Nishit Patil, Shubhalaxmi Joshi^*

Computer Systems Science and Engineering, Vol.49, pp. 49-77, 2025, DOI:10.32604/csse.2024.057702 - 03 January 2025

Abstract Intrusion detection (ID) is a cyber security practice that encompasses the process of monitoring network activities to identify unauthorized or malicious actions. This includes problems like the difficulties of existing intrusion detection models to identify emerging attacks, generating many false alarms, and their inability and difficulty to adapt themselves with time when it comes to threats, hence to overcome all those existing challenges in this research develop a Prairie Araneida optimization based fused Convolutional Neural Network model (PAO-CNN) for intrusion detection. The fused CNN (Convolutional Neural Netowrk) is a remarkable development since it combines statistical… More >

Sameh Zarif^1,2,*, Hadier Moawad², Khalid Amin², Abdullah Alharbi³, Wail S. Elkilani⁴, Shouze Tang⁵, Marian Wagdy⁶

Computer Systems Science and Engineering, Vol.49, pp. 79-98, 2025, DOI:10.32604/csse.2024.057439 - 03 January 2025

Abstract Authentication is the most crucial aspect of security and a predominant measure employed in cybersecurity. Cloud computing provides a shared electronic device resource for users via the internet, and the authentication techniques used must protect data from attacks. Previous approaches failed to resolve the challenge of making passwords secure, memorable, usable, and time-saving. Graphical Password (GP) is still not widely utilized in reality because consumers suffer from multiple login stages. This paper proposes an Indexed Choice-Based Graphical Password (ICGP) scheme for improving the authentication part. ICGP consists of two stages: registration and authentication. At the… More >

Verónica C. Tapia^1,2,*, Carlos M. Gaona²

Computer Systems Science and Engineering, Vol.49, pp. 1-17, 2025, DOI:10.32604/csse.2024.056979 - 03 January 2025

Abstract In recent years, automation has become a key focus in software development as organizations seek to improve efficiency and reduce time-to-market. The integration of artificial intelligence (AI) tools, particularly those using natural language processing (NLP) like ChatGPT, has opened new possibilities for automating various stages of the development lifecycle. The primary objective of this study is to evaluate the effectiveness of ChatGPT in automating various phases of software development. An artificial intelligence (AI) tool was developed using the OpenAI—Application Programming Interface (API), incorporating two key functionalities: 1) generating user stories based on case or process… More >

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 >

Zainatul Yushaniza Mohamed Yusoff¹, Mohamad Khairi Ishak^2,*, Lukman A. B. Rahim³, Mohd Shahrimie Mohd Asaari¹

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1669-1697, 2024, DOI:10.32604/csse.2024.056741 - 22 November 2024

Abstract The rapid adoption of Internet of Things (IoT) technologies has introduced significant security challenges across the physical, network, and application layers, particularly with the widespread use of the Message Queue Telemetry Transport (MQTT) protocol, which, while efficient in bandwidth consumption, lacks inherent security features, making it vulnerable to various cyber threats. This research addresses these challenges by presenting a secure, lightweight communication proxy that enhances the scalability and security of MQTT-based Internet of Things (IoT) networks. The proposed solution builds upon the Dang-Scheme, a mutual authentication protocol designed explicitly for resource-constrained environments and enhances it… More >

Conghao Niu^1,*, Dezhi Han¹, Bing Han², Zhongdai Wu²

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1723-1748, 2024, DOI:10.32604/csse.2024.056736 - 22 November 2024

Abstract The high coverage and all-weather capabilities of Synthetic Aperture Radar (SAR) image ship detection make it a widely accepted method for maritime ship positioning and identification. However, SAR ship detection faces challenges such as indistinct ship contours, low resolution, multi-scale features, noise, and complex background interference. This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images, incorporating key structures to enhance performance. The YOLOv8 backbone is replaced by the Slim Backbone (SB), and the Delete Medium-sized Detection Head (DMDH) structure is eliminated to concentrate on shallow features. Dynamically adjusting the… More >

B. Gomathi^1,*, R. Manimegalai¹, Srivatsan Santhanam², Atreya Biswas³

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1749-1765, 2024, DOI:10.32604/csse.2024.056659 - 22 November 2024

Abstract In enterprise operations, maintaining manual rules for enterprise processes can be expensive, time-consuming, and dependent on specialized domain knowledge in that enterprise domain. Recently, rule-generation has been automated in enterprises, particularly through Machine Learning, to streamline routine tasks. Typically, these machine models are black boxes where the reasons for the decisions are not always transparent, and the end users need to verify the model proposals as a part of the user acceptance testing to trust it. In such scenarios, rules excel over Machine Learning models as the end-users can verify the rules and have more… More >

Mohammad Al-Omari¹, Qasem Abu Al-Haija^2,*

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1537-1555, 2024, DOI:10.32604/csse.2024.056257 - 22 November 2024

Abstract More businesses are deploying powerful Intrusion Detection Systems (IDS) to secure their data and physical assets. Improved cyber-attack detection and prevention in these systems requires machine learning (ML) approaches. This paper examines a cyber-attack prediction system combining feature selection (FS) and ML. Our technique’s foundation was based on Correlation Analysis (CA), Mutual Information (MI), and recursive feature reduction with cross-validation. To optimize the IDS performance, the security features must be carefully selected from multiple-dimensional datasets, and our hybrid FS technique must be extended to validate our methodology using the improved UNSW-NB 15 and TON_IoT datasets. More >

Sagiraju Srinadhraju^*, Samaresh Mishra, Suresh Chandra Satapathy

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1641-1668, 2024, DOI:10.32604/csse.2024.055612 - 22 November 2024

Abstract This paper introduces the integration of the Social Group Optimization (SGO) algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model (COCOMO). COCOMO’s fixed coefficients often limit its adaptability, as they don’t account for variations across organizations. By fine-tuning these parameters with SGO, we aim to improve estimation accuracy. We train and validate our SGO-enhanced model using historical project data, evaluating its performance with metrics like the mean magnitude of relative error (MMRE) and Manhattan distance (MD). Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost More >

Mosab Manaseer¹, Maram Bani Younes^2,*

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1699-1721, 2024, DOI:10.32604/csse.2024.055128 - 22 November 2024

Abstract This work aims to examine the vulnerabilities and threats in the applications of intelligent transport systems, especially collision avoidance protocols. It focuses on achieving the availability of network communication among traveling vehicles. Finally, it aims to find a secure solution to prevent blackhole attacks on vehicular network communications. The proposed solution relies on authenticating vehicles by joining a blockchain network. This technology provides identification information and receives cryptography keys. Moreover, the ad hoc on-demand distance vector (AODV) protocol is used for route discovery and ensuring reliable node communication. The system activates an adaptive mode for monitoring More >