KFREAIN: Design of A Kernel-Level Forensic Layer for Improving Real-Time Evidence Analysis Performance in IoT Networks

  • Seema Shukla Dronacharya Group of Institutions, Greater Noida, India
  • Sangeeta Mangesh Dronacharya Group of Institutions, Greater Noida, India https://orcid.org/0000-0002-5379-5260
  • Prachi Chhabra JSS Academy of Technical Education, Noida, India
DOI: https://doi.org/10.24003/emitter.v11i2.804
Keywords: IoT, Kernel Layer, TBC, BFO, Forensics

Abstract

An exponential increase in number of attacks in IoT Networks makes it essential to formulate attack-level mitigation strategies. This paper proposes design of a scalable Kernel-level Forensic layer that assists in improving real-time evidence analysis performance to assist in efficient pattern analysis of the collected data samples. It has an inbuilt Temporal Blockchain Cache (TBC), which is refreshed after analysis of every set of evidences. The model uses a multidomain feature extraction engine that combines lightweight Fourier, Wavelet, Convolutional, Gabor, and Cosine feature sets that are selected by a stochastic Bacterial Foraging Optimizer (BFO) for identification of high variance features. The selected features are processed by an ensemble learning (EL) classifier that use low complexity classifiers reducing the energy consumption during analysis by 8.3% when compared with application-level forensic models. The model also showcased 3.5% higher accuracy, 4.9% higher precision, and 4.3% higher recall of attack-event identification when compared with standard forensic techniques. Due to kernel-level integration, the model is also able to reduce the delay needed for forensic analysis on different network types by 9.5%, thus making it useful for real-time & heterogenous network scenarios.

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Author Biographies

Sangeeta Mangesh, Dronacharya Group of Institutions, Greater Noida, India

Educator in the field of Electronics & Communication Engineering with experience of more than 20 years. Passionate about pedagogy and research, believe in forging deep connections with students. A leader and team player having ability to work proficiently, cohesively adapting to technological trends. Practitioner of life- long learning.

Prachi Chhabra, JSS Academy of Technical Education, Noida, India

An avid researcher and dedicated researcher

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Published
2023-12-20
How to Cite
Shukla, S., Mangesh, S., & Chhabra, P. (2023). KFREAIN: Design of A Kernel-Level Forensic Layer for Improving Real-Time Evidence Analysis Performance in IoT Networks. EMITTER International Journal of Engineering Technology, 11(2), 125-144. https://doi.org/10.24003/emitter.v11i2.804
Issue
Vol 11 No 2 (2023)
Section
Articles

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