Suneel Kumar Rath¹, Madhusmita Sahu¹, Shom Prasad Das², Junali Jasmine Jena³, Chitralekha Jena⁴, Baseem Khan^5,6,7,*, Ahmed Ali⁷, Pitshou Bokoro⁷

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1513-1536, 2024, DOI:10.32604/csse.2024.057067 - 22 November 2024

Abstract Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class’s data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning… More >

Saad M. Darwish^1,*, Abdul Rahman M. Sabri², Dhafar Hamed Abd², Adel A. Elzoghabi¹

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1595-1624, 2024, DOI:10.32604/csse.2024.054615 - 22 November 2024

Abstract The number of blogs and other forms of opinionated online content has increased dramatically in recent years. Many fields, including academia and national security, place an emphasis on automated political article orientation detection. Political articles (especially in the Arab world) are different from other articles due to their subjectivity, in which the author’s beliefs and political affiliation might have a significant influence on a political article. With categories representing the main political ideologies, this problem may be thought of as a subset of the text categorization (classification). In general, the performance of machine learning models… More >

Farida Asriani^1,2, Azhari Azhari^1,*, Wahyono Wahyono¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3079-3096, 2024, DOI:10.32604/cmc.2024.058193 - 18 November 2024

Abstract Incredible progress has been made in human action recognition (HAR), significantly impacting computer vision applications in sports analytics. However, identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns. Deep learning techniques like convolutional neural networks (CNNs), long short-term memory (LSTM), and graph convolutional networks (GCNs) improve recognition in large datasets, while the traditional machine learning methods like SVM (support vector machines), RF (random forest), and LR (logistic regression), combined with handcrafted features and ensemble approaches, perform well but… More >

Arkan Kh Shakr Sabonchi^*

Journal on Artificial Intelligence, Vol.6, pp. 261-282, 2024, DOI:10.32604/jai.2024.056552 - 18 October 2024

Abstract The phenomenal increase in device connectivity is making the signaling and resource-based operational integrity of networks at the node level increasingly prone to distributed denial of service (DDoS) attacks. The current growth rate in the number of Internet of Things (IoT) attacks executed at the time of exchanging data over the Internet represents massive security hazards to IoT devices. In this regard, the present study proposes a new hybrid optimization technique that combines the firefly optimization algorithm with global searches for use in attack detection on IoT devices. We preprocessed two datasets, CICIDS and UNSW-NB15,… More >

Fahim Nasir¹, Abdulghani Ali Ahmed^1,*, Mehmet Sabir Kiraz¹, Iryna Yevseyeva¹, Mubarak Saif²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1703-1728, 2024, DOI:10.32604/cmc.2024.055192 - 15 October 2024

Abstract Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making. However, imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics, limiting their overall effectiveness. This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers (SLCs) and evaluates their performance in data-driven decision-making. The evaluation uses various metrics, with a particular focus on the Harmonic Mean Score (F-1 score) on an imbalanced real-world bank target marketing dataset. The findings indicate… More >

Ye-Seul Kil^1,#, Yu-Ran Jeon^1,#, Sun-Jin Lee¹, Il-Gu Lee^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1473-1493, 2024, DOI:10.32604/cmes.2024.052637 - 27 September 2024

Abstract With the rise of remote work and the digital industry, advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics, rendering them difficult to detect with conventional intrusion detection methods. Signature-based intrusion detection methods can be used to detect attacks; however, they cannot detect new malware. Endpoint detection and response (EDR) tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques. However, EDR tools are restricted by the continuous generation of unnecessary logs, resulting in poor detection… More >

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 >

Amel Ali Alhussan¹, Doaa Sami Khafaga¹, El-Sayed M. El-kenawy^2,*, Marwa M. Eid^2,3, Abdelhameed Ibrahim⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1163-1177, 2024, DOI:10.32604/cmc.2024.049001 - 18 July 2024

Abstract To reduce the negative effects that conventional modes of transportation have on the environment, researchers are working to increase the use of electric vehicles. The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge. The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue. Nevertheless, the powering of these terminals presents challenges because of the high energy requirements, which may influence the quality of service. Modelling the maximum hourly capacity of each station based on… More >

Kaveh Heidary^*

Journal on Artificial Intelligence, Vol.6, pp. 153-175, 2024, DOI:10.32604/jai.2024.050563 - 18 July 2024

Abstract This paper provides a detailed mathematical model governing the operation of feedforward neural networks (FFNN) and derives the backpropagation formulation utilized in the training process. Network protection systems must ensure secure access to the Internet, reliability of network services, consistency of applications, safeguarding of stored information, and data integrity while in transit across networks. The paper reports on the application of neural networks (NN) and deep learning (DL) analytics to the detection of network traffic anomalies, including network intrusions, and the timely prevention and mitigation of cyberattacks. Among the most prevalent cyber threats are R2L,… More >

Simona-Vasilica Oprea^*, Adela Bâra

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3827-3853, 2024, DOI:10.32604/cmc.2024.051598 - 20 June 2024

Abstract The potential of text analytics is revealed by Machine Learning (ML) and Natural Language Processing (NLP) techniques. In this paper, we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators (URLs). Three categories of features, both ML and Deep Learning (DL) algorithms and a ranking schema are included in the proposed framework. We apply frequency and prediction-based embeddings, such as hash vectorizer, Term Frequency-Inverse Dense Frequency (TF-IDF) and predictors, word to vector-word2vec (continuous bag of words, skip-gram) from Google, to extract features from text. Further, we apply more… More >