An epidemic based model for the predictions of OOFI in an IoT platform
Based on the notion that when a particular node is maliciously infected, there is probability of propagating such infections to other susceptible nodes in a network. This lead to the development of malware spreading models to predict the transmission rate, transmission parameters and the number of i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Seventh Sense Research
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/87826/ http://psasir.upm.edu.my/id/eprint/87826/1/ABSTRACT.pdf |
| _version_ | 1848860508106522624 |
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| author | Ibrahim, Mohammed Abdullah @ Selimun, Mohd Taufik Abdullah, Azizol Perumal, Thinagaran |
| author_facet | Ibrahim, Mohammed Abdullah @ Selimun, Mohd Taufik Abdullah, Azizol Perumal, Thinagaran |
| author_sort | Ibrahim, Mohammed |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Based on the notion that when a particular node is maliciously infected, there is probability of propagating such infections to other susceptible nodes in a network. This lead to the development of malware spreading models to predict the transmission rate, transmission parameters and the number of infected nodes per unit
time. However, the emergence of Internet of Things (IoT) with strong base in both wired and wireless sensor network(WSN), predicting the spreading of malware infections is not the only source of concern for forensic analysis. Considering the heterogeneity and data volatility of IoT nodes, predicting the object of forensic interest (OOFI) in resource-constraint devices like sensor nodes as well as the diffusion of data among the neighboring nodes remain a critical issue for forensic analysis. From the concept of epidemic theory, a novel
model is proposed called Susceptible-Infective-Recovered with Forensic (SIR-F) that can predict and isolate OOFI among various nodes in IoT network. The essence of introducing forensic mechanism is to ascertain the OOFI by predicting the responsible nodes holding the data of forensic interest. As such, SIR-F can timely enhance the process of identifying OOFI of the collection phase of digital forensic standard operating procedure (SOP). |
| first_indexed | 2025-11-15T12:46:21Z |
| format | Article |
| id | upm-87826 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:46:21Z |
| publishDate | 2020 |
| publisher | Seventh Sense Research |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-878262022-06-14T07:50:08Z http://psasir.upm.edu.my/id/eprint/87826/ An epidemic based model for the predictions of OOFI in an IoT platform Ibrahim, Mohammed Abdullah @ Selimun, Mohd Taufik Abdullah, Azizol Perumal, Thinagaran Based on the notion that when a particular node is maliciously infected, there is probability of propagating such infections to other susceptible nodes in a network. This lead to the development of malware spreading models to predict the transmission rate, transmission parameters and the number of infected nodes per unit time. However, the emergence of Internet of Things (IoT) with strong base in both wired and wireless sensor network(WSN), predicting the spreading of malware infections is not the only source of concern for forensic analysis. Considering the heterogeneity and data volatility of IoT nodes, predicting the object of forensic interest (OOFI) in resource-constraint devices like sensor nodes as well as the diffusion of data among the neighboring nodes remain a critical issue for forensic analysis. From the concept of epidemic theory, a novel model is proposed called Susceptible-Infective-Recovered with Forensic (SIR-F) that can predict and isolate OOFI among various nodes in IoT network. The essence of introducing forensic mechanism is to ascertain the OOFI by predicting the responsible nodes holding the data of forensic interest. As such, SIR-F can timely enhance the process of identifying OOFI of the collection phase of digital forensic standard operating procedure (SOP). Seventh Sense Research 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/87826/1/ABSTRACT.pdf Ibrahim, Mohammed and Abdullah @ Selimun, Mohd Taufik and Abdullah, Azizol and Perumal, Thinagaran (2020) An epidemic based model for the predictions of OOFI in an IoT platform. SSRG International Journal of Engineering Trends and Technology (pt. 2). 52 -56. ISSN 2349-0918; ESSN: 2231-5381 http://ijettjournal.org/special-issues/cat-part-2 10.14445/22315381/CATI2P208 |
| spellingShingle | Ibrahim, Mohammed Abdullah @ Selimun, Mohd Taufik Abdullah, Azizol Perumal, Thinagaran An epidemic based model for the predictions of OOFI in an IoT platform |
| title | An epidemic based model for the predictions of OOFI in an IoT platform |
| title_full | An epidemic based model for the predictions of OOFI in an IoT platform |
| title_fullStr | An epidemic based model for the predictions of OOFI in an IoT platform |
| title_full_unstemmed | An epidemic based model for the predictions of OOFI in an IoT platform |
| title_short | An epidemic based model for the predictions of OOFI in an IoT platform |
| title_sort | epidemic based model for the predictions of oofi in an iot platform |
| url | http://psasir.upm.edu.my/id/eprint/87826/ http://psasir.upm.edu.my/id/eprint/87826/ http://psasir.upm.edu.my/id/eprint/87826/ http://psasir.upm.edu.my/id/eprint/87826/1/ABSTRACT.pdf |