Unveiling the hidden dangers: Security risks and forensic analysis of smart bulbs

IF 2 4区 医学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Forensic Science International-Digital Investigation Pub Date : 2024-06-15 DOI:10.1016/j.fsidi.2024.301794
Pankaj Sharma , Lalit Kumar Awasthi
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引用次数: 0

Abstract

People often dispose of their useless smart digital gadgets without realizing the potential presence of useful information inside these devices. This is also true for faulty smart bulbs, which cybercriminals might exploit to gain unauthorized access to a smart home and manipulate or steal private information. This research delves into the potential security risks associated with smart bulbs and provides recommendations for mitigating such risks. Through a comprehensive analysis of the functionality of smart bulbs, this study introduced the data extraction framework DEF-IoTF for collecting both hardware and application-level digital artifacts from smart bulbs. This paper presents the FIvM-IoT model for collecting and analyzing evidence from companion app data on mobile phones and Wifi modules at the hardware level. We conduct examinations on the smart bulb's Wifi module and extract its firmware using the developed Wifi_Cred tool. These include evidence related to user credentials, log time stamps, Wifi details, potential forensic information, and investigation procedures for IoT devices. Finally, this study provides prominent IoT forensic use cases along with the key requirements for hardware-level forensic investigation of Wifi modules.

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揭开隐藏的危险:智能灯泡的安全风险和取证分析
人们经常丢弃无用的智能数码设备,却没有意识到这些设备中可能存在有用的信息。有问题的智能灯泡也是如此,网络犯罪分子可能会利用这些问题,在未经授权的情况下进入智能家居,操纵或窃取私人信息。本研究深入探讨了与智能灯泡相关的潜在安全风险,并提出了降低此类风险的建议。通过对智能灯泡功能的全面分析,本研究引入了数据提取框架 DEF-IoTF,用于从智能灯泡中收集硬件和应用级数字工件。本文介绍了 FIvM-IoT 模型,用于从手机和 Wifi 模块的配套应用程序数据中收集和分析硬件层面的证据。我们对智能灯泡的 Wifi 模块进行检查,并使用开发的 Wifi_Cred 工具提取其固件。这些证据包括与用户凭证、日志时间戳、Wifi 详情、潜在取证信息和物联网设备调查程序相关的证据。最后,本研究提供了突出的物联网取证使用案例,以及对 Wifi 模块进行硬件级取证调查的关键要求。
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来源期刊
CiteScore
5.90
自引率
15.00%
发文量
87
审稿时长
76 days
期刊最新文献
Commentary:- Can I use that tool? Temporal metadata analysis: A learning classifier system approach Uncertainty and error in location traces Competence in digital forensics “What you say in the lab, stays in the lab”: A reflexive thematic analysis of current challenges and future directions of digital forensic investigations in the UK
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