scholarly journals Hardware Implementation of Secure Lightweight Cryptographic Designs for IoT Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Wajih El Hadj Youssef ◽  
Ali Abdelli ◽  
Fethi Dridi ◽  
Mohsen Machhout
Keyword(s):  
High Performance ◽  
Security Analysis ◽  
Smart Devices ◽  
Security Level ◽  
Test Results ◽  
Cryptographic Primitives ◽  
Application Deployment ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Constrained Devices

The recent expansion of the Internet of Things is creating a new world of smart devices in which security implications are very significant. Besides the claimed security level, the IoT devices are usually featured with constrained resources, such as low computation capability, low memory, and limited battery. Lightweight cryptographic primitives are proposed in the context of IoT while considering the trade-off between security guarantee and good performance. In this paper, we present optimized hardware, lightweight cryptographic designs, of 32-bit datapath, LED 64/128, SIMON 64/128, and SIMECK 64/128 algorithms, for constrained devices. Our proposed designs are investigated on Spartan-3, Spartan-6, and Zynq-7000 FPGA platforms in terms of area, speed, efficiency, and power consumption. The proposed designs achieved a high throughput up to 891.99 Mbps, 838.95 Mbps, and 210.13 Mbps for SIMECK 64/128, SIMON 64/128, and LED 64/128 on Zynq-7000, respectively. A deep comparison between our three proposed designs is elaborated on different FPGA families for adequate FPGAs-based application deployment. Test results and security analysis show that not only can our proposed designs achieve good encryption results with high performance and a low reduced cost but also they are secure enough to resist statistical attacks.

scholarly journals Implementing a Symmetric Lightweight Cryptosystem in Highly Constrained IoT Devices by Using a Chaotic S-Box

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 129
Author(s):  
Badr M. Alshammari ◽  
Ramzi Guesmi ◽  
Tawfik Guesmi ◽  
Haitham Alsaif ◽  
Ahmed Alzamil
Keyword(s):  
Secure Communication ◽  
Ieee 802.15.4 ◽  
Security Analysis ◽  
Advanced Encryption Standard ◽  
The Internet ◽  
Iot Devices ◽  
The One ◽  
Nist Tests ◽  
The Internet Of Things ◽  
Constrained Devices

In the Internet of Things (IoT), a lot of constrained devices are interconnected. The data collected from those devices can be the target of cyberattacks. In this paper, a lightweight cryptosystem that can be efficiently implemented in highly constrained IOT devices is proposed. The algorithm is mainly based on Advanced Encryption Standard (AES) and a new chaotic S-box. Since its adoption by the IEEE 802.15.4 protocol, AES in embedded platforms have been increasingly used. The main cryptographic properties of the generated S-box have been validated. The randomness of the generated S-box has been confirmed by the NIST tests. Experimental results and security analysis demonstrated that the cryptosystem can, on the one hand, reach good encryption results and respects the limitation of the sensor’s resources, on the other hand. So the proposed solution could be reliably applied in image encryption and secure communication between networked smart objects.

scholarly journals Design a blockchain-based middleware layer in the Internet of Things Architecture

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Tanweer Alam
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Smart Devices ◽  
The Internet ◽  
Novel Approach ◽  
Generation Computing ◽  
Critical Issues ◽  
Iot Devices ◽  
The Internet Of Things

In next-generation computing, the role of cloud, internet and smart devices will be capacious. Nowadays we all are familiar with the word smart. This word is used a number of times in our daily life. The Internet of Things (IoT) will produce remarkable different kinds of information from different resources. It can store big data in the cloud. The fog computing acts as an interface between cloud and IoT. The extension of fog in this framework works on physical things under IoT. The IoT devices are called fog nodes, they can have accessed anywhere within the range of the network. The blockchain is a novel approach to record the transactions in a sequence securely. Developing a new blockchains based middleware framework in the architecture of the Internet of Things is one of the critical issues of wireless networking where resolving such an issue would result in constant growth in the use and popularity of IoT. The proposed research creates a framework for providing the middleware framework in the internet of smart devices network for the internet of things using blockchains technology. Our main contribution links a new study that integrates blockchains to the Internet of things and provides communication security to the internet of smart devices.

Encryption Principles and Techniques for the Internet of Things

2019 ◽  
pp. 42-66
Author(s):  
Kundankumar Rameshwar Saraf ◽  
Malathi P. Jesudason
Keyword(s):  
Internet Of Things ◽  
Embedded System ◽  
Security Analysis ◽  
Block Size ◽  
The Internet ◽  
Lightweight Cryptography ◽  
Execution Speed ◽  
Security Algorithm ◽  
Iot Devices ◽  
The Internet Of Things

This chapter explores the encryption techniques used for the internet of things (IoT). The security algorithm used for IoT should follow many constraints of an embedded system. Hence, lightweight cryptography is an optimum security solution for IoT devices. This chapter mainly describes the need for security in IoT, the concept of lightweight cryptography, and various cryptographic algorithms along with their shortcomings given IoT. This chapter also describes the principle of operation of all the above algorithms along with their security analysis. Moreover, based on the algorithm size (i.e., the required number of gate equivalent, block size, key size, throughput, and execution speed of the algorithm), the chapter reports the comparative analysis of their performance. The chapter discusses the merits and demerits of these algorithms along with their use in the IoT system.

A Machine Learning-Based Exploration of Relationship Between Security Vulnerabilities of IoT Devices and Manufacturers

2020 ◽  
Vol 1 (2) ◽  
pp. 1-12
Author(s):  
Ritu Chauhan ◽  
Gatha Tanwar
Keyword(s):  
Cyber Security ◽  
Automated System ◽  
Smart Devices ◽  
Protocol Stack ◽  
Security Vulnerabilities ◽  
Local Networks ◽  
Daily Lives ◽  
Security Issues ◽  
Iot Devices ◽  
The Internet Of Things

The internet of things has brought in innovations in the daily lives of users. The enthusiasm and openness of consumers have fuelled the manufacturers to dish out new devices with more features and better aesthetics. In an attempt to keep up with the competition, the manufacturers are not paying enough attention to cyber security of these smart devices. The gravity of security vulnerabilities is further aggravated due to their connected nature. As a result, a compromised device would not only stop providing the intended service but could also act as a host for malware introduced by an attacker. This study has focused on 10 manufacturers, namely Fitbit, D-Link, Edimax, Ednet, Homematic, Smarter, Osram, Belkin Wemo, Philips Hue, and Withings. The authors studied the security issues which have been raised in the past and the communication protocols used by devices made by these brands. It was found that while security vulnerabilities could be introduced due to lack of attention to details while designing an IoT device, they could also get introduced by the protocol stack and inadequate system configuration. Researchers have iterated that protocols like TCP, UDP, and mDNS have inherent security shortcomings and manufacturers need to be mindful of the fact. Furthermore, if protocols like EAPOL or Zigbee have been used, then the device developers need to be aware of safeguarding the keys and other authentication mechanisms. The authors also analysed the packets captured during setup of 23 devices by the above-mentioned manufacturers. The analysis gave insight into the underlying protocol stack preferred by the manufacturers. In addition, they also used count vectorizer to tokenize the protocols used during device setup and use them to model a multinomial classifier to identify the manufacturers. The intent of this experiment was to determine if a manufacturer could be identified based on the tokenized protocols. The modelled classifier could then be used to drive an algorithm to checklist against possible security vulnerabilities, which are characteristic of the protocols and the manufacturer history. Such an automated system will be instrumental in regular diagnostics of a smart system. The authors then wrapped up this report by suggesting some measures a user can take to protect their local networks and connected devices.

CHAOS BASED PARTIAL ENCRYPTION OF SPIHT COMPRESSED IMAGES

2011 ◽  
Vol 09 (02) ◽  
pp. 317-331 ◽  
Author(s):  
NIDHI TANEJA ◽  
BALASUBRAMANIAN RAMAN ◽  
INDRA GUPTA
Keyword(s):  
High Performance ◽  
Security Analysis ◽  
Multimedia Data ◽  
Security Level ◽  
Partial Encryption ◽  
High Data ◽  
Compressed Images ◽  
Wireless Ip ◽  
Edge Based ◽  
Transmission And Distribution

Resource-constrained wireless IP networks require compression based encryption techniques for secure archival, transmission and distribution of multimedia data. A partial encryption technique based on SPIHT encoder is thus proposed that provides enhanced security level by encrypting selected bit values and keeping the encrypted bit locations as secret. The proposed technique preserves the scalability property of the encoder and provides high data security without adversely affecting the compression efficiency. Two new edge based parameters, edge ratio and edge differential ratio are also proposed to measure the degradation in the encrypted image. Thorough performance and security analysis as regards to various evaluation metrics ascertains its high performance and ability to withstand cryptanalytic attacks.

scholarly journals Towards Light Weight Cryptography Schemes for Resource Constraint Devices in IoT

2020 ◽  
Author(s):  
Santosh Pandurang Jadhav
Keyword(s):  
Processing Capacity ◽  
Light Weight ◽  
Resource Constrained ◽  
Computational Costs ◽  
Encryption And Decryption ◽  
Limited Power ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Resource Constrained Devices ◽  
Constrained Devices

The Internet of Things (IoT) is becoming the most relevant next Internet-related revolution in the world of Technology. It permits millions of devices to be connected and communicate with each other. Beside ensuring reliable connectivity their security is also a great challenge. Abounding IoT devices have a minimum of storage and processing capacity and they usually need to be able to operate on limited power consumption. Security paths that depend maximum on encryption are not good for these resource constrained devices, because they are not suited for performing complicated encryption and decryption tasks quickly to be able to transmit data securely in real-time. This paper contains an overview of some of the cryptographic-based schemes related to communication and computational costs for resource constrained devices and considers some approaches towards the development of highly secure and lightweight security mechanisms for IoT devices.

scholarly journals COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1492 ◽  
Author(s):  
Pantaleone Nespoli ◽  
David Useche Pelaez ◽  
Daniel Díaz López ◽  
Félix Gómez Mármol
Keyword(s):  
Internet Of Things ◽  
Real World ◽  
Smart Devices ◽  
Raspberry Pi ◽  
The Internet ◽  
Security Issues ◽  
Dynamic Scenario ◽  
Computer Based ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) became established during the last decade as an emerging technology with considerable potentialities and applicability. Its paradigm of everything connected together penetrated the real world, with smart devices located in several daily appliances. Such intelligent objects are able to communicate autonomously through already existing network infrastructures, thus generating a more concrete integration between real world and computer-based systems. On the downside, the great benefit carried by the IoT paradigm in our life brings simultaneously severe security issues, since the information exchanged among the objects frequently remains unprotected from malicious attackers. The paper at hand proposes COSMOS (Collaborative, Seamless and Adaptive Sentinel for the Internet of Things), a novel sentinel to protect smart environments from cyber threats. Our sentinel shields the IoT devices using multiple defensive rings, resulting in a more accurate and robust protection. Additionally, we discuss the current deployment of the sentinel on a commodity device (i.e., Raspberry Pi). Exhaustive experiments are conducted on the sentinel, demonstrating that it performs meticulously even in heavily stressing conditions. Each defensive layer is tested, reaching a remarkable performance, thus proving the applicability of COSMOS in a distributed and dynamic scenario such as IoT. With the aim of easing the enjoyment of the proposed sentinel, we further developed a friendly and ease-to-use COSMOS App, so that end-users can manage sentinel(s) directly using their own devices (e.g., smartphone).

Expert system for identifying and analyzing the IoT devices using Augmented Reality Approach

2021 ◽  
pp. 43-57
Author(s):  
WASIN ALKISHRI ◽  
Mahmood Al-Bahri
Keyword(s):  
Augmented Reality ◽  
Internet Of Things ◽  
Specific Area ◽  
Relevant Information ◽  
The Internet ◽  
Test Results ◽  
New Development ◽  
Server Software ◽  
Iot Devices ◽  
The Internet Of Things

Biometrics In conjunction with the new development of the Internet of Things (IoT), augmented reality (AR) systems are evolving to visualize 3D virtual models of the real world into an intelligent and interactive virtual reality environment that facilitates physical identification of objects and defines their specifications efficiently. The integration between AR and IoT in a complementary way helps identify network-related items' specifications and interact with the Internet of Things more efficiently. An identity is a dedicated, publicly known attribute or set of names for an individual device. Typically, identifiers operate within a specific area or network, making it difficult to identify things globally. This paper explores the use of Augmented Reality (AR) Technology for identifying devices and displaying relevant information about the device to the user. Based on the developed model network, the developed system of identification of IoT devices was tested. Also, the traffic generated by the AR device when generating requests to the organization server was investigated. According to the test results, the system is undemanding to the main network indicators. The system-generated traffic is self-similar. The test results show that the server software can solve the problems of identifying IoT devices through interaction with augmented reality devices.

scholarly journals Malware Detection Based on Deep Learning of Behavior Graphs

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Xiao ◽  
Zhaowen Lin ◽  
Yi Sun ◽  
Yan Ma
Keyword(s):  
Deep Learning ◽  
Smart Devices ◽  
Smart Device ◽  
Learning Framework ◽  
Stacked Autoencoders ◽  
Efficient Information ◽  
Iot Devices ◽  
High Level ◽  
Malicious Behaviors ◽  
The Internet Of Things

The Internet of Things (IoT) provides various benefits, which makes smart device even closer. With more and more smart devices in IoT, security is not a one-device affair. Many attacks targeted at traditional computers in IoT environment may also aim at other IoT devices. In this paper, we consider an approach to protect IoT devices from being attacked by local computers. In response to this issue, we propose a novel behavior-based deep learning framework (BDLF) which is built in cloud platform for detecting malware in IoT environment. In the proposed BDLF, we first construct behavior graphs to provide efficient information of malware behaviors using extracted API calls. We then use a neural network-Stacked AutoEncoders (SAEs) for extracting high-level features from behavior graphs. The layers of SAEs are inserted one after another and the last layer is connected to some added classifiers. The architecture of the SAEs is 6,000-2,000-500. The experiment results demonstrate that the proposed BDLF can learn the semantics of higher-level malicious behaviors from behavior graphs and further increase the average detection precision by 1.5%.

scholarly journals Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 600
Author(s):  
Gianluca Cornetta ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
High Performance ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Raspberry Pi ◽  
Embedded Devices ◽  
Iot Devices

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.

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