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

helix

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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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“…But, the accuracy is not enhanced and the training time is too minimized. Supervised technique [11,25] provides various resources that help the research into the automating IoT-oriented cyber-attack detection and the malicious…”

Section: Problem Statementmentioning

confidence: 99%

Designing Framework for Intrusion Detection in IoT Based on Spotted Hyena-Based ANN

Bathula

Muhuri

Merugu

et al. 2021

Lecture Notes in Electrical Engineering

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Internet of Things (IoT) is the platform for resource sharing amidst the computing platforms, mainly memory, power, large data, and storage. Securing the data in the IoT has become a prime concern as the hackers can access through the invaluable information from the IoT database. Once the attacks are incurred on the IoT environment, the loss of data is inevitable, and it can have a major effect on the progress of the IoT platform. In this paper, an Intrusion Detection System (IDS) with improved artificial intelligence is proposed. This paper makes use of standard benchmark datasets from diverse sources for performing the experiment. A wellperforming machine learning algorithm called Artificial Neural Network (ANN) is developed with improved network architecture. The usage of a renowned metaheuristic algorithm called Spotted Hyena Optimization (SHO) is used for selecting the optimal hidden neurons for ANN. The main objective of the improved training is to reduce the error difference between the target and the measured outputs to enhance the detection accuracy. Finally, the experimental outcomes and simulations prove the stability and robustness of the proposed model in terms of a variety of performance metrics over other machine learning models.

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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

Designing Framework for Intrusion Detection in IoT Based on Spotted Hyena-Based ANN

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