使用无线传感器网络嗅探工具的物联网(IoT)安全入侵监测模型

Gitonga Imathiu, Amos Chege, Amos O. Omamo
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引用次数: 0

摘要

物联网(IoT)彻底改变了设备通过无线传感器网络(WSN)交互和共享数据的方式,实现了无缝连接和自动化。然而,由于其固有的脆弱性,物联网设备的激增引发了严重的安全和隐私风险问题。本文通过分析现有文献,提出了一种安全入侵监测模型,并对所提模型的设计、实施和有效部署提出了见解,以利用嗅探工具实时分析 WSN 中的网络流量,检测物联网中的入侵。该模型可被动监控网络流量,并识别异常模式、未经授权的访问尝试和异常设备行为。审查结果凸显了所提模型在增强物联网系统安全性方面的重要意义。通过检测异常行为和潜在的安全漏洞。该模型能够及时采取应对和缓解措施,确保物联网设备数据的机密性、完整性和可用性(CIA)。该模型包括考虑网络架构、部署入侵检测算法和建立响应机制。它能识别各种类型的安全威胁,如未经授权的访问尝试、拒绝服务、分布式 DoS、暴力、心脏出血、僵尸网络、内部渗透和设备篡改,从而提供响应机制,包括生成警报、隔离受损设备或阻止可疑网络流量。该模型包含一个反馈回路,可持续更新检测机制,实时适应不断变化的安全威胁。将使用各种物联网设备和网络场景进行一系列实验和模拟,以评估模型的有效性。模型将包括无线路由器、用于深度神经网络(DNN)训练的 MatLab、树莓派、Wireshark 设置和一系列物联网(IoT)设备。研究人员将通过从 Wireshark Sniffing 工具中提取内在、基于主机和基于时间的属性来使用数据集。生成的数据集应通过 tshark 管道传输到以 csv 格式保存的输出文本文件中。将使用低采样技术来解决数据集的类不平衡问题。然后使用数据集训练模型,使其能够检测物联网中的入侵。预期结果将证明该模型有能力以较高的准确率和最小的误报率检测各种安全入侵。总之,该模型为保护物联网部署提供了一种积极主动的方法。通过利用嗅探工具和先进的分析技术,该模型增强了检测和响应能力,可有效防范物联网中的新兴威胁。不过,该模型也面临一些挑战,包括网络监控的复杂性和潜在的隐私问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Security intrusion monitoring model for Internet of Things (IoT) using sniffing tools on wireless sensor networks
The Internet of Things (IoT) has revolutionized the way devices interact and share data over wireless sensor networks (WSN), enabling seamless connectivity and automation. However, the proliferation of IoT devices has raised serious security and privacy risks concerns due to their inherent vulnerabilities. This paper proposes a model for security intrusion monitoring by analyzing the existing literature and providing insights into the design, implementation, and effective deployment of the proposed model to detect intrusion in IoT using sniffing tools for network traffic analysis in real-time within WSN. The model passively monitors network traffic and identifies anomalous patterns, unauthorized access attempts, and abnormal device behavior. The review findings highlight the significance of the proposed model in enhancing the security of IoT systems. By detecting anomalous behavior and potential security breaches. The model enables timely response and mitigation actions to ensure the confidentiality, integrity and availability (CIA) of IoT devices data. The model includes consideration of network architecture, deployment of intrusion detection algorithms, and establishment of response mechanisms. It identifies various types of security threats, such as unauthorized access attempts, Denial-of-service, Distributed DoS, Brute-force, Heartbleed, Botnet, Inside Infiltration and device tampering, thereby providing response mechanisms that include generating alerts, isolating compromised devices, or blocking suspicious network traffic. The model incorporates a feedback loop to continuously update the detection mechanisms and adapt to evolving security threats in real-time. Series of experiments and simulations to be conducted using various IoT devices and network scenarios to evaluate model effectiveness. The model to comprise of wireless Router, MatLab for Deep Neural Network (DNN) training, Raspberry Pi, Wireshark setup and an array of Internet of Things (IoT) devices. The researcher to use dataset by extracting intrinsic, host-based and time-based attributes from Wireshark Sniffing tool. The datasets generated shall be piped by tshark to an output text file saved as a csv. Under-sampling technique to be used to address class imbalance of datasets. The model shall then be trained using the dataset to be able to detect intrusion in IoTs. The results is expected to demonstrate the model's ability to detect a wide range of security intrusions with high accuracy and minimal false positives. In conclusion, the model offers a proactive approach to safeguard IoT deployment. By leveraging sniffing tools and advanced analysis techniques, the model enhances the detection and response capabilities, enabling efficient protection against emerging threats in IoT. However, challenges associated with the model are identified, including the complexity of network monitoring and potential privacy concerns.
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