Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

A Survey

2019 ◽  
Vol 13 (1) ◽  
pp. 86-105 ◽  
Author(s):  
Sarika Choudhary ◽  
Nishtha Kesswani
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Detection System ◽  
Human Life ◽  
Cyber Attacks ◽  
Internet Technology ◽  
General Idea ◽  
The Internet ◽  
Detection Techniques ◽  
The Internet Of Things

The latest buzzword in internet technology nowadays is the Internet of Things. The Internet of Things (IoT) is an ever-growing network which will transform real-world objects into smart or intelligent virtual objects. IoT is a heterogeneous network in which devices with different protocols can connect with each other in order to exchange information. These days, human life depends upon the smart things and their activities. Therefore, implementing protected communications in the IoT network is a challenge. Since the IoT network is secured with authentication and encryption, but not secured against cyber-attacks, an Intrusion Detection System is needed. This research article focuses on IoT introduction, architecture, technologies, attacks and IDS. The main objective of this article is to provide a general idea of the Internet of Things, various intrusion detection techniques, and security attacks associated with IoT.

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

A probable effect of the development of internet technology on insurance & banking sectors, decision processes and service structures

2020 ◽  
Vol 1 (1) ◽  
pp. 36-41
Author(s):  
Ali Osman Serdar Citak
Keyword(s):  
Internet Of Things ◽  
Decision Processes ◽  
Internet Technology ◽  
Smart Sensors ◽  
The Internet ◽  
Future Expectations ◽  
History Of ◽  
Probable Effect ◽  
Near Future ◽  
The Internet Of Things

The history of the development of the Internet of Things (IoT) covers the last twenty years. Despite the short of time, the concept and implementation of the Internet of Things have widely spread all over the world. The impetus of the dissemination of the concept has exponential speed. In the near future, billions of smart sensors and devices will interact with one another without human intervention. The early impact of the Internet of Things has been observed and discussed in the areas of technology, transportation, production, and marketing. The prospective effect of the Internet of Things on the finance sector has been discussed recently. In this study, the development of the concept of the Internet of Things and it is effect on the finance sector and specifically the insurance and banking sectors and future expectations have been evaluated.

scholarly journals Hardware Security of Fog End-Devices for the Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5729 ◽  
Author(s):  
Ismail Butun ◽  
Alparslan Sari ◽  
Patrik Österberg
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Hardware Security ◽  
Cyber Attacks ◽  
The Internet ◽  
Edge Component ◽  
Security Module ◽  
System On A Chip ◽  
Lower Spectrum ◽  
The Internet Of Things

The proliferation of the Internet of Things (IoT) caused new application needs to emerge as rapid response ability is missing in the current IoT end-devices. Therefore, Fog Computing has been proposed to be an edge component for the IoT networks as a remedy to this problem. In recent times, cyber-attacks are on the rise, especially towards infrastructure-less networks, such as IoT. Many botnet attack variants (Mirai, Torii, etc.) have shown that the tiny microdevices at the lower spectrum of the network are becoming a valued participant of a botnet, for further executing more sophisticated attacks against infrastructural networks. As such, the fog devices also need to be secured against cyber-attacks, not only software-wise, but also from hardware alterations and manipulations. Hence, this article first highlights the importance and benefits of fog computing for IoT networks, then investigates the means of providing hardware security to these devices with an enriched literature review, including but not limited to Hardware Security Module, Physically Unclonable Function, System on a Chip, and Tamper Resistant Memory.

scholarly journals Influences of the Industry 4.0 Revolution on the Human Capital Development and Consumer Behavior: A Systematic Review

2020 ◽  
Vol 12 (10) ◽  
pp. 4035 ◽  
Author(s):  
Violeta Sima ◽  
Ileana Georgiana Gheorghe ◽  
Jonel Subić ◽  
Dumitru Nancu
Keyword(s):  
Artificial Intelligence ◽  
Human Capital ◽  
Consumer Behavior ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Internet Technology ◽  
The Internet ◽  
Human Capital Development ◽  
Capital Development ◽  
The Internet Of Things

Automation and digitalization, as long-term evolutionary processes, cause significant effects, such as the transformation of occupations and job profiles, changes to employment forms, and a more significant role for the platform economy, generating challenges for social policy. This systematic literature review aims to provide an overview of the research to date related to influences of the Industry 4.0 Revolution on human capital development and consumer behavior. A search on the Web of Science identified 160 papers that met the inclusion criteria. The major objectives aimed to identify: the main types of influences of the Industry 4.0 Revolution on human capital development and consumer behavior; the main opportunities and challenges for new directions in education associated with shifting the work environment; and the drivers for human capital development and consumer behavior through the lenses of the Industry 4.0 Revolution. The results revealed some key aspects for the development of human capital: information, new jobs, the Internet, technology, training, education, new skills, automation, communication, innovativeness, professionals, productivity, artificial intelligence, digitalization, e-recruitment, and the Internet of Things, as well as the main drivers of consumer behavior: information, e-commerce, digitalization, the Internet of Things, e-distribution, technology, digitalization, automation, personalized, performance, artificial intelligence, behavior intention, e-shopping, and data mining.

scholarly journals Unified Traceability Information System of Logistics Pallet Based on the Internet of Things

2013 ◽  
Vol 765-767 ◽  
pp. 1181-1185 ◽  
Author(s):  
Xing Zhi Lin
Keyword(s):  
Information System ◽  
Internet Of Things ◽  
Internet Technology ◽  
The Internet ◽  
Coupling Mechanism ◽  
Logistics Information System ◽  
Traceability System ◽  
Logistics Information ◽  
Intelligent Logistics ◽  
The Internet Of Things

The paper focuses on the intelligent logistics pallet, networking, traceability information technology and unified logistics information system, and puts forward unified traceability information system of intelligent logistics pallet based on the Internet of things (IOT), displaying innovative system realization method and technical system. IOT intelligent logistic pallet traceability system is an integrated fusion application of RFID, GIS, GPS, computer telecommunication integration together with Internet technology; in system design and implementation, TOT and RFID coupling mechanism, CTI and API interface programming are adopted for construction of three intelligent system service architecture of logistics pallet traceability system including application, business and physics; in the logistics pallet regular operation, reading and writing RFID equipments, labels and telecommunications network, Internet, and other unified information communication mode are fused into the intelligent logistics information system, so as to realize the intelligent logistics pallet identity identification and traceability function. The system test output results indicate that, intelligent logistics pallet unified information system is equipped with intelligent recognition, management and tracing function and business process reengineering capacity including production promotion, distribution, transport logistics and allocation.

scholarly journals Decision Tree Based Interference Recognition for Fog Enabled IOT Architecture

2020 ◽  
Vol 2 (1) ◽  
pp. 15-25
Author(s):  
Dr. Mugunthan S. R.
Keyword(s):  
Internet Of Things ◽  
Initial Data ◽  
Decision Tree ◽  
False Alarm ◽  
Final Stage ◽  
Cyber Attacks ◽  
The Internet ◽  
Data Set ◽  
The Internet Of Things ◽  
Decision Forest

The cyber-attacks nowadays are becoming more and more erudite causing challenges in distinguishing them and confining. These attacks affect the sensitized information’s of the network by penetrating into the network and behaving normally. The paper devises a system for such interference recognition in the internet of things architecture that is aided by the FOG. The proposed system is a combination of variety of classifiers that are founded on the decision tree as well as the rule centered conceptions. The system put forth involves the JRip and the REP tree algorithm to utilize the features of the data set as input and distinguishes between the benign and the malicious traffic in the network and includes an decision forest that is improved with the penalizing attributes of the previous trees in the final stage to classify the traffic in the network utilizing the initial data set as well as the outputs of the classifiers that were engaged in the former stages. The proffered system was examined using the dataset such BOT-Internet of things and the CICIDS2017 to evince its competence in terms of rate of false alarm, detection, and accuracy. The attained results proved that the performance of the proposed system was better compared to the exiting methodologies to recognize the interference.

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