klm-PPSA v. 1.1:机器学习增强分析和防止云环境中的安全攻击

IF 1.8 4区 计算机科学 Q3 TELECOMMUNICATIONS Annals of Telecommunications Pub Date : 2023-07-17 DOI:10.1007/s12243-023-00971-w
Nahid Eddermoug, Abdeljebar Mansour, Mohamed Sadik, Essaid Sabir, Mohamed Azmi
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引用次数: 2

摘要

如今,云计算是不同领域中提高生产力的关键推动者之一。然而,这项技术仍然受到安全攻击。本文旨在克服通过“入侵检测和预防系统(idps)”检测未知攻击的局限性,同时解决网络安全中广泛使用的机器学习(ML)模型的黑箱问题(缺乏可解释性)。我们提出了一个“基于klm的分析和预防安全攻击(klm-PPSA)”系统(v. 1.1),用于检测、分析和预防云环境甚至基于云的物联网中的已知和未知安全攻击。该系统基于与密码、生物识别和击键技术相关的klm安全因素。此外,基于klm-PPSA方案(v. 1.1)的更新和改进版本,开发了系统的两个子方案(k-PPSA、km-PPSA和klm-PPSA),分析了这些因素对生成模型性能的影响。该模型使用两种精确且可解释的ML算法:正则化类关联规则(RCAR)和基于关联的分类(CBA)。实证结果表明,与其他模型相比,klm-PPSA模型具有较高的性能和基于RCAR/CBA的攻击预测能力。此外,RCAR的性能优于CBA。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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klm-PPSA v. 1.1: machine learning-augmented profiling and preventing security attacks in cloud environments

Nowadays, cloud computing is one of the key enablers for productivity in different domains. However, this technology is still subject to security attacks. This article aims at overcoming the limitations of detecting unknown attacks by “intrusion detection and prevention systems (IDPSs)” while addressing the black-box issue (lack of interpretability) of the widely used machine learning (ML) models in cybersecurity. We propose a “klm-based profiling and preventing security attacks (klm-PPSA)” system (v. 1.1) to detect, profile, and prevent both known and unknown security attacks in cloud environments or even cloud-based IoT. This system is based on klm security factors related to passwords, biometrics, and keystroke techniques. Besides, two sub-schemes of the system were developed based on the updated and improved version of the klm-PPSA scheme (v. 1.1) to analyze the impact of these factors on the performance of the generated models (k-PPSA, km-PPSA, and klm-PPSA). The models were built using two accurate and interpretable ML algorithms: regularized class association rules (RCAR) and classification based on associations (CBA). The empirical results show that klm-PPSA is the best model compared to other models owing to its high performance and attack prediction capability using RCAR/CBA. In addition, RCAR performs better than CBA.

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来源期刊
Annals of Telecommunications
Annals of Telecommunications 工程技术-电信学
CiteScore
5.20
自引率
5.30%
发文量
37
审稿时长
4.5 months
期刊介绍: Annals of Telecommunications is an international journal publishing original peer-reviewed papers in the field of telecommunications. It covers all the essential branches of modern telecommunications, ranging from digital communications to communication networks and the internet, to software, protocols and services, uses and economics. This large spectrum of topics accounts for the rapid convergence through telecommunications of the underlying technologies in computers, communications, content management towards the emergence of the information and knowledge society. As a consequence, the Journal provides a medium for exchanging research results and technological achievements accomplished by the European and international scientific community from academia and industry.
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