Denoising Latent Representation with SOMs for Unsupervised IoT Malware Detection

Nguyen, Huu Noi; Tran, Nghi H.; Hoang, Tuan-Hao; Cao, Van Loi

doi:10.1007/s42979-022-01344-1

Cited by 4 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These ML/DL algorithms have great potential to reduce WSN congestion, so researchers should pay close attention to them. More information can be found in [97]. The above potential sectors have both more problems and the potential to reduce congestion in IoT networks [98].…”

Section: Unsupervised Learning and Semi-supervised Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

Umar,

Khalid,

Ali

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement.

show abstract

Section: Unsupervised Learning and Semi-supervised Learningmentioning

confidence: 99%

“…(e) It is more difficult to interpret the DL models. (a) In the existing literature DL algorithms applications include LSTM, deep Recurrent Neural Networks (RNNs), deep belief networks, CNNs networks, and Boltzmann machine[97]. (b) Easy to extract the accurate information, accurate information from the complex as well as the raw WSNs data system.…”

mentioning

confidence: 99%

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

Umar,

Khalid,

Ali

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Fränti and Sieranoja apply k‐means to some benchmark clustering data sets, including synthetic data [ 18 ]. Ray et al [ 19 ], Nguyen et al [ 20 ], Ripan et al [ 21 ], Tarekegn et al [ 22 ], and Chen et al [ 23 ] have used various clustering techniques for different applications, and some of them also apply SOM. Townsley et al [ 24 ] and Wang and Zhang [ 25 ] apply machine learning algorithms to medical data sets for classification purposes.…”

Section: Introductionmentioning

confidence: 99%

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

Tripathi

2024

Health Care Science

View full text Add to dashboard Cite

IntroductionData clustering is an important field of machine learning that has applicability in wide areas, like, business analysis, manufacturing, energy, healthcare, traveling, and logistics. A variety of clustering applications have already been developed. Data clustering approaches based on self‐organizing map (SOM) generally use the map dimensions (of the grid) ranging from 2 × 2 to 8 × 8 (4–64 neurons [microclusters]) without any explicit reason for using the particular dimension, and therefore optimized results are not obtained. These algorithms use some secondary approaches to map these microclusters into the lower dimension (actual number of clusters), like, 2, 3, or 4, as the case may be, based on the optimum number of clusters in the specific data set. The secondary approach, observed in most of the works, is not SOM and is an algorithm, like, cut tree or the other.MethodsIn this work, the proposed approach will give an idea of how to select the most optimal higher dimension of SOM for the given data set, and this dimension is again clustered into the lower actual dimension. Primary and secondary, both utilize the SOM to cluster the data and discover that the weight matrix of the SOM is very meaningful. The optimized two‐dimensional configuration of SOM is not the same for every data set, and this work also tries to discover this configuration.ResultsThe adjusted randomized index obtained on the Iris, Wine, Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance, Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134, 0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, which outperforms all other results available on the web and when no reduction of attributes is done in this work.ConclusionsIt is found that SOM is superior to or on par with other clustering approaches, like, k‐means or the other, and could be used successfully to cluster all types of data sets. Ten benchmark data sets from diverse domains like medical, biological, and chemical are tested in this work, including the synthetic data sets.

show abstract

Deep Clustering Based Latent Representation for IoT Malware Detection

Nguyen,

Tran,

Cao

2023

2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS)

View full text Add to dashboard Cite

Denoising Latent Representation with SOMs for Unsupervised IoT Malware Detection

Cited by 4 publications

References 35 publications

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

Deep Clustering Based Latent Representation for IoT Malware Detection

Contact Info

Product

Resources

About