Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Dustin Mink"'
Publikováno v:
Knowledge, Vol 4, Iss 1, Pp 96-119 (2024)
One of the major problems in classifying network attack tactics is the imbalanced nature of data. Typical network datasets have an extremely high percentage of normal or benign traffic and machine learners are skewed toward classes with more data; he
Externí odkaz:
https://doaj.org/article/2d54cb2d96fb4ffda3204e577fb2b1de
Publikováno v:
Computers, Vol 13, Iss 7, p 171 (2024)
This research leverages Memgraph, an open-source graph database, to analyze graph-based network data and apply Graph Neural Networks (GNNs) for a detailed classification of cyberattack tactics categorized by the MITRE ATT&CK framework. As part of gra
Externí odkaz:
https://doaj.org/article/e0e2e90c580e49efb0c80a5d5f910472
Publikováno v:
Information, Vol 15, Iss 7, p 404 (2024)
The novelty of this paper is in determining and using hyperparameters to improve the Extended Isolation Forest (EIF) algorithm, a relatively new algorithm, to detect malicious activities in network traffic. The EIF algorithm is a variation of the Iso
Externí odkaz:
https://doaj.org/article/7d1cca7e046b4fe3851d8dc539540b4b
Publikováno v:
Computers, Vol 12, Iss 10, p 204 (2023)
Machine Learning is widely used in cybersecurity for detecting network intrusions. Though network attacks are increasing steadily, the percentage of such attacks to actual network traffic is significantly less. And here lies the problem in training M
Externí odkaz:
https://doaj.org/article/656afec6c4ad40f2a628656443f68b73
Publikováno v:
Future Internet, Vol 15, Iss 7, p 236 (2023)
There has been a great deal of research in the area of using graph engines and graph databases to model network traffic and network attacks, but the novelty of this research lies in visually or graphically representing the Reconnaissance Tactic (TA00
Externí odkaz:
https://doaj.org/article/35e0361cf5b24e6b8eb6e1337e789099
Publikováno v:
Future Internet, Vol 15, Iss 4, p 130 (2023)
This study, focusing on identifying rare attacks in imbalanced network intrusion datasets, explored the effect of using different ratios of oversampled to undersampled data for binary classification. Two designs were compared: random undersampling be
Externí odkaz:
https://doaj.org/article/f45985f4a590483e83755a0fde3407ea
Autor:
Sikha S. Bagui, Dustin Mink, Subhash C. Bagui, Tirthankar Ghosh, Russel Plenkers, Tom McElroy, Stephan Dulaney, Sajida Shabanali
Publikováno v:
Data, Vol 8, Iss 1, p 18 (2023)
With the rapid rate at which networking technologies are changing, there is a need to regularly update network activity datasets to accurately reflect the current state of network infrastructure/traffic. The uniqueness of this work was that this was
Externí odkaz:
https://doaj.org/article/408d9297a4a447b184ef9d376a20ff44
Autor:
Sikha Bagui, Dustin Mink, Subhash Bagui, Tirthankar Ghosh, Tom McElroy, Esteban Paredes, Nithisha Khasnavis, Russell Plenkers
Publikováno v:
Sensors, Vol 22, Iss 20, p 7999 (2022)
While computer networks and the massive amount of communication taking place on these networks grow, the amount of damage that can be done by network intrusions grows in tandem. The need is for an effective and scalable intrusion detection system (ID
Externí odkaz:
https://doaj.org/article/ba42a8f786d44754a7b4f5d80c3c79fe
Publikováno v:
Data Science Journal, Vol 6, Pp 46-63 (2007)
This paper presents data mining techniques that can be used to study voting patterns in the United States House of Representatives and shows how the results can be interpreted. We processed the raw data available at http://clerk.house.gov, performed
Externí odkaz:
https://doaj.org/article/fcd65a603b9543d7a75a18b6fd29ba8b
Publikováno v:
Future Internet; Volume 15; Issue 7; Pages: 236
There has been a great deal of research in the area of using graph engines and graph databases to model network traffic and network attacks, but the novelty of this research lies in visually or graphically representing the Reconnaissance Tactic (TA00
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::2f264503279a2f141ee85190cd5eeadc