Islam Zada^1,*, Mohammed Naif Alatawi², Syed Muhammad Saqlain¹, Abdullah Alshahrani³, Adel Alshamran⁴, Kanwal Imran⁵, Hessa Alfraihi⁶

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835 - 15 August 2024

Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >

Shahid Mohammad Ganie¹, Pijush Kanti Dutta Pramanik², Majid Bashir Malik³, Anand Nayyar⁴, Kyung Sup Kwak^5,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3993-4006, 2023, DOI:10.32604/csse.2023.035244 - 03 April 2023

Abstract Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and… More >

Smita Khade¹, Shilpa Gite^1,2,*, Sudeep D. Thepade³, Biswajeet Pradhan^4,5,*, Abdullah Alamri⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 323-345, 2023, DOI:10.32604/cmes.2023.023674 - 05 January 2023

Abstract Contactless verification is possible with iris biometric identification, which helps prevent infections like COVID-19 from spreading. Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses, replayed the video, and print attacks. The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness. Seven assorted feature creation ways are studied in the presented solutions, and these created features are explored for the training of eight distinct… More > Graphic Abstract

V. Joseph Raymond^1,2,*, R. Jeberson Retna Raj¹

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2147-2163, 2023, DOI:10.32604/csse.2023.028227 - 01 August 2022

Abstract Android devices are popularly available in the commercial market at different price levels for various levels of customers. The Android stack is more vulnerable compared to other platforms because of its open-source nature. There are many android malware detection techniques available to exploit the source code and find associated components during execution time. To obtain a better result we create a hybrid technique merging static and dynamic processes. In this paper, in the first part, we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid… More >

Attia Qammar¹, Ahmad Karim^1,*, Yasser Alharbi², Mohammad Alsaffar², Abdullah Alharbi²

Computer Systems Science and Engineering, Vol.43, No.3, pp. 915-930, 2022, DOI:10.32604/csse.2022.023652 - 09 May 2022

Abstract Smartphone devices particularly Android devices are in use by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks launched the Distributed denial of services (DDoS) that causes to steal of sensitive data, remote access, and spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic analysis. In this paper, a novel hybrid model, the combination More >

Jabeen Sultana^1,*, Anjani Kumar Singha², Shams Tabrez Siddiqui³, Guthikonda Nagalaxmi⁴, Anil Kumar Sriram⁵, Nitish Pathak⁶

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1007-1024, 2022, DOI:10.32604/iasc.2022.021507 - 17 November 2021

Abstract COVID-19 is a novel virus that spreads in multiple chains from one person to the next. When a person is infected with this virus, they experience respiratory problems as well as rise in body temperature. Heavy breathlessness is the most severe sign of this COVID-19, which can lead to serious illness in some people. However, not everyone who has been infected with this virus will experience the same symptoms. Some people develop cold and cough, while others suffer from severe headaches and fatigue. This virus freezes the entire world as each country is fighting against… More >

Muhammad Zubair Asghar¹, Fazli Subhan², Muhammad Imran¹, Fazal Masud Kundi¹, Adil Khan³, Shahboddin Shamshirband^{4, 5, *}, Amir Mosavi^{6, 7, 8}, Peter Csiba⁸, Annamaria R. Varkonyi Koczy⁸

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1093-1118, 2020, DOI:10.32604/cmc.2020.07709 - 30 April 2020

Abstract Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show More >