scholarly journals EADA: An Algorithm for Early Detection of Attacks on IoT Resources

IoT applications are becoming widespread in monitoring and managing critical infrastructure. Many attacks have been demonstrated in the state-of-the-art on IoT resources. These attacks make use of vulnerabilities present in various connected systems and the Internet of Things (IoT). The state-of-the-art presents many approaches to detect and mitigate such attacks on IoT resources. The early attack detection mechanism is essential to prevent damage to the IoT system and human. This paper presents an algorithm for early detection of attacks on IoT resources through use of predictive descriptor tables. Effectiveness of the proposed algorithm is evaluated through experimental setup built using Google cloud platform. Experimental results show that the proposed algorithm is efficient in the detection of attacks in real-time.

2021 ◽  
Vol 11 (16) ◽  
pp. 7228
Author(s):  
Edward Staddon ◽  
Valeria Loscri ◽  
Nathalie Mitton

With the ever advancing expansion of the Internet of Things (IoT) into our everyday lives, the number of attack possibilities increases. Furthermore, with the incorporation of the IoT into Critical Infrastructure (CI) hardware and applications, the protection of not only the systems but the citizens themselves has become paramount. To do so, specialists must be able to gain a foothold in the ongoing cyber attack war-zone. By organising the various attacks against their systems, these specialists can not only gain a quick overview of what they might expect but also gain knowledge into the specifications of the attacks based on the categorisation method used. This paper presents a glimpse into the area of IoT Critical Infrastructure security as well as an overview and analysis of attack categorisation methodologies in the context of wireless IoT-based Critical Infrastructure applications. We believe this can be a guide to aid further researchers in their choice of adapted categorisation approaches. Indeed, adapting appropriated categorisation leads to a quicker attack detection, identification, and recovery. It is, thus, paramount to have a clear vision of the threat landscapes of a specific system.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
David Gil ◽  
Magnus Johnsson ◽  
Higinio Mora ◽  
Julian Szymański

There is a growing awareness that the complexity of managing Big Data is one of the main challenges in the developing field of the Internet of Things (IoT). Complexity arises from several aspects of the Big Data life cycle, such as gathering data, storing them onto cloud servers, cleaning and integrating the data, a process involving the last advances in ontologies, such as Extensible Markup Language (XML) and Resource Description Framework (RDF), and the application of machine learning methods to carry out classifications, predictions, and visualizations. In this review, the state of the art of all the aforementioned aspects of Big Data in the context of the Internet of Things is exposed. The most novel technologies in machine learning, deep learning, and data mining on Big Data are discussed as well. Finally, we also point the reader to the state-of-the-art literature for further in-depth studies, and we present the major trends for the future.


Author(s):  
Jalindar Karande ◽  
Sarang Joshi

The internet of things (IoT) is used in domestic, industrial as well as mission-critical systems including homes, transports, power plants, industrial manufacturing and health-care applications. Security of data generated by such systems and IoT systems itself is very critical in such applications. Early detection of any attack targeting IoT system is necessary to minimize the damage. This paper reviews security attack detection methods for IoT Infrastructure presented in the state-of-the-art. One of the major entry points for attacks in IoT system is topology exploitation. This paper proposes a distributed algorithm for early detection of such attacks with the help of predictive descriptor tables. This paper also presents feature selection from topology control packet fields. The performance of the proposed algorithm is evaluated using an extensive simulation carried out in OMNeT++. Performance parameter includes accuracy and time required for detection. Simulation results presented in this paper show that the proposed algorithm is effective in detecting attacks ahead in time.


2020 ◽  
pp. 1-12
Author(s):  
Zhang Caiqian ◽  
Zhang Xincheng

The existing stand-alone multimedia machines and online multimedia machines in the market have certain deficiencies, so they cannot meet the actual needs. Based on this, this research combines the actual needs to design and implement a multi-media system based on the Internet of Things and cloud service platform. Moreover, through in-depth research on the MQTT protocol, this study proposes a message encryption verification scheme for the MQTT protocol, which can solve the problem of low message security in the Internet of Things communication to a certain extent. In addition, through research on the fusion technology of the Internet of Things and artificial intelligence, this research designs scheme to provide a LightGBM intelligent prediction module interface, MQTT message middleware, device management system, intelligent prediction and push interface for the cloud platform. Finally, this research completes the design and implementation of the cloud platform and tests the function and performance of the built multimedia system database. The research results show that the multimedia database constructed in this paper has good performance.


This paper presents the design of 2*1 and 4*1 RFID reader microstrip array antenna at 2.4GHz for the Internet of things (IoT) networks which are Zigbee, Bluetooth and WIFI. The proposed antenna is composed of identical circular shapes radiating patches printed in FR4 substrate. The dielectric constant εr and substrate thickness h are 4.4 and 1.6mm, respectively. The 2*1 and 4*1 array antennas present a gain improvement of 27.3% and 61.9%, respectively. The single,2*1 and 4*1 array antennas were performed with CADFEKO.


Automatic environmental monitoring is a field that encompasses several scientific practices for the assessment of risks that may negatively impact a given environment, such as the forest. A forest is a natural environment that hosts various forms of plant and animal life, so preserving the forest is a top priority. To this end, the authors of this paper will focus on the development of an intelligent system for the early detection of forest fires, based on an IoT solution. This latter will thus facilitate the exploitation of the functionalities offered by the Cloud and mobile applications. Detecting and predicting forest fires with accuracy is a difficult task that requires machine learning and an in-depth analysis of environmental conditions. This leads the authors to adopt the forward neural network algorithm by highlighting its contribution through real experiments, performed on the prototype developed in this paper.


i-com ◽  
2017 ◽  
Vol 16 (2) ◽  
pp. 181-193 ◽  
Author(s):  
Christian Reuter ◽  
Katja Pätsch ◽  
Elena Runft

AbstractThe Internet and especially social media are not only used for supposedly good purposes. For example, the recruitment of new members and the dissemination of ideologies of terrorism also takes place in the media. However, the fight against terrorism also makes use of the same tools. The type of these countermeasures, as well as the methods, are covered in this work. In the first part, the state of the art is summarized. The second part presents an explorative empirical study of the fight against terrorism in social media, especially on Twitter. Different, preferably characteristic forms are structured within the scope with the example of Twitter. The aim of this work is to approach this highly relevant subject with the goal of peace, safety and safety from the perspective of information systems. Moreover, it should serve following researches in this field as basis and starting point.


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