scholarly journals A Survey of IoT Security Based on a Layered Architecture of Sensing and Data Analysis

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3625 ◽  
Author(s):  
Hichem Mrabet ◽  
Sana Belguith ◽  
Adeeb Alhomoud ◽  
Abderrazak Jemai
Keyword(s):  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Transport Layer ◽  
Security Threats ◽  
Application Layer ◽  
Research Issues ◽  
Layered Architecture ◽  
Survey Paper ◽  
Encryption Algorithms ◽  
Lightweight Encryption

The Internet of Things (IoT) is leading today’s digital transformation. Relying on a combination of technologies, protocols, and devices such as wireless sensors and newly developed wearable and implanted sensors, IoT is changing every aspect of daily life, especially recent applications in digital healthcare. IoT incorporates various kinds of hardware, communication protocols, and services. This IoT diversity can be viewed as a double-edged sword that provides comfort to users but can lead also to a large number of security threats and attacks. In this survey paper, a new compacted and optimized architecture for IoT is proposed based on five layers. Likewise, we propose a new classification of security threats and attacks based on new IoT architecture. The IoT architecture involves a physical perception layer, a network and protocol layer, a transport layer, an application layer, and a data and cloud services layer. First, the physical sensing layer incorporates the basic hardware used by IoT. Second, we highlight the various network and protocol technologies employed by IoT, and review the security threats and solutions. Transport protocols are exhibited and the security threats against them are discussed while providing common solutions. Then, the application layer involves application protocols and lightweight encryption algorithms for IoT. Finally, in the data and cloud services layer, the main important security features of IoT cloud platforms are addressed, involving confidentiality, integrity, authorization, authentication, and encryption protocols. The paper is concluded by presenting the open research issues and future directions towards securing IoT, including the lack of standardized lightweight encryption algorithms, the use of machine-learning algorithms to enhance security and the related challenges, the use of Blockchain to address security challenges in IoT, and the implications of IoT deployment in 5G and beyond.

scholarly journals Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4821
Author(s):  
Rami Ahmad ◽  
Raniyah Wazirali ◽  
Qusay Bsoul ◽  
Tarik Abu-Ain ◽  
Waleed Abu-Ain
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Open Field ◽  
Network Lifetime ◽  
Detection Efficiency ◽  
Denial Of Service ◽  
Harmony Search ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Feature Selection Techniques

Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

scholarly journals Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

2021 ◽  
Vol 11 (4) ◽  
pp. 1627
Author(s):  
Yanbin Li ◽  
Gang Lei ◽  
Gerd Bramerdorfer ◽  
Sheng Peng ◽  
Xiaodong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Methods ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Robust Design Optimization ◽  
Support Vector ◽  
Future Directions ◽  
Electromagnetic Devices ◽  
Recent Developments

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

LIGHTWEIGHT CRYPTOGRAPHIC ALGORITHM IMPLEMENTATION IN A MICROCONTROLLER SYSTEM

2021 ◽  
Vol 17 (1) ◽  
pp. 260-264
Author(s):  
Alexandru VULPE ◽  
Raluca ANDREI ◽  
Alexandru BRUMARU ◽  
Octavian FRATU
Keyword(s):  
The Internet ◽  
Continuous Increase ◽  
Security Issues ◽  
Cryptographic Algorithm ◽  
Physical Limitations ◽  
Encryption Algorithms ◽  
Iot Devices ◽  
The Cost ◽  
Algorithm Implementation ◽  
Lightweight Encryption

Abstract: With the development of mobile devices and the advent of smartphones, the Internet has become part of everyday life. Any category of information about weather, flight schedule, etc. it is just a click away from the keyboard. This availability of data has led to a continuous increase in connectivity between devices, from any corner of the world. Combining device connectivity with systems automation allows the collection of information, its analysis and implicitly decision-making on the basis of information. Their introduction and continued expansion of devices that communicate in networks (including the Internet) have made security issues very important devices as well as for users. One of the main methodologies that ensures data confidentiality is encryption, which protects data from unauthorized access, but at the cost of using extensive mathematical models. Due to the nature of IoT devices, the resources allocated to a device can be constrained by certain factors, some of which are related to costs and others to the physical limitations of the device. Ensuring the confidentiality of data requires the use of encryption algorithms for these interconnected devices, which provide protection while maintaining the operation of that device. The need for these types of algorithms has created conditions for the growth and development of the concept of lightweight encryption, which aim to find encryption systems that can be implemented on these categories of devices, with limited hardware and software requirements. The paper proposes a lightweight cryptographic algorithm implemented on a microcontroller system, comparing its performances with those of the already existing system (based on x86).

scholarly journals An Integrated Cross Layer Design to Enhance the Quality of Service of Video Streaming in Mobile Adhoc Networks

2018 ◽  
Vol 7 (2) ◽  
pp. 57
Author(s):  
N. Gomathi ◽  
M. Uvaneshwari
Keyword(s):  
Signal To Noise Ratio ◽  
Transport Layer ◽  
Application Layer ◽  
Font Size ◽  
Signal To Noise ◽  
Mapping Algorithm ◽  
Novel Technique ◽  
Dynamic Mapping ◽  
Multipath Transport ◽  
Description Coding

<span style="font-size: 9.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">The intent of this paper is to put forth a novel technique for improvising the QoS of multimedia applications in by using Modified dynamic mapping algorithm and Multipath transport(MPT) and Multi Description Coding(MDC). The improvement is attained by applying the MDC at application layer along with UDPLite in transport layer and multipath at network layer and Modified dynamic mapping in MAC Layer.Thismethod attains an increase of 30.84% in Peak Signal to Noise Ratio (PSNR) and 18.57% decrease in delay in contrast to the conventional methods.</span><table class="MsoTableGrid" style="width: 444.85pt; border-collapse: collapse; border: none; mso-border-alt: solid windowtext .5pt; mso-yfti-tbllook: 1184; mso-padding-alt: 0in 5.4pt 0in 5.4pt;" width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr style="mso-yfti-irow: 0; mso-yfti-firstrow: yes; mso-yfti-lastrow: yes; height: 63.4pt;"><td style="width: 290.6pt; border: none; border-top: solid windowtext 1.0pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; height: 63.4pt;" valign="top" width="387"><p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;"><span style="font-size: 9.0pt;">The intent of this paper is to put forth a novel technique for improvising the QoS of multimedia applications in by using Modified dynamic mapping algorithm and Multipath transport(MPT) and Multi Description Coding(MDC). The improvement is attained by applying the MDC at application layer along with UDPLite in transport layer and multipath at network layer and Modified dynamic mapping in MAC Layer.Thismethod attains an increase of 30.84% in Peak Signal to Noise Ratio (PSNR) and 18.57% decrease in delay in contrast to the conventional methods.</span></p></td></tr></tbody></table>

scholarly journals DDoS Detection on Internet of Things using Unsupervised Algorithms

2021 ◽  
Vol 297 ◽  
pp. 01005
Author(s):  
Hailyie Tekleselassie
Keyword(s):  
Learning Algorithms ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Accuracy Score ◽  
Ddos Attacks ◽  
Knowledge Based ◽  
Ddos Detection ◽  
Iot Devices ◽  
Unsupervised Algorithms

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

scholarly journals Text File Privacy on the Cloud Based on Diagonal Fragmentation and Encryption

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tawfiq Barhoom ◽  
Mahmoud Abu Shawish
Keyword(s):  
Data Privacy ◽  
Cloud Services ◽  
Text File ◽  
Layer Model ◽  
Encrypted Data ◽  
Cloud Server ◽  
Cloud Environments ◽  
Encryption Algorithms ◽  
Well Data ◽  
Encryption Method

Despite the growing reliance on cloud services and software, privacy is somewhat difficult. We store our data on remote servers in cloud environments that are untrusted. If we do not handle the stored data well, data privacy can be violated with no awareness on our part. Although it requires expensive computation, encrypting the data before sending it appears to be a solution to this problem. So far, all known solutions to protect textual files using encryption algorithms fell short of privacy expectations. Thus is because encrypting cannot stand by itself. The encrypted data on the cloud server becomes full file in the hand causing the privacy of this data to be intrusion-prone, thus allowing intruders to access the file data once they can decrypt it. This study aimed to develop an effective cloud confidentiality model based on combining fragmentation and encryption of text files to compensate for reported deficiency in encryption methods. The fragmentation method used the strategy of dividing text files into two triangles through the axis. Whereas the encryption method used the Blowfish algorithm. The research concluded that high confidentiality is achieved by building a multi-layer model: encryption, chunk, and fragmentation of every chunk to prevent intruders from reaching the data even if they were able to decrypt the file. Using the privacy accuracy equation (developed for the purpose in this research), the model achieved accuracy levels of 96% and 90% when using 100 and 200 words in each chunk on small, medium, and large files respectively.

Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks

2021 ◽  
Vol 17 (4) ◽  
pp. 75-88
Author(s):  
Padmaja Kadiri ◽  
Seshadri Ravala
Keyword(s):  
Machine Learning ◽  
Service Provider ◽  
Predictive Models ◽  
Cloud Service ◽  
Cloud Services ◽  
Security Threats ◽  
Learning Models ◽  
Security Attacks ◽  
Cloud Service Provider ◽  
Machine Learning Models

Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during security attacks. The analysis of the results shows that the kernel-based models show 87% accuracy in predicting the mitigation time. Furthermore, the paper explores the performance of the kernel-based machine learning models based on the regression-based predictive models. The regression model is used as a benchmark model to analyze the performance of the machine learning-based predictive models in the prediction of mitigation time in the wake of an attack.

Design of Smart Grid Test Bed Using OPNET and PLC

2018 ◽  
pp. 22-42
Keyword(s):  
Smart Grid ◽  
Power Line ◽  
Transport Layer ◽  
Application Layer ◽  
Test Bed ◽  
Data Link ◽  
Link Layer ◽  
Data Link Layer ◽  
Base Data ◽  
Grid Test

In this design unit, a design to test the performances of varying models was developed for the simulations in the PLC-base data link layer. The design includes a smart home and a Smart Grid environment where a comparison between Zigbee and WiMax-based models can be performed. The Smart Grid Test Bed has been designed using OPNET and Power Line Communication is proposed in this book. It is being designed to allow test bed experiments in four layers among OSI 7 layers. This chapter is organized as follows: The Physical Layer and Datalink Layer for Smart Grid Test Bed in Section 1; the Transport Layer for Smart Grid Test Bed in Section 2; and finally, Application Layer for Smart Grid Test Bed in Section.

A Lightweight Authentication and Encryption Protocol for Secure Communications Between Resource-Limited Devices Without Hardware Modification

2018 ◽  
pp. 1-46
Author(s):  
Piotr Ksiazak ◽  
William Farrelly ◽  
Kevin Curran
Keyword(s):  
Ubiquitous Computing ◽  
Secure Communications ◽  
Wireless Devices ◽  
Authentication Protocols ◽  
It Industry ◽  
Processing Power ◽  
Resource Limited ◽  
Encryption Algorithms ◽  
Lightweight Authentication ◽  
Lightweight Encryption

In this chapter, the authors examine the theoretical context for the security of wireless communication between ubiquitous computing devices and present an implementation that addresses this need. The number of resource-limited wireless devices utilized in many areas of the IT industry is growing rapidly. Some of the applications of these devices pose real security threats that can be addressed using authentication and cryptography. Many of the available authentication and encryption software solutions are predicated on the availability of ample processing power and memory. These demands cannot be met by most ubiquitous computing devices; thus, there is a need to apply lightweight cryptography primitives and lightweight authentication protocols that meet these demands in any application of security to devices with limited resources. The analysis of the lightweight solutions is divided into lightweight authentication protocols and lightweight encryption algorithms. The authors present a prototype running on the nRF9E5 microcontroller that provides necessary authentication and encryption on resource-limited devices.

A Deep Learning Approach for Detection of Application Layer Attacks in Internet

2020 ◽  
pp. 175-188
Author(s):  
V. Punitha ◽  
C. Mala
Keyword(s):  
Deep Learning ◽  
Transport Layer ◽  
Classification Model ◽  
Learning Approach ◽  
Ddos Attacks ◽  
Application Layer ◽  
Learning Models ◽  
Technological Transformation ◽  
Application Deployment ◽  
Machine Learning Models

The recent technological transformation in application deployment, with the enriched availability of applications, induces the attackers to shift the target of the attack to the services provided by the application layer. Application layer DoS or DDoS attacks are launched only after establishing the connection to the server. They are stealthier than network or transport layer attacks. The existing defence mechanisms are unproductive in detecting application layer DoS or DDoS attacks. Hence, this chapter proposes a novel deep learning classification model using an autoencoder to detect application layer DDoS attacks by measuring the deviations in the incoming network traffic. The experimental results show that the proposed deep autoencoder model detects application layer attacks in HTTP traffic more proficiently than existing machine learning models.

Sign in / Sign up

Export Citation Format

Share Document