Internet Of Things Cybersecurity Research Articles

An Integral Pedagogical Strategy for Teaching and Learning IoT Cybersecurity.

Internet of Things (IoT) has become a fundamental content of any engineering program due to the emerging need of experts in this field. However, the complexity of technologies that interact in IoT environments and the amount of different professional profiles required to design, implement and manage IoT environments, considering cybersecurity as a must, has led to a huge challenge in the educational world. This paper proposes an integral pedagogical strategy for learning IoT cybersecurity structured in three different stages, in a higher education institution. These stages focus not only on the content about IoT and cybersecurity but also on the competencies to acquire, the most suitable learning methodologies and the expected learning outcomes. The association of these concepts in each stage is detailed. Examples of courses are explained, the related competencies and learning outcomes are specified, and the contents and methodologies to achieve the expected results are described. An analysis of student results and stakeholder evaluations is provided to verify if the pedagogical strategy proposed is suitable. Furthermore, students’ feedback is included to corroborate the innovation, the suitability of the acquired skills, and the overall student satisfaction with the related courses and consequently with the proposed IoT cybersecurity pedagogical strategy.

Teaching and Learning IoT Cybersecurity andVulnerability Assessment with Shodan through Practical Use Cases.

Shodan is a search engine for exploring the Internet and thus finding connected devices. Its main use is to provide a tool for cybersecurity researchers and developers to detect vulnerable Internet-connected devices without scanning them directly. Due to its features, Shodan can be used for performing cybersecurity audits on Internet of Things (IoT) systems and devices used in applications that require to be connected to the Internet. The tool allows for detecting IoT device vulnerabilities that are related to two common cybersecurity problems in IoT: the implementation of weak security mechanisms and the lack of a proper security configuration. To tackle these issues, this article describes how Shodan can be used to perform audits and thus detect potential IoT-device vulnerabilities. For such a purpose, a use case-based methodology is proposed to teach students and users to carry out such audits and then make more secure the detected exploitable IoT devices. Moreover, this work details how to automate IoT-device vulnerability assessments through Shodan scripts. Thus, this article provides an introductory practical guide to IoT cybersecurity assessment and exploitation with Shodan.

A Comprehensive Study of the IoT Cybersecurity in Smart Cities

Smart cities exploit emerging technologies such as Big Data, the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) to enhance public services management. The use of IoT allows detecting and reporting specific parameters related to different domains of the city, such as health, waste management, agriculture, transportation, and energy. LoRa technologies, for instance, are used to develop IoT solutions for several smart city domains thanks to its available features, but sometimes people (i.e., citizens, information technology administrators, or city managers) might think that these available features involve cybersecurity risks. This study explores the cybersecurity aspects that define an assessment model of cybersecurity maturity of IoT solutions to develop smart city applications. In that sense, we perform a systematic literature review based on a top-down approach of cybersecurity incident response in IoT ecosystems. Besides, we propose and validate a model based on risk levels to evaluate the IoT cybersecurity maturity in a smart city.

On data-driven curation, learning, and analysis for inferring evolving internet-of-Things (IoT) botnets in the wild

The insecurity of the Internet-of-Things (IoT) paradigm continues to wreak havoc in consumer and critical infrastructures. The highly heterogeneous nature of IoT devices and their widespread deployments has led to the rise of several key security and measurement-based challenges, significantly crippling the process of collecting, analyzing and correlating IoT-centric data. To this end, this paper explores macroscopic, passive empirical data to shed light on this evolving threat phenomena. The proposed work aims to classify and infer Internet-scale compromised IoT devices by solely observing one-way network traffic, while also uncovering, reporting and thoroughly analyzing “in the wild” IoT botnets. To prepare a relevant dataset, a novel probabilistic model is developed to cleanse unrelated traffic by removing noise samples (i.e., misconfigured network traffic). Subsequently, several shallow and deep learning models are evaluated in an effort to train an effective multi-window convolutional neural network. By leveraging active and passing measurements when generating the training dataset, the neural network aims to accurately identify compromised IoT devices. Consequently, to infer orchestrated and unsolicited activities that have been generated by well-coordinated IoT botnets, hierarchical agglomerative clustering is employed by scrutinizing a set of innovative and efficient network feature sets. Analyzing 3.6 TB of recently captured darknet traffic revealed a momentous 440,000 compromised IoT devices and generated evidence-based artifacts related to 350 IoT botnets. Moreover, by conducting thorough analysis of such inferred campaigns, we reveal their scanning behaviors, packet inter-arrival times, employed rates and geo-distributions. Although several campaigns exhibit significant differences in these aspects, some are more distinguishable; by being limited to specific geo-locations or by executing scans on random ports besides their core targets. While many of the inferred botnets belong to previously documented campaigns such as Hide and Seek, Hajime and Fbot, newly discovered events portray the evolving nature of such IoT threat phenomena by demonstrating growing cryptojacking capabilities or by targeting industrial control services. To motivate empirical (and operational) IoT cyber security initiatives as well as aid in reproducibility of the obtained results, we make the source codes of all the developed methods and techniques available to the research community at large.

Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics

As an emerging technology, the Internet of Things (IoT) revolutionized the global network comprising of people, smart devices, intelligent objects, data, and information. The development of IoT is still in its infancy and many related issues need to be solved. IoT is a unified concept of embedding everything. IoT has a great chance to make the world a higher level of accessibility, integrity, availability, scalability, confidentiality, and interoperability. However, how to protect IoT is a challenging task. System security is the foundation for the development of IoT. This article systematically reviews IoT cybersecurity. The key considerations are the protection and integration of heterogeneous smart devices and information communication technologies (ICT). This review provides useful information and insights to researchers and practitioners who are interested in cybersecurity of IoT, including the current research of IoT cybersecurity, IoT cybersecurity architecture and taxonomy, key enabling countermeasures and strategies, major applications in industries, research trends and challenges.

Analysis of Botnet Domain Names for IoT Cybersecurity

Botnets are widespread nowadays with the expansion of the Internet and commonly occur in many cyber-attacks, resulting in serious threats to network services and users' properties. With the rapid development of the Internet of Things (IoT) applications, the botnet can easily make use of IoT devices for larger-scale attacks. Domain name system (DNS) is widely used by the botnet to establish the connection between bots and their corresponding command-and-control (C&C). In order to avoid the track of the C&C through the DNS information, some sophisticated schemes are used by the botnet and fast-flux is a typical one. In this paper, the activities of Rustock botnet domain names which just use the fast-flux as the connection method between bots and C&C, are deeply analyzed from multiple aspects. Besides, we extract 32 special features of Rustock domain named querying traffic. Then multiple popular classifiers are adopted in order to pick the malicious domain names out from the DNS traffic using those 32 features. The work of this paper aims to provide guidance for future botnet detection based on real statics and experiments.

A framework for Internet of Things-enabled smart government: A case of IoT cybersecurity policies and use cases in U.S. federal government

Internet of Things (IoT) is ubiquitous in society. IoT-enabled dynamic capabilities in real-time sensing and responding can spur digital transformation in unlocking the potential of digital government into data-driven smart government capable of delivering policies and services of public interest and public value. However, the literature indicates challenges in IoT cybersecurity and systemic use across the government. There is the urgent need for IoT research on policy and use. This paper developed a framework for IoT-enabled smart government performance. We applied this framework to conduct case study analyses of digital technology policy, IoT cybersecurity policy, and IoT use in major application domains at the U.S. federal government level. The results show that some agencies were strategic and forward-thinking in funding and partnering with sub-national governments in promoting the IoT use. However, there remains a critical need for national IoT policies to promote systemic IoT use across the application domains.

An Ontology-Based Cybersecurity Framework for the Internet of Things.

The use of sensors and actuators as a form of controlling cyber-physical systems in resource networks has been integrated and referred to as the Internet of Things (IoT). However, the connectivity of many stand-alone IoT systems through the Internet introduces numerous cybersecurity challenges as sensitive information is prone to be exposed to malicious users. This paper focuses on the improvement of IoT cybersecurity from an ontological analysis, proposing appropriate security services adapted to the threats. The authors propose an ontology-based cybersecurity framework using knowledge reasoning for IoT, composed of two approaches: (1) design time, which provides a dynamic method to build security services through the application of a model-driven methodology considering the existing enterprise processes; and (2) run time, which involves monitoring the IoT environment, classifying threats and vulnerabilities, and actuating in the environment ensuring the correct adaptation of the existing services. Two validation approaches demonstrate the feasibility of our concept. This entails an ontology assessment and a case study with an industrial implementation.