Using Machine Learning on Testing IoT Applications: a systematic mapping
Resumo
Internet of Things (IoT) devices are increasingly present in people’s daily lives. Thus has increased research interest in investigating strategies that can ensure that these applications work as expected considering specific and vital characteristics of IoT, for example, security, performance and interoperability. In a testing point of view, there is a need to optimize and define an efficient strategy, from its planning to its execution. Considering all the steps that can be taken to test an IoT application, this process, if performed manually, can demand great effort and time. Machine learning (ML) algorithms have been applied in several areas of computing in order to optimize and automate processes that involve large volumes of data. In this paper, we present a systematic mapping resulting in 40 studies that highlights techniques or approaches that use machine learning algorithms for the most diverse goals within the IoT application testing process, such as the use of neural networks for predicting the cost of time in the preparation and execution of tests; identification of security attacks; and automatic generation of test cases from textual language. We also identified that the vast majority of testing techniques are focused on a specific IoT characteristic (e.g., security, performance), specially security, and apply the machine learning algorithm in two ways: directly in the algorithm, called predictive maintenance, or during the execution of planned tests, both of them bring difficulties related to extracting and defining data to train ML algorithms.
Palavras-chave:
Software Testing, Machine Learning, Internet of Things, Systematic Mapping
Referências
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Serkan Ayvaz and Koray Alpay. 2021. Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications 173 (2021), 114598. https://doi.org/10.1016/j.eswa.2021.114598
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Mariela Cortés, Raphael Saraiva, Marcia Souza, Patricia Mello, and Pamella Soares. 2019. Adoption of software testing in internet of things: A systematic literature mapping. In Proceedings of the IV Brazilian Symposium on Systematic and Automated Software Testing. 3–11. https://doi.org/10.1145/3356317.3356326
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Fabrizio De Vita, Dario Bruneo, and Sajal K Das. 2020. On the use of a full stack hardware/software infrastructure for sensor data fusion and fault prediction in industry 4.0. Pattern Recognition Letters 138 (2020), 30–37. https://doi.org/10.1016/j.patrec.2020.06.028
Vasily Desnitsky, Andrey Chechulin, and Igor Kotenko. 2022. Multi-Aspect Based Approach to Attack Detection in IoT Clouds. Sensors 22, 5 (2022), 1831. https://doi.org/10.3390/s22051831
Vinicius HS Durelli, Rafael S Durelli, Simone S Borges, Andre T Endo, Marcelo M Eler, Diego RC Dias, and Marcelo P Guimarães. 2019. Machine learning applied to software testing: A systematic mapping study. IEEE Transactions on Reliability 68, 3 (2019), 1189–1212. https://doi.org/10.1109/TR.2019.2892517
Thomas Fehlmann and Eberhard Kranich. 2017. Autonomous real-time software & systems testing. In Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement. 54–63. https://doi.org/10.1145/3143434.3143444
Peter Flach. 2012. Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press.
Anna Katrina Gomez and SimiKamini Bajaj. 2019. Challenges of testing complex Internet of Things (IoT) devices and systems. In 2019 11th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 1–4. https://doi.org/10.1109/KSE.2019.8919324
VVF Grubisic, JPF Aguiar, and Z Simeu-Abazi. 2020. A Review on Intelligent Predictive Maintenance: Bibliometric analysis and new research directions. In 2020 International Conference on Control, Automation and Diagnosis (ICCAD). IEEE, 1–6. https://doi.org/10.1109/ICCAD49821.2020.9260504
Eman Hammad, Abhijit Kumar Nag, Anitha Chennamaneni, Mohsen Aghashahi, and Erdogan Dogdu. 2021. A Deep-Defense Approach for Next-Gen Cyber-Resilient Inter-Dependent Critical Infrastructure Systems. In 2021 Resilience Week (RWS). IEEE, 1–7. https://doi.org/10.1109/RWS52686.2021.9611790
Sohaib Hanif, Tuba Ilyas, and Muhammad Zeeshan. 2019. Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset. In 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT). IEEE, 152–156. https://doi.org/10.1109/HONET.2019.8908122
Kuo-Kai Hsieh, Li-C Wang, Wen Chen, and Jayanta Bhadra. 2017. Learning to produce direct tests for security verification using constrained process discovery. In 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC). IEEE, 1–6. https://doi.org/10.1145/3061639.3062271
Faisal Jamil, Dohyeun Kim, et al. 2021. An Ensemble of a Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments. Sustainability 13, 18 (2021), 10057. https://doi.org/10.3390/su131810057
Kyeong-Joong Jeong, Jin-Duk Park, Kyusoon Hwang, Seong-Lyun Kim, and Won-Yong Shin. 2022. Two-Stage Deep Anomaly Detection With Heterogeneous Time Series Data. IEEE Access 10 (2022), 13704–13714. https://doi.org/10.1109/ACCESS.2022.3147188
Maxim Kalinin and Peter Zegzhda. 2020. AI-based Security for the Smart Networks. In 13th International Conference on Security of Information and Networks. 1–4. https://doi.org/10.1145/3433174.3433593
Kavin Kamaraj, Behnam Dezfouli, and Yuhong Liu. 2019. Edge mining on iot devices using anomaly detection. In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 33–40. https://doi.org/10.1109/APSIPAASC47483.2019.9023207
Jalindar Karande and Sarang Joshi. 2021. DEDA: An algorithm for early detection of topology attacks in the internet of things. International Journal of Electrical and Computer Engineering 11, 2 (2021), 1761. https://doi.org/10.11591/ijece.v11i2.pp1761-1770
Barbara Kitchenham and Stuart Charters. 2007. Guidelines for performing systematic literature reviews in software engineering. (2007).
Vasiliy Krundyshev. 2020. Neural network approach to assessing cybersecurity risks in large-scale dynamic networks. In 13th International Conference on Security of Information and Networks. 1–8. https://doi.org/10.1145/3433174.3433603
Chu Luo, Jorge Goncalves, Eduardo Velloso, and Vassilis Kostakos. 2020. A survey of context simulation for testing mobile context-aware applications. ACM Computing Surveys (CSUR) 53, 1 (2020), 1–39. https://doi.org/10.1145/3372788
S Manimurugan. 2021. IoT-Fog-Cloud model for anomaly detection using improved Naïve Bayes and principal component analysis. Journal of Ambient Intelligence and Humanized Computing (2021), 1–10. https://doi.org/10.1007/s12652-020-02723-3
Noha Medhat, Sherin M Moussa, Nagwa Lotfy Badr, and Mohamed F Tolba. 2020. A Framework for Continuous Regression and Integration Testing in IoT Systems Based on Deep Learning and Search-Based Techniques. IEEE Access 8 (2020), 215716–215726. https://doi.org/10.1109/ACCESS.2020.3039931
Noha Medhat Mohamed, Sherin Moussa, Nagwa Badr, and Mohamed Tolba. 2021. Enhancing Test Cases Prioritization for Internet of Things based systems using Search-based Technique. International Journal of Intelligent Computing and Information Sciences 21, 1 (2021), 84–94. https://doi.org/10.21608/ijicis.2021.69462.1076
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2018. Foundations of machine learning. MIT press.
W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. 2019. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences 116, 44 (2019), 22071–22080. https://doi.org/10.1073/pnas.1900654116
TD Ovasapyan, VD Danilov, and Dmitry A Moskvin. 2021. Application of synthetic data generation methods to the detection of network attacks on internet of things devices. Automatic Control and Computer Sciences 55, 8 (2021), 991–998. https://doi.org/10.3103/S0146411621080241
Keyur K Patel, Sunil M Patel, et al. 2016. Internet of things-IOT: definition, characteristics, architecture, enabling technologies, application & future challenges. International journal of engineering science and computing 6, 5 (2016). https://doi.org/10.4010/2016.1482
Ivan Porres, Tanwir Ahmad, Hergys Rexha, Sébastien Lafond, and Dragos Truscan. 2020. Automatic exploratory performance testing using a discriminator neural network. In 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 105–113. https://doi.org/10.1109/ICSTW50294.2020.00030
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Publicado
07/11/2022
Como Citar
FREITAS, Lavínia; LELLI, Valéria.
Using Machine Learning on Testing IoT Applications: a systematic mapping. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 28. , 2022, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 373-383.
