Text Message Classification Using Supervised Machine Learning Algorithms

Merugu, Suresh; Reddy, M. Chandra Shekhar; Goyal, Ekansh; Piplani, Lakshay

doi:10.1007/978-981-13-0212-1_15

Cited by 32 publications

(5 citation statements)

References 7 publications

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“…These integrations could improve model performance and adaptability, particularly to address evolving malware threats. Furthermore, exploring ensemble techniques, transfer learning approaches, or other ML approaches tested in different fields could be valuable in improving model generalization and scalability [15][16][17][18]. These avenues offer promising directions for extending the capabilities and robustness of malware detection systems.…”

Section: Accuracymentioning

confidence: 99%

Advanced Android Malware Detection through Deep Learning Optimization

Alhussen

2024

Eng. Technol. Appl. Sci. Res.

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Android stands out as one of the most prevalent mobile operating systems globally, due to its widespread adoption and open-source nature. However, its susceptibility to malware attacks, facilitated by the ability to install third-party applications without centralized control, poses significant security challenges. Despite efforts to integrate security measures, the proliferation of malicious activities and vulnerabilities emphasizes the need for advanced detection techniques. This study implemented and optimized Long Short-Term Memory (LSTM) and Neural Network (NN) models for malware detection on the Android platform. Leveraging meticulous hyperparameter tuning and robust data preprocessing techniques, this study aimed to increase the efficacy of LSTM and NN models in identifying and mitigating various forms of malware. The results demonstrate remarkable performance, with the LSTM model achieving an accuracy of 99.24%, precision of 99.07%, recall of 98.79%, and F1-score of 98.93%, and the NN model attaining an accuracy of 99.18%, precision of 99.02%, recall of 98.84%, and F1-score of 98.93%. By addressing these challenges and achieving such high levels of accuracy and effectiveness, this study contributes significantly to the ongoing endeavor to fortify defenses against cyber threats, thus fostering a safer digital environment for users worldwide.

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Section: Accuracymentioning

confidence: 99%

Advanced Android Malware Detection through Deep Learning Optimization

Alhussen

2024

Eng. Technol. Appl. Sci. Res.

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“…The major drawback of the system is Global positioning system (GPS), the user position information will be investigated in the future. Piyush Chanana, Rohan Paul, M Balakrishnan and PVM Rao [9] made a comprehensive study on various assistive technology solutions available for Visually Impaired people and the study revealed that most of the assistive technologies already present provide limited solutions to the user needs. Most of them focused on technology and not on user needs.…”

Section: Portable Electronic Travel Toolsmentioning

confidence: 99%

A Review of Some Assistive Tools and their Limitations for Visually Impaired

Merugu¹,

Ghinea²,

Shaik³

et al. 2022

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World report on Vision by World Health Organization reported that there are at least 2.2 Billion people are Visually Impaired. "The National Blindness & Visual Impairment Survey India 2015-2019" survey report by Government of India reported that the overall estimated prevalence of the Blindness in age 50 or more is 1.99% and the overall estimated prevalence of Visually Impaired in age 50 or more is 13.76%. The above numbers urge researchers to develop advanced assistive tools for Blind and Visually Impaired. There have been several assistive tools already designed to assist and enhance the quality of life to Blind and Visually Impaired. But most of them are limited and confined to specific problems. In this paper, a systematic review of some of the assistive tools for Blind and Visually Impaired is done. The detailed review of existing assistive tools reveals that the factors like user-friendly, ensuring safety, independent mobility, multiple features and user-centric design need priority consideration while designing such tools.

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“…They performed data preprocessing, then they selected features using Infinite Latent Feature Selection (ILFS). Finally, they classified emails with an accuracy of 95.45% using Random Forest, while the remaining classifiers: Artificial Neural Network, Logistic Regression, Support Vector Machine, Random Tree, K-Nearest Neighbors, Decision Merugu et al [15] classified text messages into Spam and Ham category with an accuracy rate of 97.6% using Naive Bayes, which proved to outperform other machine learning algorithms such as Random Forest, Support Vector Machine and K-Nearest Neighbors according to the experimental results. The messages were collected from the UCI repository.…”

Section: Related Workmentioning

confidence: 99%

Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text

Shaaban,

Hassan,

Guirguis

2021

Preprint

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The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people's fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.

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Text Message Classification Using Supervised Machine Learning Algorithms

Cited by 32 publications

References 7 publications

Advanced Android Malware Detection through Deep Learning Optimization

Advanced Android Malware Detection through Deep Learning Optimization

A Review of Some Assistive Tools and their Limitations for Visually Impaired

Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text

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