Mujahid Ali^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2161-2194, 2024, DOI:10.32604/cmc.2024.058888 - 18 November 2024

Abstract Forecasting travel demand requires a grasp of individual decision-making behavior. However, transport mode choice (TMC) is determined by personal and contextual factors that vary from person to person. Numerous characteristics have a substantial impact on travel behavior (TB), which makes it important to take into account while studying transport options. Traditional statistical techniques frequently presume linear correlations, but real-world data rarely follows these presumptions, which may make it harder to grasp the complex interactions. Thorough systematic review was conducted to examine how machine learning (ML) approaches might successfully capture nonlinear correlations that conventional methods may… More >

Noor Ullah Bacha¹, Songfeng Lu¹, Attiq Ur Rehman¹, Muhammad Idrees², Yazeed Yasin Ghadi³, Tahani Jaser Alahmadi^4,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 707-748, 2024, DOI:10.32604/cmc.2024.054780 - 15 October 2024

Abstract Cross-Site Scripting (XSS) remains a significant threat to web application security, exploiting vulnerabilities to hijack user sessions and steal sensitive data. Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats. This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression (LR), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Deep Neural Networks (DNN). Utilizing the XSS-Attacks-2021 dataset, which comprises 460 instances across various real-world traffic-related scenarios, this framework significantly enhances XSS attack detection. Our approach, which… More >

Ahsan Wajahat¹, Jingsha He¹, Nafei Zhu¹, Tariq Mahmood^2,3, Tanzila Saba², Amjad Rehman Khan², Faten S. Alamri^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 651-673, 2024, DOI:10.32604/cmc.2024.047530 - 25 April 2024

Abstract The growing usage of Android smartphones has led to a significant rise in incidents of Android malware and privacy breaches. This escalating security concern necessitates the development of advanced technologies capable of automatically detecting and mitigating malicious activities in Android applications (apps). Such technologies are crucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world. Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitations they require substantial computational resources and are prone to a high frequency of false positives. This… More >

Nasser Alshammari¹, Shumaila Shahzadi², Saad Awadh Alanazi^1,*, Shahid Naseem³, Muhammad Anwar³, Madallah Alruwaili⁴, Muhammad Rizwan Abid⁵, Omar Alruwaili⁴, Ahmed Alsayat¹, Fahad Ahmad^6,7

Computer Systems Science and Engineering, Vol.48, No.2, pp. 363-394, 2024, DOI:10.32604/csse.2023.040721 - 19 March 2024

Abstract Software Defined Network (SDN) and Network Function Virtualization (NFV) technology promote several benefits to network operators, including reduced maintenance costs, increased network operational performance, simplified network lifecycle, and policies management. Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration (NFV MANO), and malicious attacks in different scenarios disrupt the NFV Orchestrator (NFVO) and Virtualized Infrastructure Manager (VIM) lifecycle management related to network services or individual Virtualized Network Function (VNF). This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and… More >

Vaishnawi Priyadarshni¹, Sanjay Kumar Sharma¹, Mohammad Khalid Imam Rahmani^2,*, Baijnath Kaushik³, Rania Almajalid^2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2441-2468, 2024, DOI:10.32604/cmc.2024.044963 - 27 February 2024

Abstract Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women. Early detection and effective treatment of BC can help save women’s lives. Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques. This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set. The novelty of the proposed framework lies in the integration of More >

Ayesha Aslam¹, Adil Hussain^2,*

Journal on Artificial Intelligence, Vol.6, pp. 1-21, 2024, DOI:10.32604/jai.2024.047226 - 31 January 2024

Abstract With the increased accessibility of global trade information, transaction fraud has become a major worry in global banking and commerce security. The incidence and magnitude of transaction fraud are increasing daily, resulting in significant financial losses for both customers and financial professionals. With improvements in data mining and machine learning in computer science, the capacity to detect transaction fraud is becoming increasingly attainable. The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud. The research looks at the efficacy of these machine learning… More >

Shaik Mahaboob Basha^1,*, Victor Hugo C. de Albuquerque², Samia Allaoua Chelloug^3,*, Mohamed Abd Elaziz^4,5,6,7, Shaik Hashmitha Mohisin⁸, Suhail Parvaze Pathan⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1981-2004, 2024, DOI:10.32604/cmes.2023.031425 - 17 November 2023

Abstract Manual investigation of chest radiography (CXR) images by physicians is crucial for effective decision-making in COVID-19 diagnosis. However, the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques. This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies, including normal cases. Texture information is extracted using gray co-occurrence matrix (GLCM)-based features, while vessel-like features are obtained using Frangi, Sato, and Meijering filters. Machine learning models employing Decision Tree (DT) and Random Forest (RF) approaches are designed to categorize CXR images… More > Graphic Abstract

Mohammed Zakariah¹, Salman A. AlQahtani^2,*, Abdulaziz M. Alawwad¹, Abdullilah A. Alotaibi³

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4025-4054, 2023, DOI:10.32604/cmc.2023.043752 - 26 December 2023

Abstract Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic. By consuming time and resources, intrusive traffic hampers the efficient operation of network infrastructure. An effective strategy for preventing, detecting, and mitigating intrusion incidents will increase productivity. A crucial element of secure network traffic is Intrusion Detection System (IDS). An IDS system may be host-based or network-based to monitor intrusive network activity. Finding unusual internet traffic has become a severe security risk for intelligent devices. These systems are negatively impacted by several attacks, which are… More >

Dinesh Kalla^1,*, Sivaraju Kuraku²

Journal on Artificial Intelligence, Vol.5, pp. 145-162, 2023, DOI:10.32604/jai.2023.043366 - 18 December 2023

Abstract Phishing websites present a severe cybersecurity risk since they can lead to financial losses, data breaches, and user privacy violations. This study uses machine learning approaches to solve the problem of phishing website detection. Using artificial intelligence, the project aims to provide efficient techniques for locating and thwarting these dangerous websites. The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification. Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Classifiers, Linear Support Vector Classifiers, and Naive Bayes were all used More >

Oscar Peña-Cáceres^1,2,*, Henry Silva-Marchan³, Manuela Albert⁴, Miriam Gil¹

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1873-1891, 2023, DOI:10.32604/cmc.2023.043176 - 29 November 2023

Abstract The development of artificial intelligence (AI) and smart home technologies has driven the need for speech recognition-based solutions. This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes. Speech recognition allows users to control devices and perform everyday actions through spoken commands, eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light, heating, or lowering the blinds. The purpose of this study is to develop a speech-based classification model for recognizing human actions in… More >