Security of IoT in 5G Cellular Networks: A Review of Current Status, Challenges and Future Directions

The Internet of Things (IoT) refers to a global network that integrates real life physical objects with the virtual world through the Internet for making intelligent decisions. In a pervasive computing environment, thousands of smart devices, that are constrained in storage, battery backup and computational capability, are connected with each other. In such an environment, cellular networks that are evolving from 4G to 5G, are set to play a crucial role. Distinctive features like high bandwidth, wider coverage, easy connectivity, in-built billing mechanism, interface for M2M communication, etc., makes 5G cellular network a perfect candidate to be adopted as a backbone network for the future IoT. However, due to resource constrained nature of the IoT devices, researchers have anticipated several security and privacy issues in IoT deployments over 5G cellular network. Off late, several schemes and protocols have been proposed to handle these issues. This paper performs a comprehensive review of such schemes and protocols proposed in recent times. Different open security issues, challenges and future research direction are also summarized in this review paper

Security in 5G-Enabled Internet of Things Communication: Issues: Challenges, and Future Research Roadmap

5G mobile communication systems promote the mobile network to not only interconnect people, but also interconnect and control the machine and other devices. 5G-enabled Internet of Things (IoT) communication environment supports a wide-variety of applications, such as remote surgery, self-driving car, virtual reality, flying IoT drones, security and surveillance and many more. These applications help and assist the routine works of the community. In such communication environment, all the devices and users communicate through the Internet. Therefore, this communication agonizes from different types of security and privacy issues. It is also vulnerable to different types of possible attacks (for example, replay, impersonation, password reckoning, physical device stealing, session key computation, privileged-insider, malware, man-in-the-middle, malicious routing, and so on). It is then very crucial to protect the infrastructure of 5G-enabled IoT communication environment against these attacks. This necessitates the researchers working in this domain to propose various types of security protocols under different types of categories, like key management, user authentication/device authentication, access control/user access control and intrusion detection. In this survey paper, the details of various system models (i.e., network model and threat model) required for 5G-enabled IoT communication environment are provided. The details of security requirements and attacks possible in this communication environment are further added. The different types of security protocols are also provided. The analysis and comparison of the existing security protocols in 5G-enabled IoT communication environment are conducted. Some of the future research challenges and directions in the security of 5G-enabled IoT environment are displayed. The motivation of this work is to bring the details of different types of security protocols in 5G-enabled IoT under one roof so that the future researchers will be benefited with the conducted work

Secure Key Exchange Against Man-in-the-Middle Attack: Modified Diffie-Hellman Protocol

One of the most famous key exchange protocols is Diffie-Hellman Protocol (DHP) which is a widely used technique on which key exchange systems around the world depend. This protocol is simple and uncomplicated, and its robustness is based on the Discrete Logarithm Problem (DLP). Despite this, he is considered weak against the man-in-the-middle attack. This article presents a completely different version of the DHP protocol. The proposed version is based on two verification stages. In the first step, we check if the pseudo-random value α that Alice sends to Bob has been manipulated! In the second step, we make sure that the random value β that Bob sends to Alice is not manipulated. The man-in-the-middle attacker Eve can impersonate neither Alice nor Bob, manipulate their exchanged values, or discover the secret encryption key

A user-centric privacy-preserving authentication protocol for IoT-AmI environments

Ambient Intelligence (AmI) in Internet of Things (IoT) has empowered healthcare professionals to monitor, diagnose, and treat patients remotely. Besides, the AmI-IoT has improved patient engagement and gratification as doctors’ interactions have become more comfortable and efficient. However, the benefits of the AmI-IoT-based healthcare applications are not availed entirely due to the adversarial threats. IoT networks are prone to cyber attacks due to vulnerable wireless mediums and the absentia of lightweight and robust security protocols. This paper introduces computationally-inexpensive privacy-assuring authentication protocol for AmI-IoT healthcare applications. The use of blockchain & fog computing in the protocol guarantees unforgeability, non-repudiation, transparency, low latency, and efficient bandwidth utilization. The protocol uses physically unclonable functions (PUF), biometrics, and Ethereum powered smart contracts to prevent replay, impersonation, and cloning attacks. Results prove the resource efficiency of the protocol as the smart contract incurs very minimal gas and transaction fees. The Scyther results validate the robustness of the proposed protocol against cyber-attacks. The protocol applies lightweight cryptography primitives (Hash, PUF) instead of conventional public-key cryptography and scalar multiplications. Consequently, the proposed protocol is better than centralized infrastructure-based authentication approaches

Blockchain-Enabled Authenticated Key Agreement Scheme for Mobile Vehicles-Assisted Precision Agricultural IoT Networks

Precision Farming Has a Positive Potential in the Agricultural Industry Regarding Water Conservation, Increased Productivity, Better Development of Rural Areas, and Increased Income. Blockchain Technology is a Better Alternative for Storing and Sharing Farm Data as It is Reliable, Transparent, Immutable, and Decentralized. Remote Monitoring of an Agricultural Field Requires Security Systems to Ensure that Any Sensitive Information is Exchanged Only among Authenticated Entities in the Network. to This End, We Design an Efficient Blockchain-Enabled Authenticated Key Agreement Scheme for Mobile Vehicles-Assisted Precision Agricultural Internet of Things (IoT) Networks Called AgroMobiBlock. the Limited Existing Work on Authentication in Agricultural Networks Shows Passive Usage of Blockchains with Very High Costs. AgroMobiBlock Proposes a Novel Idea using the Elliptic Curve Operations on an Active Hybrid Blockchain over Mobile Farming Vehicles with Low Computation and Communication Costs. Formal and Informal Security Analysis Along with the Formal Security Verification using the Automated Validation of Internet Security Protocols and Applications (AVISPA) Software Tool Have Shown the Robustness of AgroMobiBlock Against Man-In-The-Middle, Impersonation, Replay, Physical Capture, and Ephemeral Secret Leakage Attacks among Other Potential Attacks. the Blockchain-Based Simulation on Large-Scale Nodes Shows the Computational Time for an Increase in the Network and Block Sizes. Moreover, the Real-Time Testbed Experiments Have Been Performed to Show the Practical Usefulness of the Proposed Scheme

A Tutorial and Future Research for Building a Blockchain-Based Secure Communication Scheme for Internet of Intelligent Things

The Internet of Intelligent Things (IoIT) communication environment can be utilized in various types of applications (for example, intelligent battlefields, smart healthcare systems, the industrial internet, home automation, and many more). Communications that happen in such environments can have different types of security and privacy issues, which can be resolved through the utilization of blockchain. In this paper, we propose a tutorial that aims in desiging a generalized blockchain-based secure authentication key management scheme for the IoIT environment. Moreover, some issues with using blockchain for a communication environment are discussed as future research directions. The details of different types of blockchain are also provided. Some of the widely-accepted consensus algorithms are then discussed. Next, we discuss different types of applications in blockchain-based IoIT communication environments. The details of the associated system models are provided, such as, the network and attack models for the blockchain-based IoIT communication environment, which are helpful in designing a security protocol for such an environment. A practical demonstration of the proposed generalized scheme is provided in order to measure the impact of the scheme on the performance of the essential parameters. Finally, some of the future research challenges in the blockchain-based IoIT communication environment are highlighted, which will also be helpful to the researchers

A Secure Integrated Framework for Fog-Assisted Internet of Things Systems

Fog-Assisted Internet of Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this paper proposes a framework featuring attribute-based access control and trust-based behavioural monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities’ performance using a trust model based on a set of QoS and network communication features. Subsequently, trust is embedded as an attribute within SC’s access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely DoS, DDoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework’s design principles. The framework is evaluated on a Raspberry Pi 3 Model B to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices

LDAKM-EIoT: Lightweight Device Authentication and Key Management Mechanism for Edge-Based IoT Deployment

In recent years, edge computing has emerged as a new concept in the computing paradigm that empowers several future technologies, such as 5G, vehicle-to-vehicle communications, and the Internet of Things (IoT), by providing cloud computing facilities, as well as services to the end users. However, open communication among the entities in an edge based IoT environment makes it vulnerable to various potential attacks that are executed by an adversary. Device authentication is one of the prominent techniques in security that permits an IoT device to authenticate mutually with a cloud server with the help of an edge node. If authentication is successful, they establish a session key between them for secure communication. To achieve this goal, a novel device authentication and key management mechanism for the edge based IoT environment, called the lightweight authentication and key management scheme for the edge based IoT environment (LDAKM-EIoT), was designed. The detailed security analysis and formal security verification conducted by the widely used Automated Validation of Internet Security Protocols and Applications (AVISPA) tool prove that the proposed LDAKM-EIoT is secure against several attack vectors that exist in the infrastructure of the edge based IoT environment. The elaborated comparative analysis of the proposed LDAKM-EIoT and different closely related schemes provides evidence that LDAKM-EIoT is more secure with less communication and computation costs. Finally, the network performance parameters are calculated and analyzed using the NS2 simulation to demonstrate the practical facets of the proposed LDAKM-EIoT

A Comprehensive Survey on the Cooperation of Fog Computing Paradigm-Based IoT Applications: Layered Architecture, Real-Time Security Issues, and Solutions

The Internet of Things (IoT) can enable seamless communication between millions of billions of objects. As IoT applications continue to grow, they face several challenges, including high latency, limited processing and storage capacity, and network failures. To address these stated challenges, the fog computing paradigm has been introduced, purpose is to integrate the cloud computing paradigm with IoT to bring the cloud resources closer to the IoT devices. Thus, it extends the computing, storage, and networking facilities toward the edge of the network. However, data processing and storage occur at the IoT devices themselves in the fog-based IoT network, eliminating the need to transmit the data to the cloud. Further, it also provides a faster response as compared to the cloud. Unfortunately, the characteristics of fog-based IoT networks arise traditional real-time security challenges, which may increase severe concern to the end-users. However, this paper aims to focus on fog-based IoT communication, targeting real-time security challenges. In this paper, we examine the layered architecture of fog-based IoT networks along working of IoT applications operating within the context of the fog computing paradigm. Moreover, we highlight real-time security challenges and explore several existing solutions proposed to tackle these challenges. In the end, we investigate the research challenges that need to be addressed and explore potential future research directions that should be followed by the research community.©2023 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed