Detecting and forecasting cryptojacking attack trends in Internet of Things and wireless sensor networks devices.

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE PeerJ Computer Science Pub Date : 2024-11-29 eCollection Date: 2024-01-01 DOI:10.7717/peerj-cs.2491
Kishor Kumar Reddy C, Vijaya Sindhoori Kaza, Madana Mohana R, Abdulrahman Alamer, Shadab Alam, Mohammed Shuaib, Sultan Basudan, Abdullah Sheneamer
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Abstract

This research addresses the critical issue of cryptojacking attacks, a significant cybersecurity threat where malicious actors covertly exploit computational resources for unauthorized cryptocurrency mining, particularly in wireless sensor networks (WSN) and Internet of Things (IoT) devices. The article proposes an innovative approach that integrates time series analysis with graph neural networks (GNNs) to forecast/detect cryptojacking attack trends within these vulnerable ecosystems. Utilizing the "Cryptojacking Attack Timeseries Dataset," the proposed method emphasizes early detection and predictive insights to anticipate emerging attack patterns. Through rigorous experiments, the model demonstrated high accuracy with ARIMA achieving up to 99.98% on specific attributes and the GNN model yielding an accuracy of 99.99%. Despite these strengths, the ensemble approach showed a slightly lower overall accuracy of 90.97%. Despite the reduction in accuracy compared to individual models, the ensemble method enhances predictive robustness and adaptability, making it more effective in identifying emerging cryptojacking trends amidst varying network conditions. This research significantly contributes to enhancing cybersecurity measures against the evolving threat of cryptojacking in WSN and IoT environments by providing a robust, proactive defence mechanism.

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检测和预测物联网和无线传感器网络设备的加密攻击趋势。
本研究解决了加密劫持攻击的关键问题,这是一种重大的网络安全威胁,恶意行为者暗中利用计算资源进行未经授权的加密货币挖掘,特别是在无线传感器网络(WSN)和物联网(IoT)设备中。本文提出了一种创新的方法,将时间序列分析与图神经网络(gnn)相结合,以预测/检测这些脆弱生态系统中的加密劫持攻击趋势。利用“加密劫持攻击时间序列数据集”,提出的方法强调早期检测和预测洞察力,以预测新出现的攻击模式。经过严格的实验,该模型具有较高的准确率,ARIMA模型在特定属性上的准确率可达99.98%,GNN模型的准确率可达99.99%。尽管有这些优势,集成方法的总体准确率略低,为90.97%。尽管与单个模型相比准确性降低,但集成方法增强了预测鲁棒性和适应性,使其在不同网络条件下更有效地识别新出现的加密劫持趋势。本研究通过提供强大的主动防御机制,为增强网络安全措施,应对WSN和物联网环境中不断变化的加密劫持威胁做出了重大贡献。
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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