Malware is defined as any computer software explicitly designed to damage computers or networks. Their major motivation has evolved to malicious economic considerations. The anti-malware software or intrusion prevention systems are highly dependent on a signature database. A signature is a sequence of bits that is present within malicious executables and in the files already infected. Another features that can be used to face obfuscated and previously unseen malware are techniques based on dynamically extracted characteristics. Dynamic analysis executes the inspected specimen in a controlled environment called ‘sandbox’. The main advantages of static techniques are that they are safer because they do not execute malware, they are able to analyse all the execution paths of the executable, and the analysis and detection is usually fast.
Topics:
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- Malware detection based on machine learning and semi-supervised learning
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- Intrusion Detection System for IoT and/or IIoT / MEC infraestructure
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- Forensics
Relevant Publications:
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- Sergio Ruiz-Villafranca, José Roldán Gómez, Javier Carrillo Mondéjar, José Luis Martínez, Carlos H. Gañán, “WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance”. Future Gener. Comput. Syst. 166: 107707 (2025)
- Sergio Ruiz-Villafranca, Juan Manuel Castelo Gómez, Javier Carrillo-Mondéjar, José Roldán-Gómez, José Luis Martinez, “A Self-Contained Emulator for the Forensic Examination of IoE Scenarios“, accepted in Ad Hoc Networks, ISSN:1570-8705 [JCR IF 4.4: 1st quarter (2023)]
- J. Carrillo-Mondejar, H. Turtiainen, A. Costin, J.L. Martinez and G. Suarez-Tangil, “HALE-IoT: HArdening LEgacy Internet-of-Things devices by retrofitting defensive firmware modifications and implants”. IEEE Internet of Things Journal, November 2022. Impact: 10,238. Position: 9/164 (1st quarter)
- José Roldán-Gómez, J.M. del Rincon, J. Boubeta-Puig and J.L. Martinez, “An automatic unsupervised complex event processing rules generation architecture for real-time IoT attacks detection”. Wireless Networks, January, 2023. ISSN: 1572-8196. Impact: 2,701. Position: 138/276 (2nd quarter)
- Javier Carrillo-Mondejar, José Luis Martinez and Guillermo Suarez-Tangil, “On how VoIP attacks foster the malicious call ecosystem”, Computers & Security, Volume 119, August 2022, ISSN: 0167-4048. Impact: 4,438 Position: 40/161 (1st quarter)
- José Roldán, Juan Boubeta-Puig, José Luis Martínez and Guadalupe Ortiz, “Integrating Complex Event Processing and Machine Learning: an Intelligent Architecture for Detecting IoT Security Attacks”, Expert Systems With Applications, ISSN: 0957-4174, Vol. 149(113251), July, 2020. Impact: 4,292. Position: 7/84 (1st quarter)
- J. Carrillo-Mondéjar, J.L. Martínez and G. Suarez-Tangil, “Characterizing Linux-based malware: Findings and recent trends”, Future Generation Computer Systems , ISSN: 0167-739X, Vol. 110, pp 267-281, September, 2020. Impact: 5,678. Position: 8/105 (1st quarter)
People:
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Jose Luis Martinez, PhD Full Professor Phone number: +34 967 599 200 – Ext. 2294 Email: joseluis.martinez@uclm.es   |
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Javier Carrillo Mondejar Professor Ayudante Doctor at University of Zaragoza Email: jcarrillo@unizar.es   |
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José Roldán Gómez Substitute Professor at University of Zaragoza Email: jroldan@unizar.es |
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Juan Manuel Castelo Gómez Professor Ayudante Doctor at University Polytechnic of Madrid Email: juanmanuel.castelo@upm.es |
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Sergio Ruiz Villafranca PostDoc Email: Sergio.rvillafranca@ucln.es |
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Luis Miguel García Sáez PhD student Email: luism.garcia@uclm.es |