Monitoring and Detection Time Optimization of Man in the Middle Attacks using Machine Learning
Monitoring and Detection Time Optimization of Man in the Middle Attacks using Machine Learning
复制标题
使用机器学习监控和检测中间人攻击的时间优化
DOI：
10.1109/aipr50011.2020.9425304
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
K. Kornegay
中科院分区：
文献类型：
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作者：
Otily Toutsop;Paige Harvey;K. Kornegay
物联网（IoT）随着技术的进步而发展。许多供应商正在开发物联网设备，以提高消费者的生活质量。这些设备包括智能电网、智能家居、智能医疗保健系统、智能交通和许多其他应用。物联网设备使用传感器和执行器与环境和彼此交互。然而，物联网设备的广泛扩散带来了许多网络安全威胁。物联网设备的互连为试图未经授权访问这些设备的攻击者打开了大门。对于许多IT网络来说，在设备运行期间建立信任和安全性是一项挑战。此外，设备还可能泄露重要信息，这是网络安全中的一个重大问题。此前的研究表明，安全漏洞在过去五年中增加了67%，95%的HTTP服务器容易受到中间人（MIM）攻击。本文研究了黑客和对策研究实验室（HCRL）从现实生活中的物联网设备（包括智能相机，笔记本电脑和智能手机）收集的攻击数据集。我们提出了一个使用随机森林，逻辑回归和决策树的模型。实验结果表明，该系统的整体检测准确率为98- 100%，比传统的入侵检测系统（IDS）更有前途。
The Internet of Things (IoT) is growing with the advancement of technology. Many vendors are creating IoT devices to leverage the quality of life of consumers. These devices include smart grids, smart homes, smart health care systems, smart transportation, and many other applications. IoT devices interact with the environment and each other using sensors and actuators. However, the widespread proliferation of IoT devices poses many cybersecurity threats. The IoT devices’ interconnection opens the door to attackers who try to gain unauthorized access to these devices. For many IT networks, establishing trust and security during the device operation is challenging. Further, devices also may leak vital information, which is a significant concern in cybersecurity. Prior research has shown that security breaches have increased by 67% over the past five years, and 95% of HTTPs servers are vulnerable to Man-in-the-middle (MIM) attacks. This paper examines attack datasets from the Hacking and Countermeasure Research Lab (HCRL) collected from real-life IoT devices that include smart cameras, laptops, and smartphones [1]. We present a model using Random Forest, Logistic Regression, and Decision Tree. Results indicate that the overall detection accuracy is 98-100%, which is more promising than traditional Intrusion Detection Systems (IDS).