A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Omar Azib Alkhudaydi, Moez Krichen, Ans D. Alghamdi
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引用次数: 1

Abstract

With the increasing severity and frequency of cyberattacks, the rapid expansion of smart objects intensifies cybersecurity threats. The vast communication traffic data between Internet of Things (IoT) devices presents a considerable challenge in defending these devices from potential security breaches, further exacerbated by the presence of unbalanced network traffic data. AI technologies, especially machine and deep learning, have shown promise in detecting and addressing these security threats targeting IoT networks. In this study, we initially leverage machine and deep learning algorithms for the precise extraction of essential features from a realistic-network-traffic BoT-IoT dataset. Subsequently, we assess the efficacy of ten distinct machine learning models in detecting malware. Our analysis includes two single classifiers (KNN and SVM), eight ensemble classifiers (e.g., Random Forest, Extra Trees, AdaBoost, LGBM), and four deep learning architectures (LSTM, GRU, RNN). We also evaluate the performance enhancement of these models when integrated with the SMOTE (Synthetic Minority Over-sampling Technique) algorithm to counteract imbalanced data. Notably, the CatBoost and XGBoost classifiers achieved remarkable accuracy rates of 98.19% and 98.50%, respectively. Our findings offer insights into the potential of the ML and DL techniques, in conjunction with balancing algorithms such as SMOTE, to effectively identify IoT network intrusions.
预测物联网网络安全攻击的深度学习方法
随着网络攻击的日益严重和频繁,智能对象的快速扩张加剧了网络安全威胁。物联网(IoT)设备之间的大量通信流量数据对保护这些设备免受潜在的安全漏洞提出了相当大的挑战,而网络流量数据不平衡的存在进一步加剧了这一挑战。人工智能技术,特别是机器和深度学习,在检测和解决这些针对物联网网络的安全威胁方面显示出了希望。在本研究中,我们首先利用机器和深度学习算法从现实网络流量BoT-IoT数据集中精确提取基本特征。随后,我们评估了十种不同的机器学习模型在检测恶意软件方面的功效。我们的分析包括两个单一分类器(KNN和SVM),八个集成分类器(例如随机森林,Extra Trees, AdaBoost, LGBM)和四个深度学习架构(LSTM, GRU, RNN)。我们还评估了这些模型在与SMOTE(合成少数派过采样技术)算法集成以抵消不平衡数据时的性能增强。值得注意的是,CatBoost和XGBoost分类器的准确率分别达到了98.19%和98.50%。我们的研究结果为机器学习和深度学习技术的潜力提供了见解,并结合平衡算法(如SMOTE),有效识别物联网网络入侵。
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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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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