scholarly journals Towards a Secure and Scalable IoT Infrastructure: A Pilot Deployment for a Smart Water Monitoring System

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 50
Author(s):  
Anthony Overmars ◽  
Sitalakshmi Venkatraman
Keyword(s):  
Monitoring System ◽  
Low Cost ◽  
Water Monitoring ◽  
Security And Privacy ◽  
Security Model ◽  
Smart Environment ◽  
Iot Security ◽  
Security Controls ◽  
Smart Water ◽  
Iot Devices

Recent growth in the Internet of Things (IoT) looks promising for realizing a smart environment of the future. However, concerns about the security of IoT devices are escalating as they are inherently constrained by limited resources, heterogeneity, and lack of standard security controls or protocols. Due to their inability to support state-of-the-art secure network protocols and defense mechanisms, standard security solutions are unsuitable for dynamic IoT environments that require large and smart IoT infrastructure deployments. At present, the IoT based smart environment deployments predominantly use cloud-centric approaches to enable continuous and on-demand data exchange that leads to further security and privacy risks. While standard security protocols, such as Virtual Private Networks (VPNs), have been explored for certain IoT environments recently, the implementation models reported have several variations and are not practically scalable for any dynamically scalable IoT deployment. This paper addresses current drawbacks in providing the required flexibility, interoperability, scalability, and low-cost practical viability of a secure IoT infrastructure. We propose an adaptive end-to-end security model that supports the defense requirements for a scalable IoT infrastructure. With low-cost embedded controllers, such as the Raspberry Pi, allowing for the convergence of more sophisticated networking protocols to be embedded at the IoT monitoring interface, we propose a scalable IoT security model integrating both the IoT devices and the controller as one embedded device. Our approach is unique, with a focus on the integration of a security protocol at the embedded interface. In addition, we demonstrate a prototype implementation of our IoT security model for a smart water monitoring system. We believe that our modest first step would instill future research interests in this direction.

Towards SWARM: A Smart Water Monitoring System

2020 ◽  
Author(s):  
Julian Kunze ◽  
Vincent Mayer ◽  
Lisa Thiergart ◽  
Saqib Javed ◽  
Patrick Scheppe ◽  
...  
Keyword(s):  
Monitoring System ◽  
Water Monitoring ◽  
Smart Water

scholarly journals Secure and Intelligent Energy Data Management Scheme for Smart IoT Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tianqi Zhou ◽  
Jian Shen ◽  
Sai Ji ◽  
Yongjun Ren ◽  
Leiming Yan
Keyword(s):  
Renewable Energy ◽  
Data Management ◽  
Energy Management ◽  
Energy Utilization ◽  
Security And Privacy ◽  
Smart Environment ◽  
Energy Data ◽  
Management Scheme ◽  
High Efficient ◽  
Iot Devices

The renewable energy plays an increasingly important role in many fields such as lighting, automobile, and electric power. In order to make full use of the renewable energy, various smart Internet of Thing (IoT) devices are deployed. However, in the field of energy management, the two-way mismatch between the demand and the supply of the renewable energy will greatly affect the efficiency of the renewable energy. In addition, the security threat of the energy data and the privacy leakage of the user may hinder the further development of smart IoT devices. Therefore, how to achieve consistency and balance between the demand and the renewable energy supply and how to guarantee the security and privacy of smart IoT devices become the key problems of the energy-efficient smart environment. In this paper, a secure and intelligent energy data management scheme for smart IoT devices is proposed. It is worth noting that, with the help of artificial intelligence (AI) technologies and secure cryptography primitives, the proposed scheme realizes high-efficient and secure energy utilization in a smart environment. Specifically, the proposed scheme aims at improving the efficiency of the energy utilization in the multidimensions of a smart environment. In order to realize the fine-grain energy management of smart IoT devices, strategies of three different dimensions are considered and realized in the proposed scheme. Moreover, technologies in AI are applied and integrated into the energy management scheme. The analysis shows that the proposed scheme can make full use of the renewable energy in smart IoT devices.

scholarly journals A Review on Evaluation and Configuration of Fault Injection Attack Instruments to Design Attack Resistant MCU-Based IoT Applications

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1153
Author(s):  
Zahra Kazemi ◽  
David Hely ◽  
Mahdi Fazeli ◽  
Vincent Beroulle
Keyword(s):  
Fault Injection ◽  
Low Cost ◽  
Security And Privacy ◽  
Production Cycle ◽  
Fault Attacks ◽  
Primary Means ◽  
Level Of Knowledge ◽  
Iot Devices ◽  
Fault Injection Attack ◽  
Evaluation Platform

The Internet-of-Things (IoT) has gained significant importance in all aspects of daily life, and there are many areas of application for it. Despite the rate of expansion and the development of infrastructure, such systems also bring new concerns and challenges. Security and privacy are at the top of the list and must be carefully considered by designers and manufacturers. Not only do the devices need to be protected against software and network-based attacks, but proper attention must also be paid to recently emerging hardware-based attacks. However, low-cost unit software developers are not always sufficiently aware of existing vulnerabilities due to these kinds of attacks. To tackle the issue, various platforms are proposed to enable rapid and easy evaluation against physical attacks. Fault attacks are the noticeable type of physical attacks, in which the normal and secure behavior of the targeted devices is liable to be jeopardized. Indeed, such attacks can cause serious malfunctions in the underlying applications. Various studies have been conducted in other research works related to the different aspects of fault injection. Two of the primary means of fault attacks are clock and voltage fault injection. These attacks can be performed with a moderate level of knowledge, utilizing low-cost facilities to target IoT systems. In this paper, we explore the main parameters of the clock and voltage fault generators. This can help hardware security specialists to develop an open-source platform and to evaluate their design against such attacks. The principal concepts of both methods are studied for this purpose. Thereafter, we conclude our paper with the need for such an evaluation platform in the design and production cycle of embedded systems and IoT devices.

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

An Investigation for Cellular Automata-Based Lightweight Data Security Model Towards Possible Uses in Fog Networks

2021 ◽  
pp. 209-226
Author(s):  
Arnab Mitra ◽  
Sayantan Saha
Keyword(s):  
Internet Of Things ◽  
Cellular Automata ◽  
Data Security ◽  
Low Cost ◽  
Security And Privacy ◽  
Security Model ◽  
Physical Implementation ◽  
Security Models ◽  
Industry Applications ◽  
Computing Capacity

A lightweight data security model is of much importance in view of security and privacy of data in several networks (e.g., fog networks) where available computing units at edge nodes are often constrained with low computing capacity and limited storage/availability of energy. To facilitate lightweight data security at such constrained scenarios, cellular automata (CA)-based lightweight data security model is presented in this chapter to enable low-cost physical implementation. For this reason, a detailed investigation is presented in this chapter to explore the potential capabilities of CA-based scheme towards the design of lightweight data security model. Further, a comparison among several existing lightweight data security models ensure the effectiveness for proposed CA-based lightweight data security model. Thus, application suitability in view of fog networks is explored for the proposed CA-based model which has further potential for easy training of a reservoir of computers towards uses in IoT (internet of things)-based multiple industry applications.

scholarly journals PUF Based Authentication Protocol for IoT

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 352 ◽  
Author(s):  
An Braeken
Keyword(s):  
Internet Of Things ◽  
Key Agreement ◽  
Essential Feature ◽  
Low Cost ◽  
Security Model ◽  
Alternative Scheme ◽  
Physical Unclonable Functions ◽  
Authentication Server ◽  
Random Patterns ◽  
Iot Devices

Key agreement between two constrained Internet of Things (IoT) devices that have not met each other is an essential feature to provide in order to establish trust among its users. Physical Unclonable Functions (PUFs) on a device represent a low cost primitive exploiting the unique random patterns in the device and have been already applied in a multitude of applications for secure key generation and key agreement in order to avoid an attacker to take over the identity of a tampered device, whose key material has been extracted. This paper shows that the key agreement scheme of a recently proposed PUF based protocol, presented by Chatterjee et al., for Internet of Things (IoT) is vulnerable for man-in-the-middle, impersonation, and replay attacks in the Yao–Dolev security model. We propose an alternative scheme, which is able to solve these issues and can provide in addition a more efficient key agreement and subsequently a communication phase between two IoT devices connected to the same authentication server. The scheme also offers identity based authentication and repudiation, when only using elliptic curve multiplications and additions, instead of the compute intensive pairing operations.

scholarly journals Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yawei Yue ◽  
Shancang Li ◽  
Phil Legg ◽  
Fuzhong Li
Keyword(s):  
Deep Learning ◽  
Smart Home ◽  
Ad Hoc ◽  
Security Analysis ◽  
Home Healthcare ◽  
Security And Privacy ◽  
Iot Security ◽  
Privacy Concerns ◽  
Iot Devices ◽  
Primary Focus

Internet of Things (IoT) applications have been used in a wide variety of domains ranging from smart home, healthcare, smart energy, and Industrial 4.0. While IoT brings a number of benefits including convenience and efficiency, it also introduces a number of emerging threats. The number of IoT devices that may be connected, along with the ad hoc nature of such systems, often exacerbates the situation. Security and privacy have emerged as significant challenges for managing IoT. Recent work has demonstrated that deep learning algorithms are very efficient for conducting security analysis of IoT systems and have many advantages compared with the other methods. This paper aims to provide a thorough survey related to deep learning applications in IoT for security and privacy concerns. Our primary focus is on deep learning enhanced IoT security. First, from the view of system architecture and the methodologies used, we investigate applications of deep learning in IoT security. Second, from the security perspective of IoT systems, we analyse the suitability of deep learning to improve security. Finally, we evaluate the performance of deep learning in IoT system security.

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