Abstract
The tremendous growth in the Internet of Things (IoT) creates great potential which provides us with incredible productivity and simplified our daily lives. But, due to resource constraints and computation, IoT networks are vulnerable to a variety of malicious activities. Thus, protecting the network from hostile attacks should be the top priority. This can be done by planning and implementing effective security measures, one of them is an intrusion detection system. It detects harmful activities on the network and monitors network traffic based on that detection. The aim of the intrusion detection system (IDS) is to afford various approaches for detecting the rapidly growing network attacks, as well as to stop the harmful activities that occur in the IoT devices. Various artificial intelligence methods were evaluated and concluded on various datasets, including BoT-IoT, IoT-23, UNSW-NB15, CSE-CIC-IDS2018, and MQTT-IOT-IDS2020, in search of a suitable algorithm that can easily learn the pattern of network attack activities. The feature extraction and pre-processing data were then fed into IDS as data to train the model for future anomaly detection, prediction, and analysis. This study focuses primarily on the various types of cyber-attacks and machine learning algorithms used to identify cyber-attacks.
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Aarthi, G., Sharon Priya, S., Aisha Banu, W. (2023). Comparative Study on Different Intrusion Detection Datasets Using Machine Learning and Deep Learning Algorithms. In: Venkataraman, N., Wang, L., Fernando, X., Zobaa, A.F. (eds) Big Data and Cloud Computing. ICBCC 2022. Lecture Notes in Electrical Engineering, vol 1021. Springer, Singapore. https://doi.org/10.1007/978-981-99-1051-9_8
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