Design and implementation of automated IoT security testbed

The concept of Internet of Things (IoT) was designed to change everyday lives of people via multiple forms of computing and easy deployment of applications. In recent years, the increasing complexity of IoT-ready devices and processes has led to potential risks related to system reliability. Therefore, the comprehensive testing of IoT technology has attracted the attention of many researchers, which promotes the extensive development of IoT testing methods and infrastructure. However, the current research on IoT testing methods and testbeds mainly focuses on specific application scenarios, lacking systematic review and analysis of many applications from different points of view. This paper systematically summarizes the latest testing methods covering different IoT fields and discusses the development status of the existing Internet of things testbed. Findings of this review demonstrate that IoT testing is moving toward larger scale and intelligent testing, and that in near future, IoT test architecture is set to become more standardized and universally applicable with multi-technology convergence—i.e., a combination of big data, cloud computing, and artificial intelligence—being the prime focus of IoT testing.

Survey of Testing Methods and Testbed Development Concerning Internet of Things

The research study provides a comprehensive bibliometric assessment in the field of Software Testing (ST). The dynamic evolution in the field of ST is evident from the publication rate over the last six years. The research study is carried out to provide insight into the field of ST from various research bibliometric aspects. Our methodological approach includes dividing the six-year time frame into the set of two symmetric but different periods (2016–2018) and (2019–2021) comprising a total of 75,098 records. VOSViewer is used to perform analysis with respect to collaboration network of countries and co-word assessment. Bibliometrix (Studio R) analysis tool is used to evaluate research themes/topics. The year 2019 leads the publication rate whereas a decrement in publication frequency is observed for the years 2020 and 2021. Our research study shows the influence of ST in other research domains as depicted in different research areas. Especially the impact of ST in the Electrical and Electronics Domain is quite notable. Most of the research publications are from the USA and China as they are among the most resourceful countries. On the whole, the majority of the publications are from Asian countries. Collaboration networks amongst countries demonstrate the fact that the higher the collaboration network, the greater would be the research output. Co-word analysis presents the relatedness of documents based on the keywords. The topic dendrogram is generated based on the identified research themes. Although English is the leading language, prominent studies are present in other languages also. This research study provides a comprehensive analysis based on 12 informative research questions

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A Comprehensive Bibliometric Assessment on Software Testing (2016–2021)

In recent years, different variants of the botnet are targeting government, private organizations and there is a crucial need to develop a robust framework for securing the IoT (Internet of Things) network. In this paper, a Hadoop based framework is proposed to identify the malicious IoT traffic using a modified Tomek-link under-sampling integrated with automated Hyper-parameter tuning of machine learning classifiers. The novelty of this paper is to utilize a big data platform for benchmark IoT datasets to minimize computational time. The IoT benchmark datasets are loaded in the Hadoop Distributed File System (HDFS) environment. Three machine learning approaches namely naive Bayes (NB), K-nearest neighbor (KNN), and support vector machine (SVM) are used for categorizing IoT traffic. Artificial immune network optimization is deployed during cross-validation to obtain the best classifier parameters. Experimental analysis is performed on the Hadoop platform. The average accuracy of 99% and 90% is obtained for BoT_IoT and ToN_IoT datasets. The accuracy difference in ToN-IoT dataset is due to the huge number of data samples captured at the edge layer and fog layer. However, in BoT-IoT dataset only 5% of the training and test samples from the complete dataset are considered for experimental analysis as released by the dataset developers. The overall accuracy is improved by 19% in comparison with state-of-the-art techniques. The computational times for the huge datasets are reduced by 3–4 hours through Map Reduce in HDFS.

A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

Internet of Things (IoT) systems are fast evolving nowadays, in which huge amounts of data are produced rapidly from heterogeneous sources. The nature of IoT-based systems implies many challenges, in terms of operation, security, quality control and data management. Thus, testing such systems is a key element to their success. In this paper, we present a comprehensive study for the main testing techniques and tools that have been considered for the IoT-based systems. Detailed comparison and analytical criticism are conducted, identifying the different testing types that have been applied for the main application domains. The research gaps are addressed, which highlight the future directions that can be adopted.

Testing Techniques in IoT-based Systems

As well as the important industry 4.0 revolution, based on of the serves that IoT provides in our society in different fields such as smart building, factory, mobility, health care. Previous studies explore various technology solution to find the best technique to test IoT applications in order to ensure quality for IoT devices. The paper surveys diverse of aspects of multiples software testing and tools for loT devices, and provides details in use case testing for IoT environment and test different accepts such as usability, security, connectivity. The paper considers various security requirement and challenges an unveils different research problems in testing IoT applications and proposes multiple tools and software techniques that help to enhance IoT applications quality

Ghadeer Murad

Papers

Software Testing Techniques in IoT