Application of Natural Language Processing for Phishing Detection Using Machine and Deep Learning Models

Villanueva, Alonica; Atibagos, Christian; Guzman, Jericko De; Cruz, John Carlo Dela; Rosales, Menchie; Francisco, Ryan

doi:10.1109/iciss55894.2022.9915037

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…Various methods have been used to eradicate fraud. From the researchers’ point of view, various fraud prediction analyses have been developed to identify elements of fraud, such as email filtering, data intrusion element detection, identification of fake news, fake job ads, fake auctions, fake investments, fake social media dating, and so on ( Yan, Li & He, 2021 ; Raghavan & Gayar, 2019 ; Rezayi et al, 2021 ; Park et al, 2019 ; Samarthrao & Rohokale, 2022 ; Ali et al, 2022 ; Villanueva et al, 2022 ; Prashanth et al, 2022 ; Suarez-Tangil et al, 2020 ; Gowri et al, 2021 ). All these studies use machine learning and data mining as an innovative artificial intelligence method.…”

Section: Related Workmentioning

confidence: 99%

Clustering analysis for classifying fake real estate listings

Mohd Amin,

Sani,

Nasrudin

et al. 2024

PeerJ Computer Science

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With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market.

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Section: Related Workmentioning

confidence: 99%

Clustering analysis for classifying fake real estate listings

Mohd Amin,

Sani,

Nasrudin

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

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“…If the network's number of neurons is less than the ideal, the network will not train appropriately, and the results will be inaccurate [37]. Furthermore, if a large number of neurons is used in comparison to the optimum amount, poor interpolation quality might arise, which is known as an over-fitting problem [38], [39]. The electric supply to the HVAC system is three-phase.…”

Section: Figure 4 Exploratory Data Analysis Of Hvac Datasetsmentioning

confidence: 99%

Prediction of Electric Power Demand of HVACs for Operating Rooms in Case of Dynamic Set Points of Temperature: A Case Study

Ullah

Ahmad

Yasin

et al. 2022

JCBI

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In healthcare settings, particularly in areas such as operating rooms and intensive care units, there is a need for a dynamically controlled temperature environment that can adapt to the changing needs of both patients and healthcare workers. This is due to the fact that the desired temperature can vary depending on the condition of the patient and the specific requirements of surgical and treatment procedures. To address this need, our objective is to develop a tool for predicting the electric power needed to maintain a desired temperature in these critical care areas. Previous research has employed artificial learning algorithms and mathematical equations to predict electric power for various types and sizes of buildings, with promising results. However, our study focuses specifically on critical care areas within hospitals and utilizes fluctuating temperature set-points to predict power demand using historical weather data and Building Management System (BMS) data. We employed both Multi-Layer Artificial Neural Network (ML-ANN) and Long short-term memory (LSTM) models for this purpose and found that ML-ANN outperformed LSTM. The results showed that the ML-ANN model performed better than the LSTM model, with a testing accuracy of 96% compared to 78% for the LSTM model. This indicates that the ML-ANN model was more accurate in predicting the power consumption for the desired temperature in the operating room.

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Enhancing Web Security: Hybrid Deep Learning Model for Robust URL Phishing Detection

Dubey,

Makwana

2024

2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)

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Application of Natural Language Processing for Phishing Detection Using Machine and Deep Learning Models

Cited by 6 publications

References 7 publications

Clustering analysis for classifying fake real estate listings

Clustering analysis for classifying fake real estate listings

Prediction of Electric Power Demand of HVACs for Operating Rooms in Case of Dynamic Set Points of Temperature: A Case Study

Enhancing Web Security: Hybrid Deep Learning Model for Robust URL Phishing Detection

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