Cross-Layer Learning: A Deep Learning-Based Forensic Framework for IoT Systems

View Sample PDF

Author(s): Tushar Mane (Symbiosis Institute of Technology, Symbiosis International University (Deemed), India)and Ambika Pawar (Symbiosis Institute of Technology, Symbiosis International University (Deemed), India)
Copyright: 2022
Pages: 29
Source title: Technologies to Advance Automation in Forensic Science and Criminal Investigation
Source Author(s)/Editor(s): Chung-Hao Chen (Old Dominion University, USA), Wen-Chao Yang (National Central Police University, Taiwan)and Lijia Chen (Henan University, China)
DOI: 10.4018/978-1-7998-8386-9.ch005

Keywords: Criminal Science and Forensics / Digital Crime & Forensics / Information Science Reference / Security & Forensics

Purchase

View Cross-Layer Learning: A Deep Learning-Based Forensic Framework for IoT Systems on the publisher's website for pricing and purchasing information.

Abstract

Deep learning-based investigation mechanisms are available for conventional forensics, but not for IoT forensics. Dividing the system into different layers according to their functionalities, collecting data from each layer, finding the correlating factor, and using it for pattern detection is the fundamental concept behind the proposed intelligent system. The authors utilize this notion for embedding intelligence in forensics and speed up the investigation process by providing hints to the examiner. They propose a novel cross-layer learning architecture (CCLA) for IoT forensics. To the best of their knowledge, this is the first attempt to incorporate deep learning into the forensics of the IoT ecosystem.

The IRMA Community

Research IRM

Cross-Layer Learning: A Deep Learning-Based Forensic Framework for IoT Systems

Purchase

Abstract

Related Content

IRMA Sponsors