Maghrabi Louai A, Subahi Alanoud, Alghanmi Nouf Atiahallah, Althaqafi Turki, Abid Nahla J, Albogami Nasser N, Ragab Mahmoud
Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia.
Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, 25732, Rabigh, Saudi Arabia.
Sci Rep. 2025 May 15;15(1):16920. doi: 10.1038/s41598-025-01561-7.
As cyberattacks become more advanced, conventional centralized threat intelligence models often fail to keep up with these threats' growing complexity and frequency, highlighting the requirement for innovative approaches to strengthen cybersecurity resilience. Federated learning (FL), a decentralized machine learning (ML) model, provides a promising solution by permitting spread objects to train techniques on local data collaboratively without distributing sensitive data. The efficiency of FL in enhancing attack intelligence skills emphasizes its probability of driving a novel period of robust and privacy-protecting cybersecurity practices. Furthermore, combining FL into cybersecurity structures can strengthen attack intelligence models by permitting real upgrades and adaptive learning mechanisms. Recently, ML and Deep Learning (DL) approaches have drawn the study community to advance security solutions for cyberattack defence mechanism models. Conventional ML and DL techniques that function with data kept on a federal server increase the main privacy issues for user information. This manuscript presents a Cyberattack Defence Mechanism System for Federated Learning Framework using Attention Induced Deep Convolution Neural Networks (CDMFL-AIDCNN) technique. The CDMFL-AIDCNN model presents an improved structure incorporating self-guided FL with attack intelligence to improve defence mechanisms across varied cybersecurity applications in distributed systems. Initially, the data preprocessing stage utilizes Z-score normalization to transform input data into a beneficial format. The Dung Beetle Optimization (DBO) technique is used in the feature selection process to identify the most relevant and non-redundant features. Furthermore, the fusion of convolutional neural networks, bidirectional long short-term memory, gated recurrent units, and attention (CBLG-A) models are employed to classify cyberattack defence mechanisms. Finally, the parameter tuning of the CBLG-A approach is performed by the growth optimizer (GO) approach. The CDMFL-AIDCNN technique is extensively analyzed using the CIC-IDS-2017 and UNSW-NB15 datasets. The comparison analysis of the CDMFL-AIDCNN technique portrayed a superior accuracy value of 99.07% and 98.64% under the CIC-IDS-2017 and UNSW-NB15 datasets.
随着网络攻击变得越来越复杂，传统的集中式威胁情报模型往往难以跟上这些威胁日益增长的复杂性和频率，这凸显了采用创新方法来增强网络安全弹性的必要性。联邦学习（FL）是一种去中心化的机器学习（ML）模型，它通过允许分布式对象在本地数据上协作训练技术而不泄露敏感数据，提供了一个很有前景的解决方案。联邦学习在提升攻击情报技能方面的效率强调了其推动稳健且保护隐私的网络安全实践新时代的可能性。此外，将联邦学习融入网络安全架构可以通过允许实时更新和自适应学习机制来强化攻击情报模型。最近，机器学习和深度学习（DL）方法吸引了研究界为网络攻击防御机制模型推进安全解决方案。在联邦服务器上处理数据的传统机器学习和深度学习技术增加了用户信息的主要隐私问题。本文提出了一种使用注意力诱导深度卷积神经网络（CDMFL-AIDCNN）技术的联邦学习框架的网络攻击防御机制系统。CDMFL-AIDCNN模型提出了一种改进的结构，将自引导联邦学习与攻击情报相结合，以改善分布式系统中各种网络安全应用的防御机制。最初，数据预处理阶段利用Z分数归一化将输入数据转换为有益的格式。在特征选择过程中使用蜣螂优化（DBO）技术来识别最相关且无冗余的特征。此外，卷积神经网络与双向长短期记忆、门控循环单元和注意力（CBLG-A）模型的融合用于对网络攻击防御机制进行分类。最后，通过增长优化器（GO）方法对CBLG-A方法进行参数调整。使用CIC-IDS-2017和UNSW-NB15数据集对CDMFL-AIDCNN技术进行了广泛分析。CDMFL-AIDCNN技术的比较分析在CIC-IDS-2017和UNSW-NB15数据集下分别呈现出99.07%和98.64%的卓越准确率。
