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This work was supported by the MINECO and European Commission (FEDER funds) under project RTI2018-098156-B-C52, the JCCM under the project SB-PLY/17/180501/-00035, the Spanish Education, Culture and Sports Ministry under grants FPU 17/03105 and FPU 17/02007, the University of Castilla-La Mancha under the contract 2018-PREDUCLM-7476 and the project 2020-GRIN-28846, and the Spanish State Research Agency under the project PEJ2018-003001-A.

Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Castelo Gomez, Juan ManuelAuthor
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Article

Automatic Analysis Architecture of IoT Malware Samples

Publicated to:Security And Communication Networks. 2020 8810708- - 2020-10-26 2020(), DOI: 10.1155/2020/8810708

Authors: Carrillo-Mondejar, Javier; Castelo Gomez, Juan Manuel; Nunez-Gomez, Carlos; Roldan Gomez, Jose; Martinez, Jose Luis

Affiliations

Univ Castilla La Mancha, Res Inst Informat I3A, Albacete 02071, Spain - Author

Abstract

The weakness of the security measures implemented on IoT devices, added to the sensitivity of the data that they handle, has created an attractive environment for cybercriminals to carry out attacks. To do so, they develop malware to compromise devices and control them. The study of malware samples is a crucial task in order to gain information on how to protect these devices, but it is impossible to manually do this due to the immense number of existing samples. Moreover, in the IoT, coexist multiple hardware architectures, such as ARM, PowerPC, MIPS, Intel 8086, or x64-86, which enlarges even more the quantity of malicious software. In this article, a modular solution to automatically analyze IoT malware samples from these architectures is proposed. In addition, the proposal is subjected to evaluation, analyzing a testbed of 1500 malware samples, proving that it is an effective approach to rapidly examining malicious software compiled for any architecture.

Keywords
Peace, justice, and strong institutions

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Security And Communication Networks due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2020, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category . Notably, the journal is positioned en el Cuartil Q4 for the agency WoS (JCR) in the category Telecommunications.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 1.43, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Apr 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-04-30, the following number of citations:

  • WoS: 3
  • Scopus: 7
  • Open Alex: 7
  • OpenCitations: 5
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-04-30:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 22 (PlumX).
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 16 - Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels, with a probability of 60% according to the mBERT algorithm developed by Aurora University.