IoT forensics: Analysis of a HIKVISION's mobile app

Dragonas, Evangelos; Lambrinoudakis, Costas; Kotsis, Michael

doi:10.1016/j.fsidi.2023.301560

Forensic Science International: Digital Investigation

2023

DOI: 10.1016/j.fsidi.2023.301560

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IoT forensics: Analysis of a HIKVISION's mobile app

Evangelos Dragonas

Costas Lambrinoudakis

Michael Kotsis

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2023

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“…Hanwha Techwin: Hanwha Techwin is a company that produces deep-learning-based surveillance cameras, such as the Q-AI and X-AI series, which use deep-learning algorithms to detect objects, people, and activities [119][120][121]. • Hikvision: Hikvision is a company that manufactures deep-learning-based surveillance cameras, such as the DeepinView series, which utilizes deep-learning algorithms to detect objects, people, and activities [122,123]. • NVIDIA: NVIDIA is a company that produces hardware and software for image and video processing based on deep learning, such as the Jetson platform, which can be used for surveillance applications such as facial recognition and object detection [124][125][126][127].…”

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Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Elhanashi,

Dini,

Saponara

et al. 2023

Electronics

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The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep-belief networks (DBNs), have demonstrated significant efficacy in mitigating these challenges by furnishing robust tools for learning and extraction of insights from vast and diverse IoT-generated data. This survey article offers a comprehensive and meticulous examination of recent scholarly endeavors encompassing the amalgamation of deep-learning techniques within the IoT landscape. Our scrutiny encompasses an extensive exploration of diverse deep-learning models, expounding on their architectures and applications within IoT domains, including but not limited to smart cities, healthcare informatics, and surveillance applications. We proffer insights into prospective research trajectories, discerning the exigency for innovative solutions that surmount extant limitations and intricacies in deploying deep-learning methodologies effectively within IoT frameworks.

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confidence: 99%

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Elhanashi,

Dini,

Saponara

et al. 2023

Electronics

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show abstract

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IoT forensics: Analysis of a HIKVISION's mobile app

Cited by 1 publication

References 3 publications

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

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