagram Protocol (UDP) in an OSI Network environment.

3.2.12. DDP

Distributed Data Protocol (or DDP) is a client-server protocol designed to query and update a server-side database and

to synchronize such updates between clients. It uses a messaging pattern for publish-subscribe. The Meteor JavaScript ap-

plication was developed for use.

3.2.13. ICMP

Connectionless-mode Network Service (CLNS) or simply Connectionless Network Service is an OSI Network Layer data-

gram service that does not allow a circuit to be set up before data is transmitted and routes messages to their destinations

independently of any other messages. As such, it is a best-effort rather than a “reliable” delivery service. CLNS is not an

Internet service but offers f eatures similar to those offered by the Internet Protocol (IP) and User Datagram Protocol (UDP)

in an OSI Network environment.

3.2.14. DSI

Digital Serial Interface (DSI) is a protocol for regulating lighting (initially electrical ballast) in buildings. It is based on

Manchester-coded 8-bit protocol, 1200 baud data rate, 1 start bit, 8 data bits (dim value), 4 stop bits, and is the basis

for the more advanced Digital Addressable Lighting Interface (DALI) protocol. The technology uses a single byte (0–255 or

0x00-0xFF) to communicate the lighting level. DSI was the first use of digital communication to control lighting and was

the precursor to DALI.

3.2.15. ISDN

Integrated Services Digital Network (ISDN) is a set of communication standards for simultaneous digital transmission of

voice, video, data, and other network services over the traditional circuits of the public switched telephone network. The

key feature of ISDN is that it integrates speech and data on the same lines, adding features that were not available in the

classic telephone system. In the emergency mode of IoT devices, the ISDN facility can be useful.

3.3. Application

IoT applications are nowadays developed in many fields. The development of many open-source platforms like Azure

IoT Suite, IBM Watson, Amazon Web Services (AWS), Oracle IoT, Kaa, Bevywise IoT platform used for industrial IoT, IoTIFY

cloud-based platform used to build scalable IoT applications. Most of the opensource platform is enabled with AI and ML

technology for intelligent processing and computing the information. The manufacture of smart devices that can read, pro-

cess, and computing the things makes the IoT as one of the emerging fields. There are many application areas where IoT

is used, as shown in Fig. 2 . In these eight different application fields, IoT has already made an impact on enhancing and

increasing the efficiency of the system.

3.3.1. Smart home

The IoT makes the traditional home system into an intelligent one. The refrigerator, smart television, security camera, gas

sensors, temperature sensor, light system all can sense the home environment, communicate and connect to the internet

through wired or wireless. Even the refrigerator can place an order to the registered retail shop and give notification to

the user. Due to the development of smart things, the living standard becomes more comfortable. In paper [18] , authors

design a smart home system based on IoT technology. Using technology like IoT and Fog computing home converted into an

intelligent home system where monitoring of the home can be done remotely as well as processing can be done instantly.

The authentication of devices is essential to prevent unwanted access to the IoT network. The authors in Satapathy et al.

[19] and Panda et al. [20] proposed different authentication schemes for a smart home network. Still, some security issues

[21] , are exist in IoT based smart home systems.

6 B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227

Fig. 2. Internet of things applications.

3.3.2. Smart hospital

Since the development of IoT patient monitoring in real-time is possible with the use of sensors and fog/edge computing,

the paper [22] , authors have proposed an IoT-cloud based framework for data collection in the healthcare system. Similarly,

in Moosavi et al. [23] , authors performed the authentication and authorization of the smart devices in the healthcare system.

In the healthcare system, privacy is one of the main issues, so proper security and privacy protocol need to be developed to

secure the system.

3.3.3. Smart city

The ever-growing city has lots of problems like traffic management, waste management, waste management, and en-

vironmental management. The city needs a solution to monitor and control the problem exist. In papers [24,25] , authors

explained the challenges that exist in implementing smart cities and done a survey in detail about how IoT can solve an

existing problem. Using IoT and associated technology, a smart city can be developed to enhance the living standard of the

city, maintaining the security and privacy issue of the citizen.

3.3.4. Smart transportation

In recent times traffic is one of the major problems in a city. The intelligent transportation system is the need of the

hour. The IoT enables vehicles can collect information from the roadside unit and process to get the details about journey

path, time, and traffic details. Some of the research work [26,27] addressed the smart transportation issue using IoT. In

paper [28] , the authors proposed the IoT-ITS system for the transportation system. The authors in Dey et al. [29] proposed

a “Magtrack” to detect condition of the road surface using in-build mobile sensors and machine learning concepts.

3.3.5. Smart grid

The smart grid is one of the application areas of IoT, where a grid system can be made automation using IoT. The elec-

tric power generation and distribution among consumers can be monitor in real-time. The cybersecurity solution approach

[30] is explained in detail. The architecture of the IoT-Cloud based system proposed by the authors in paper [31] . The effi-

cient, economical and distribution can be improved using the IoT technology in the smart grid system.

B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227 7

Table 3

The different security attacks in IoT.

Different attacks cases and relevant research papers

Attacks type Paper

IoT

Attacks

Jamming attacks [43]

DoS attacks [44]

Intrusion detection System [45]

Malicious node [46]

Power analysis attack [47,48]

Internal attacks [49]

Access control [50]

Wormhole attack [51]

Side channel security [52]

Distributed Dos [53]

Man in the Middle attack [54]

Active attacks [55]

Routing attacks [56]

Sybil attacks [57,58]

Deceptive attack [59]

Spoofing [60]

Buffer overflow attack [61]

Impersonation attack [62]

3.3.6. Supply chain system

The IoT smart devices, once used in a supply chain management system, can fundamentally change the traditional way

to monitor the transport system. By using the IoT technique, the material is easily located, their current condition, packing

details, and it is easy to track how goods are a move through the supply chain. It increases to maintain the demand-supply

of good, easy to monitor the material movement, real-time tracking, efficient storage, energy efficient [32] , and distribution.

The authors in Li et al. [33] , explained how tracking and tracing could be done in real-time using the IoT system. Similarly,

in paper [34,35] authors, discussed the IoT based architecture and risk management in the supply chain system. In paper

[34] , authors have proposed artificial intelligent integration with IoT for the retail shop supply chain system.

3.3.7. Smart retails

The retail sector also using IoT services along with artificial intelligent [36] to enhance productivity, improve store opera-

tion, and to take the decision in real-time to manage the inventory system.

3.3.8. Agriculture

Agriculture is one of the promising application areas in IoT. In a smart agriculture system by deploying the sensors to

monitor the soil quality, water management, crop growing condition, etc. which improve the farming efficiency by reducing

time and cost. In real-time, a user can monitor all details from the remote locations. In paper [37,38] authors proposed

smart irrigation using machine learning and IoT to enhance farming. similarly, in paper [39,40] , smart water management

and weather conditions in the agriculture system are explained in detail. Likewise, in paper [41,42] , smart agriculture system

integration with IoT technologies is explained in detail. As some of the work already done in the field of agriculture, still

some security issues exist like mobility, infrastructure, and secure processing of the collected data.

4. Security attacks in internet of things

In Table 3 some common Internet of Things attacks in the different layer is shown along with the current research work

done on the corresponding attacks types.

Jamming attack is a subset of DoS attacks where the attacker tries to affect the communication channel in paper [43] au-

thors also explained the details about the jamming attacks.

Dos attack is one of the common attack used in IoT applications. Most of the IoT devices are a low-end device which

is vulnerable to the attacker. The attacker gets under the data traffic stream through device connection or infrastructure.

Denial of service (DoS) attacks, consists of a huge volume of network packets, targeting the node present in the application

causes service interrupt in real-time [44] .

Intrusion detection system(IDS) is the process in which network traffic is control by the attacker. There are some types

of IDS attacks, like misuse detection, anomaly detection, Host-based IDS, and Network-based IDS. The authors in paper

[45] described the IDS attacks in IoT network.

Malicious node attack is possible in a distributed IoT network due to the heterogeneous nature of the smart devices.

The identify the genius node or fake node in the network is a challenging one. In paper [46] authors proposed a perception

and K-mean to build the trust among the node and detect the malicious node.

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Fig. 3. The basic machine learning based model integration with IoT.

Power analysis attack and its corresponding solution approach are explained in papers [47,48] . This attack is mainly

made to gain the computational power of the nodes so that the basic cryptographic algorithm is not possible to execute. In

an IoT network, privacy also needs to be maintained to build trust among the node.

Internal attack in paper [49] and Access control attack in paper [50] are discussed in details. Wormhole attack is taken

place at the 6LoWPAN layer, where the attacker makes a tunnel between two nodes that are connected [51] .

The Side channel security attack in cloud-based IoT application along with the security challenges are explained in paper

[52] . Similarly, Distributed Dos attack is the process where the server is unreachable so that smart nodes in the network

can not get the services it desires to get [53] .

Man-in-the-middle attack, where the attacker relays the message or change the message during the transmission in

the insecure channel, explained in Li et al. [54] IoT-Fog network. Active attacks is explained in Zhang et al. [55] and its

corresponding solution in the physical layer of the IoT network. There are different types of active attacks possible in IoT,

where attackers make changes in the target node. The authors in Raoof et al. [56] explained the Routing attacks in routing

protocol lossy network based on IoT application. The Sybil attack is one most common types of attack in IoT. The authors in

Zhang et al. [57] and Mishra et al. [58] study the phases of Sybil attacks and their countermeasures in the internet of things.

The Deceptive attack in La et al. [59] and Spoofing attack in Zhang et al. [60] authors have addressed the corresponding

attacks and their security analysis in the internet of things applications. The Buffer overflow attacks is the process of

writing the program in a block of memory where the memory space is insufficient. The A IoT network, when nodes execute

the different programs in the deices for processing or computation purpose attackers, can capture that and perform memory

overflow attack. The detecting buffer overflow attack and providing appropriate security design in explained in Xu et al. [61] .

In a large IoT network where heterogeneous devices are connected and communicate with each other. The trust is one of

the major issues in the network. The Impersonation [62] attacks where a fake node behaves like a genius node in the

network and tries to gain the information from other nodes. This is one of the most challenging issues in IoT applications

where smart devices are heterogeneous and low-end devices.

5. Security issue address using machine learning

The machine learning is a technique to perform computational intelligently. The model needs to design and test using

different learning methods. Figs. 3 and 4 describe the basic principle of machine learning functionality and integration

with IoT applications. As discussed in Section 3.3 application of the Internet of Things is many. Some of the application

requirement is decision should be taken before the actual event occurs. For example, predicting the fire in a kitchen or any

industrial area and alarm the sound to prevent the fire. This could be possible if machine learning technologies are used in

IoT applications. Also, it needs to address the security issue present in the IoT system ta make the system tamper-proof. An

efficient framework [63] is required to process and compute the huge data collection using a machine learning technique.

In paper [64] , authors review the security issue associated when applying machine learning in a smart grid application. In

paper [65,66] authors address the intrusion detection in IoT application.

In Tables 4–6 the details machine learning integration with IoT security issue related work are explained.

6. Security issue address using artificial intelligence

The innovation of smart devices having sensing and acting capability makes the IoT system usability in widely. As the

numbers of devices are connected to the network are huge, which generate a large volume of data. To process and perform

computation is a challenging task in an IoT environment. So Artificial intelligence comes as a rescue along with some other

emerging technology to address the security issue in IoT. As shown in Fig. 5 , IoT and AI can combine to improve the analysis

of the system, improve operational efficiency, and improve the accuracy rate. The authors in Ghosh et al. [82] explained that

B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227 9

Fig. 4. In-depth model of machine learning in IoT application.

Table 4

Machine learning apply on different IoT security.

Reference Years Contribution

[63] 2018 The authors proposed a framework to monitor security in Mobile IoT using Big data processing and ML.

[64] 2019 Application of ML methods on big data generated in the smart grid to extract useful information and to detect

and protect the data from cyber-security threats.

[65] 2019 Review on Network Intrusion Detection System (NIDS) in an IoT environment using ML algorithms.

[66] 2019 The authors proposed, a 3-layer Intrusion Detection System (IDS) using a supervised learning method of ML to

distinguish between malicious or benign network activity and to detect network-based cyber-attacks such as

DoS, MITM/Spoofing, Replay, and Reconnaissance and also to detect a multi-stage attack on IoT networks.

[67] 2017 Proposed a physical-layer authentication (PLA) scheme based on extreme learning machine (ELM)with a

2-dimensional measure space to ameliorate spoofing detection accuracy.

[68] 2017 Proposed an ML-based malicious app detection tool that uses naive Bayesian, J48 decision tree as a classifier

model to detect malicious applications instantaneously in Android devices.

[69] 2018 Implemented an autonomous and adaptive detection mechanisms using ML and software-defined networking

(SDN) for their IoT security framework to deal with the problem of erratic behavior and heterogeneity of IoT

systems.

[70] 2018 Presented the different ML methods that can be applied to the data generated in the IoT system based

environments like Smart cities to pull out the higher-level information if it.

Fig. 5. The common functionality in IoT and artificial intelligence.

10 B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227

Table 5

Machine learning apply on different IoT security.

Reference Years Contribution

[71] 2018 The proposed a reliable, scalable, and robust Swarm Intelligence (SI)-based IoT system to

overcome the problem of dynamic and heterogeneity behavior of IoT systems.

[72] 2018 The authors presented a Dense Random Neural Networks (RNN) based deep-learning technique

by analyzing the traffic flow exchange in IoT gateways. To detect the network attacks online

such as TCP SYN attack, which is a variety of denial-of-service (DoS) attacks.

[73] 2018 The authors proposed a robust real-time distributed fog-based attack detection framework for

IoT, which relies on a fog computing paradigm and a newly proposed ELM-based

Semi-supervised Fuzzy C-Means (ESFCM). Extreme Learning Machine (ELM) algorithm provides

good generalization performance at a faster detection rate, and semi-supervised Fuzzy C-Means

method handles the labeled data issue in IoT.

[74] 2018 The proposed a new darknet analysis method to find the traffic patterns of a specific scanning

attack i.e., TCP SYN packets due to the majority of darknet packets using the association rule

learning.

[75] 2018 The authors proposed a novel algorithm for quantifiable intelligent trust assessment model to

overcome the issue of potential discrimination. The data analytics is done over delicate

information such as locations, interests, and activities, using the SVM model of ML. This process

generates exact and inherent trust values for probable actors. It helps in determining whether

an incoming interaction is trustworthy or not, based on several trusts features corresponding to

an IoT environment.

Table 6

Machine learning apply on different IoT security.

Reference Years Contribution

[76] 2018 The authors proposed a system for real-time monitoring of the health parameters to detect

bombs nearby and to predict the warzone environment. Using various sensors to collect the

data, network infrastructure like LoRaWAN and ZigBee to transmit those data to the cloud and

K-Means Clustering machine learning algorithm to analyze the data.

[77] 2018 Proposed a Deep Learning (DL) based secure framework for Intrusion detection system using

Restricted Boltzmann Machines (RBM) for SDN based IoT.

[78] 2019 The authors proposed a robust prediction model for real-life mobile phone data of individual

users using a rule-based machine learning classification technique, i.e., decision tree on the

noise-free quality dataset. Naive Bayes classifier and Laplace estimator are used to improving

the prediction accuracy of the model by removing the noisy instances in the data.

[79] 2019 Proposed an ML-based anomaly detection system which can detect cyber-attacks like backdoor,

command injection, and Structured Query Language (SQL) injection attacks in the Industrial

Internet of Things (IIoT) devices.

[80] 2019 Authors compared the performances of various ML models such as Logistic Regression (LR),

Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural

Network (ANN) for predicting attacks like DoS, Data Type Probing, Malicious Control, Malicious

Operation, Scan, Spying and Wrong Setup, and anomalies on the IoT systems accurately.

[81] 2019 Presented the preliminary work of neural network (NN)-based specific emitter identification

(SEI) on IoT devices using raw in-phase and quadrature (IQ) streams, with protocols to secure

IoT networks by providing an extra layer of security and trust.

AI could help IoT huge volume, unstructured data, heterogeneous data to compute in real-time, which makes the system

realistic. The authors propose the large margin cosine estimation (LMCE) technique in this paper [83] to detect the adversary

in IoT enable environments. The malware detection work in the IoT system using

AI is addressed in paper [84] . Similarly, in paper [85] , the authors proposed a model using Blockchain and AI in IoT

architecture to make the system tamper-proof. In Fig. 6 , integration of IoT and AI with some basic functionality are shown.

The combination of AI and IoT some work is already done by the researcher addressing that AI can be a driving force to

make the IoT system more improve in decision making and doing computation.The authors apply a master attack in IoT

enables smart city application based on AI [86] . Similarly, in Zou et al. [87] , the authors explained Edge and fog computing

for IoT applications.

7. Security issue address using blockchain technology

Blockchain technology is a decentralized/distributed network where each is connected to others in some way. The mes-

sage is broadcast in the Blockchain network. As shown in Fig. 7 distributed architecture based on blockchain techniques in

IoT application. A block consists of lots of valid transaction and its associated attributes. The smart contract [88] are self

executable program used to implement the business logic in the network. The Blockchain network uses different consen-

sus algorithm [89] to meet the consensus among the nodes. The details Blockchain architecture and application areas are

explained in paper [90] by the authors. The authors in paper [91,92] described the mechanism and related work on IoT

security along with Blockchain as a solution approach.The authors have proposed a secure framework for the internet of

B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227 11

Fig. 6. Integration of IoT and AI and their basic properties.

Fig. 7. Blockchain based different IoT applications.

things applications based on a distributed Blockchain system in Satapathy et al. [93] . The use of Blockchain technology in

IoT is briefly given in paper [94] by the authors. The many IoT security challenges and corresponding Blockchain solutions,

along with the implementation challenges, are review by the authors in paper [95] . In Tables 7–10 details review regarding

blockchain and IoT security issue are described.

8. Analysis of the survey and research challenges

The Internet of Things (IoT) in recent time attract lots of attention to the research community as well as an industry

sector. The IoT devices are manufactures in large number which already cross the total world population. These smart de-

vices are connected to different applications for capturing information from the environment. The IoT devices are resource

constraint devices, so devices are vulnerable to the attacker. Security and privacy issues are important for IoT applications.

So this survey is carried out in-depth to identified security and privacy issues that exist in the IoT system up to March

2020. The solution approaches of these security and privacy issues solved by some emerging technologies are also discussed.

12 B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227

Table 7

Blockchain technology work on IoT security.

Reference Years Contribution

[96] 2017 The authors in this paper proposed a distributed Blockchain-based model. The proposed system

one miner is used to control the communication within the smart home as well as an external

source. The framework is secure against fundamental security goals.

[97] 2018 The authors evaluated the feasibility of using blockchain nodes on IoT devices.

[98] 2018 The authors proposed a distributed ledger-based blockchain (DL-BC) technology to address

security and privacy issues in IoT, such as spoofing, false authentication.

[99] 2018 Proposed distributed intelligence that performs instance decision making and reduces

unnecessary data transfer to the cloud, addressing various security challenges in the IoT

paradigm.

[91] 2018 The authors proposed a blockchain-based compromised firmware detection and self-healing

approach that can be deployed in an IoT environment for secure datasets sharing.

[100] 2018 The authors proposed a blockchain-based secure scheme to resolve the issue of time

announcements in IoT.

[101] 2018 Proposed the Named Data Networking (NDN) of Things architecture and the blockchain solution

to deal with the security attacks in this.

[102] [103] 2018 The authors proposed a blockchain-based high-level security management scheme for various

IoT devices.

[104] 2020 The authors explored the major benefits and design challenges for integration of blockchain

technologies for IoT applications.

Table 8

Blockchain technology work on IoT security.

Reference Years Contribution

[105] 2019 The authors proposed device classification methods by applying machine learning algorithms on

the data stored in the blockchain network which in turn helps to enhance the security of IoT

environment by detecting unauthorised devices.

[106] 2019 The authors proposed a trust management framework for providing secure and trustworthy

access control and also detecting and removing malicious and compromised nodes in a

decentralized IoT system.

[107] 2019 The authors proposed a Secure Private Blockchain-based framework (SPB) using which

negotiations can be done among the energy prosumers over the energy price and trade energy

in a distributed manner for a smart grid IoT application.

[108] 2018 The authors proposed a permissioned blockchain based framework to find provenance of supply

chain products.

[109] 2019 The authors proposed a three-layered trust management framework – TrustChain, based on

consortium blockchain for tracking the interactions among supply chain participants and based

on these interactions it dynamically assign trust and reputation scores.

[110] 2018 The authors proposed a noble blockchain-based framework for providing a private and secure

communication model for smart vehicles so that they can trust the data they receive are

generated by a trusted node.

Table 9

Blockchain technology work on IoT security.

Reference Years Contribution

[111] 2018 Authors proposed a Permissioned blockchain architecture to handle the most expensive

computation in pairing-based cryptographic protocols i.e., secure outsourcing of bilinear

pairings (SOBP).

[112] 2019 The authors proposed a credit-based proof-of-work (PoW) mechanism in blockchain for IoT

devices, which can guarantee system security and transaction efficiency.

[113] 2019 The authors surveyed some of the promising applications that are being implemented using

blockchain and also outlined solutions to overcome numerous challenges.

[114] 2019 Proposed an anti-counterfeiting approach for IoT devices exploiting characteristics of memory

chips to derive a cryptographic secret combined with a blockchain for trusted and reliable

verification of device identities.

[115] 2019 Proposed a blockchain-based searchable encryption for electronic health records (EHRs) sharing

scheme by using smart contracts to perform a reliable and confidential search.

[116] 2019 The authors proposed a blockchain-based privacy-preserving software update protocol to

perform secure and reliable updates with an incentive mechanism without hampering the

privacy of involved users.

[117] 2019 The authors proposed a blockchain-based energy trading scheme for secure energy trading in

the Intelligent Transportation System (ITS) by utilizing energy coins.

B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227 13

Table 10

Related work blockchain and IoT security.

Focus area Reference Contribution

Privacy Perservation [118,119] [120,121] [122] In an IoT application, privacy is a significant concern for the end-users. The

blockchain-based encryption techniques are proposed by different authors to

solve the privacy preservation issue.

Authentication [123,124] [125,126] [127] [128] The device authentication is one of the important factors for secure

communication in the IoT network. The different methods, like mutual

authentication, PSO-AES, and distributed authentication, are used for IoT

device authentication using blockchain techniques.

Access Control [129,130] The management of devices accessibility in the IoT system is essential as

critical information is sense using different smart things. The attribute-based

access control and blockchain-based permission delegation access control

techniques are proposed by the researcher to manage the accessibility of the

vital information securely.

Scalability [131,132] The work has already been done to address the scalable issue in IoT network

using blockchain.

Information Share [133,134] [135,136] [137,138] [139,140] Information exchange in the IoT network is very important in real-time

monitoring of the environment. The blockchain-based secure information

share mechanism is integrated with the IoT system.

Trust Management [141,142] [143,144] [145] In many IoT applications, multiple nodes are required in the decision-making

process for better and efficient decisions. Some work has already been done

regarding trust management.

Initially, different research databases like ScienceDirect, IEEE Xplore, Inderscience, ACM Digital Library, DBLP, google

scholar, Springer are used to search 500 articles. The word used to search the articles are “Internet of Things”, “IoT”, “secu-

rity”, “privacy”, “machine learning”, “blockchain”, “artificial intelligence”.

The number of articles are reduces to number 250 after reading the abstract and title. Again duplicate or redundant

articles are remove. In the final stage 145 numbers of article are consider after reading the full text.

8.1. Summary of the review

In this survey, authors have work on the Security issues that exist in the IoT system. The purpose of this survey is to

identify the solution need to address the security issue. Security is one of the most challenging tasks and need to address

in IoT applications to be successful.

8.1.1. Critical analysis of machine learning

The machine learning technique is consists of supervised and unsupervised. The IoT application generates a huge volume

of information. Before data are computation is done, data are needed through the verification process to avoid any mali-

cious data or redundancy data. This survey, authors identified 29 numbers of articles that address the security issue of IoT

applications. Machine learning addresses the following security issues:

• Intrusion detection system. • Malware detection. • Anomaly detection. • Unauthorized IoT devices identification. • Distributed denial-of-service. • Jamming attack, Spoofing attack. • Authentication, Eavesdropping. • False data injection, Impersonation.

8.1.2. Critical analysis of blockchain technology

In this survey, Section 7 the details blockchain technology and corresponding research works are mention in Tables 7–10 .

The 58 numbers of an article are listed. The authors found blockchain is the most promising technology recently researchers

are working on to solve the security issue of IoT applications. The following security issues are address by the blockchain

technology:

• Identity verification. • Firmware detection and self healing. • Privacy preservation and Address space. • Data integrity and Secure communication. • Authentication and authorization. • Access control and Information Sharing. • Secure storage and computation.

• Trust Management.

14 B.K. Mohanta, D. Jena and U. Satapathy et al. / Internet of Things 11 (2020) 100227

8.1.3. Critical analysis of artificial intelligence

As per the survey done in this paper, authors found 6 numbers of papers address the security issue of IoT. In an ap-

plication like smart transportation and smart weather forecasting, the prediction is essential. The AI provides some of the

security issues like malware detection, privacy preservation, and authorization.

8.2. Research challenges

Some of the research challenges are underlined below:

1. As the huge number of IoT devices are connected, system throughput and consensus algorithm problems still exist.

2. Scalability issue of IoT needs to be consider when addressing security protocols.

3. Secure computation and processing are other areas that need to address.

4. The security protocol should be design in terms of light-weight to meet the resource constraint devices.

9. Conclusion

In this paper, the authors firstly study in-depth the various security challenges exist in IoT application. Secondly, the

authors have surveyed to address existing security challenges. From the survey, it was found that some research has already

been done in various technology like Machine learning, Artificial intelligence, and Blockchain technology, which are capable

of addressing the existing security issue. So in detail study has been made in three technology machine learning, artificial

intelligence and Blockchain technology, and their integration with IoT. Security is an important issue that needs to address.

In this survey, the authors outline the emerging technology like ML, AI, and Blockchain integrate with IoT to make the

system more secure. Some of the research challenges mention in the end.

Declaration of Competing Interest

The authors do not have conflict of interest with any one.

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