In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

中文翻译：

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使用人工智能架构的物联网可持续安全

在这个数字时代，人类在各个领域对技术的依赖度都在急剧增加。每天都在制造大量不同的电子产品以供日常使用。随着互联网技术世界的进步，软件和硬件系统的网络安全现在已成为主要业务运营的先决条件。市场上的每一项技术都有多个漏洞，黑客和网络犯罪分子每天都会利用这些漏洞来操纵数据，有时会出于恶意目的。在任何系统中，入侵检测系统 (IDS) 都是确保设备安全免受数字攻击的基本组件。现有 IDS 越来越难以识别新出现的数字威胁。此外，IDS 需要先进的框架才能有效地发挥作用。商业领域中常见的网络攻击包括用于窃取私人数据的轻微攻击。本文介绍了一种使用长短期网络分类器检测物联网网络攻击的深度学习方法。我们广泛的实验测试显示准确率为 99.09%，F1 分数为 99.46%，召回率为 99.51%。以表格形式表示我们结果的详细指标被用来比较我们的模型在检测网络攻击方面的能力如何优于其他最先进的模型。我们广泛的实验测试显示准确率为 99.09%，F1 分数为 99.46%，召回率为 99.51%。以表格形式表示我们结果的详细指标被用来比较我们的模型在检测网络攻击方面的能力如何优于其他最先进的模型。我们广泛的实验测试显示准确率为 99.09%，F1 分数为 99.46%，召回率为 99.51%。以表格形式表示我们结果的详细指标被用来比较我们的模型在检测网络攻击方面的能力如何优于其他最先进的模型。