IoT-Based Water Quality Monitoring System Using LoRaWAN

Liloja,; Sreelekha, M.G.; Gopakumar, Ganesh; Shahil, K.

doi:10.1007/978-981-15-9647-6_92

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…M. El-Shrkaway et al [8] proposed the Multi-layer Feature Selection and Reduction IDS (MFSR-IDS) algorithm to provide a high level of protection against DoS and Probe attacks using an anomaly-based approach. Liloja et al [11] developed an effective intrusion detector using both a deep learning model and a machine learning model. To obtain high-accuracy scores, a deep learning model was used in the process of extracting features, and a machine learning model was used to measure the intrusion detection performance of intrusion detection.…”

Section: Ml-based Intrusion Detectionmentioning

confidence: 99%

“…To address this problem, researchers have leveraged machine learning algorithms on intrusion detection systems to analyze incoming network traffic to identify suspicious or anomalous activities in IoT environments [8][9][10][11][12]. Based on the labeled data categorizing packets as either benign or malicious, these models acquire the ability to discern patterns associated with different types of attacks.…”

Section: Introductionmentioning

confidence: 99%

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A Transferable Deep Learning Framework for Improving the Accuracy of Internet of Things Intrusion Detection

Kim,

Park,

Hong

et al. 2024

Future Internet

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As the size of the IoT solutions and services market proliferates, industrial fields utilizing IoT devices are also diversifying. However, the proliferation of IoT devices, often intertwined with users’ personal information and privacy, has led to a continuous surge in attacks targeting these devices. However, conventional network-level intrusion detection systems with pre-defined rulesets are gradually losing their efficacy due to the heterogeneous environments of IoT ecosystems. To address such security concerns, researchers have utilized ML-based network-level intrusion detection techniques. Specifically, transfer learning has been dedicated to identifying unforeseen malicious traffic in IoT environments based on knowledge distillation from the rich source domain data sets. Nevertheless, since most IoT devices operate in heterogeneous but small-scale environments, such as home networks, selecting adequate source domains for learning proves challenging. This paper introduces a framework designed to tackle this issue. In instances where assessing an adequate data set through pre-learning using transfer learning is non-trivial, our proposed framework advocates the selection of a data set as the source domain for transfer learning. This selection process aims to determine the appropriateness of implementing transfer learning, offering the best practice in such scenarios. Our evaluation demonstrates that the proposed framework successfully chooses a fitting source domain data set, delivering the highest accuracy.

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Section: Ml-based Intrusion Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Transferable Deep Learning Framework for Improving the Accuracy of Internet of Things Intrusion Detection

Kim,

Park,

Hong

et al. 2024

Future Internet

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show abstract

“…The most common application of data mining is classification, in which patterns are detected based on their groups. Neural Networks work on the basis of artificial neurons [15,16]. Each node in the network is given the name neuron, the basic processing unit [17].…”

Section: Proposed Techniquementioning

confidence: 99%

Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning

Selvakumari

2023

RAiSE-2023

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Advanced Medical Nursing Care Unit for Emergency Healthcare Based on the Internet of Things

Patel

Pandey

2023

2023 International Conference on Artificial Intelligence and Smart Communication (AISC)

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IoT-Based Water Quality Monitoring System Using LoRaWAN

Cited by 5 publications

References 10 publications

A Transferable Deep Learning Framework for Improving the Accuracy of Internet of Things Intrusion Detection

A Transferable Deep Learning Framework for Improving the Accuracy of Internet of Things Intrusion Detection

Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning

Advanced Medical Nursing Care Unit for Emergency Healthcare Based on the Internet of Things

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