F
Francisco J. Aparicio-Navarro
Researcher at De Montfort University
Publications - 25
Citations - 480
Francisco J. Aparicio-Navarro is an academic researcher from De Montfort University. The author has contributed to research in topics: Intrusion detection system & Network security. The author has an hindex of 10, co-authored 24 publications receiving 299 citations. Previous affiliations of Francisco J. Aparicio-Navarro include Newcastle University & Loughborough University.
Papers
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Journal ArticleDOI
Detection of advanced persistent threat using machine-learning correlation analysis
Ibrahim Ghafir,Ibrahim Ghafir,Mohammad Hammoudeh,Vaclav Prenosil,Liangxiu Han,Robert Hegarty,Khaled M. Rabie,Francisco J. Aparicio-Navarro +7 more
TL;DR: The presented system is able to predict APT in its early steps with a prediction accuracy of 84.8% and is a significant contribution to the current body of research.
Journal ArticleDOI
A novel intrusion detection system against spoofing attacks in connected electric vehicles
Dimitrios Kosmanos,Apostolos Pappas,Leandros A. Maglaras,Sotiris Moschoyiannis,Francisco J. Aparicio-Navarro,Antonios Argyriou,Helge Janicke +6 more
TL;DR: A probabilistic cross-layer Intrusion Detection System (IDS), based on Machine Learning (ML) techniques, is introduced, capable of detecting spoofing attacks with more than 90 % accuracy and uses a new metric, Position Verification using Relative Speed (PVRS), which seems to have a significant effect in classification results.
Journal ArticleDOI
Hidden Markov Models and Alert Correlations for the Prediction of Advanced Persistent Threats
Ibrahim Ghafir,Konstantinos G. Kyriakopoulos,Sangarapillai Lambotharan,Francisco J. Aparicio-Navarro,Basil AsSadhan,Hamad Binsalleeh,Diab M. Diab +6 more
TL;DR: This paper proposes a novel intrusion detection system for APT detection and prediction that estimates the sequence of APT stages with a prediction accuracy of at least 91.80% and predicts the next step of the APT campaign with an accuracy of 66.50%, 92.70%, and 100% based on four correlated alerts.
Proceedings ArticleDOI
Support Vector Machine for Network Intrusion and Cyber-Attack Detection
Kinan Ghanem,Francisco J. Aparicio-Navarro,Konstantinos G. Kyriakopoulos,Sangarapillai Lambotharan,Jonathon A. Chambers +4 more
TL;DR: An unsupervised anomaly-based IDS that uses statistical techniques to conduct the detection process, and the results evidence that the IDS could benefit from the use of ML techniques to increase its accuracy when analysing datasets comprising of non- homogeneous features.
Journal ArticleDOI
A Hybrid Intrusion Detection System for Virtual Jamming Attacks on Wireless Networks
Diego Santoro,Gines Escudero-Andreu,Konstantinos G. Kyriakopoulos,Francisco J. Aparicio-Navarro,David J. Parish,Michele Vadursi +5 more
TL;DR: A novel Hybrid-NIDS (H- NIDS) based on Dempster-Shafer (DS) Theory of Evidence is presented, which aims at combining the advantages of signature-based and anomaly-based NIDSs for virtual jamming attacks on IEEE 802.11 networks.