Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Olivier De Vel"'
Publikováno v:
IEEE Access, Vol 7, Pp 183162-183176 (2019)
Insider threat detection has drawn increasing attention in recent years. In order to capture a malicious insider's digital footprints that occur scatteredly across a wide range of audit data sources over a long period of time, existing approaches oft
Externí odkaz:
https://doaj.org/article/76a58b56355345fd8022c7f50cace951
Publikováno v:
IEEE Transactions on Dependable and Secure Computing. 19:438-451
A major cause of security incidents such as cyber attacks is rooted in software vulnerabilities. These vulnerabilities should ideally be found and fixed before the code gets deployed. Machine learning-based approaches achieve state-of-the-art perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a5410f302556c63df2bb92d2a5256038
https://doi.org/10.1109/tdsc.2020.2984505
https://doi.org/10.1109/tdsc.2020.2984505
Autor:
Yi Han, Olivier De Vel, Paul Montague, Tansu Alpcan, Tamas Abraham, Sarah M. Erfani, Benjamin I. P. Rubinstein, Christopher Leckie, David Hubczenko
Publikováno v:
IJCNN
Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting. While most existing work studies the problem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c292898b322291e9368266aef167e2e6
https://doi.org/10.1109/ijcnn48605.2020.9206634
https://doi.org/10.1109/ijcnn48605.2020.9206634
Autor:
Tamas Abraham, Christopher Leckie, Yi Han, David Hubczenko, Paul Montague, Tansu Alpcan, Sarah M. Erfani, Benjamin I. P. Rubinstein, Olivier De Vel
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030015534
GameSec
GameSec
Despite the successful application of machine learning (ML) in a wide range of domains, adaptability—the very property that makes machine learning desirable—can be exploited by adversaries to contaminate training and evade classification. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::311040586e3bb1e7efc27f95a60d227a
https://doi.org/10.1007/978-3-030-01554-1_9
https://doi.org/10.1007/978-3-030-01554-1_9
Autor:
Olivier de Vel
Publikováno v:
Data Mining and Knowledge Discovery. 13:309-334
In this paper we report an investigation into the learning of semi-structured document categorization. We automatically discover low-level, short-range byte data structure patterns from a document data stream by extracting all byte sub-sequences with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8ef6ef4f19c688fe3b415c20138eef1
https://doi.org/10.1007/s10618-005-0037-z
https://doi.org/10.1007/s10618-005-0037-z
Autor:
Olivier de Vel
Publikováno v:
Digital Investigation. 1:150-157
The ability to automatically classify files based on their low-level, short-range structures is of particular importance in computer forensics. We report a study on the automatic learning of file classification using byte sub-stream kernels that capt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::954b642904b450ec3a8bba3d42e5b6f3
https://doi.org/10.1016/j.diin.2004.03.002
https://doi.org/10.1016/j.diin.2004.03.002
Publikováno v:
AI 2008: Advances in Artificial Intelligence ISBN: 9783540893776
Australasian Conference on Artificial Intelligence
Australasian Conference on Artificial Intelligence
Owing to the spread of worms and botnets, cyber attacks have significantly increased in volume, coordination and sophistication. Cheap rentable botnet services, e.g., have resulted in sophisticated botnets becoming an effective and popular tool for c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35c15247a2fc878704ff83acc880ac05
https://doi.org/10.1007/978-3-540-89378-3_48
https://doi.org/10.1007/978-3-540-89378-3_48
Publikováno v:
Intelligence and Security Informatics ISBN: 9783540691365
ISI Workshops
ISI Workshops
Because of the high impact of high-tech digital crime upon our society, it is necessary to develop effective Information Retrieval (IR) tools to support digital forensic investigations. In this paper, we propose an IR system for digital forensics tha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6114a0acc7428a2735daae1ff820e4a
https://doi.org/10.1007/978-3-540-69304-8_22
https://doi.org/10.1007/978-3-540-69304-8_22
Publikováno v:
2006 International Workshop on Integrating AI and Data Mining.
This paper expands the use of Hidden Markov Models in Digital Forensics by using Coupled Hidden Markov Models to investigate interactions between multiple suspects in forensic cases. This paper compares the output of a coupled hidden Markov model to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af9c7dfced6a77f2b32ec70c6fa535cc
https://doi.org/10.1109/aidm.2006.16
https://doi.org/10.1109/aidm.2006.16
Publikováno v:
SAC
The authors have previously developed the ECF (Event Correlation for Forensics) framework for scenario matching in the forensic investigation of activity manifested in digital transactional logs. ECF incorporated a suite of log parsers to reduce even
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d185194edddc614f5e30ffec8f2e6526
https://doi.org/10.1145/1141277.1141346
https://doi.org/10.1145/1141277.1141346