Matches in SemOpenAlex for { <https://semopenalex.org/work/W4205569013> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W4205569013 abstract "As the demand for data has increased, we have witnessed a surge in the use of machine learning to help aid industry and government in making sense of massive amounts of data and, subsequently, making predictions and decisions. For the military, this surge has manifested itself in the Internet of Battlefield Things. The pervasive nature of data on today's battlefield will allow machine learning models to increase soldier lethality and survivability. However, machine learning models are predicated upon the assumptions that the data upon which these machine learning models are being trained is truthful and the machine learning models are not compromised. These assumptions surrounding the quality of data and models cannot be the status-quo going forward as attackers establish novel methods to exploit machine learning models for their benefit. These novel attack methods can be described as adversarial machine learning (AML). These attacks allow an attacker to unsuspectingly alter a machine learning model before and after model training in order to degrade a model's ability to detect malicious activity. In this paper, we show how AML, by poisoning data sets and evading well trained models, affect machine learning models' ability to function as Network Intrusion Detection Systems (NIDS). Finally, we highlight why evasion attacks are especially effective in this setting and discuss some of the causes for this degradation of model effectiveness." @default.
- W4205569013 created "2022-01-25" @default.
- W4205569013 creator A5005126546 @default.
- W4205569013 creator A5032194186 @default.
- W4205569013 creator A5081968933 @default.
- W4205569013 date "2021-11-29" @default.
- W4205569013 modified "2023-10-12" @default.
- W4205569013 title "A Sensitivity Analysis of Poisoning and Evasion Attacks in Network Intrusion Detection System Machine Learning Models" @default.
- W4205569013 cites W1968998685 @default.
- W4205569013 cites W1971751574 @default.
- W4205569013 cites W2093825590 @default.
- W4205569013 cites W2095577883 @default.
- W4205569013 cites W2099940443 @default.
- W4205569013 cites W2124283284 @default.
- W4205569013 cites W2293768274 @default.
- W4205569013 cites W2434476332 @default.
- W4205569013 cites W2543927648 @default.
- W4205569013 cites W2792450155 @default.
- W4205569013 cites W3198511875 @default.
- W4205569013 cites W4251616545 @default.
- W4205569013 doi "https://doi.org/10.1109/milcom52596.2021.9652959" @default.
- W4205569013 hasPublicationYear "2021" @default.
- W4205569013 type Work @default.
- W4205569013 citedByCount "2" @default.
- W4205569013 countsByYear W42055690132022 @default.
- W4205569013 countsByYear W42055690132023 @default.
- W4205569013 crossrefType "proceedings-article" @default.
- W4205569013 hasAuthorship W4205569013A5005126546 @default.
- W4205569013 hasAuthorship W4205569013A5032194186 @default.
- W4205569013 hasAuthorship W4205569013A5081968933 @default.
- W4205569013 hasConcept C108583219 @default.
- W4205569013 hasConcept C119857082 @default.
- W4205569013 hasConcept C154945302 @default.
- W4205569013 hasConcept C165696696 @default.
- W4205569013 hasConcept C203014093 @default.
- W4205569013 hasConcept C2778403875 @default.
- W4205569013 hasConcept C2781133158 @default.
- W4205569013 hasConcept C2781251061 @default.
- W4205569013 hasConcept C31258907 @default.
- W4205569013 hasConcept C35525427 @default.
- W4205569013 hasConcept C38652104 @default.
- W4205569013 hasConcept C41008148 @default.
- W4205569013 hasConcept C86803240 @default.
- W4205569013 hasConcept C8891405 @default.
- W4205569013 hasConceptScore W4205569013C108583219 @default.
- W4205569013 hasConceptScore W4205569013C119857082 @default.
- W4205569013 hasConceptScore W4205569013C154945302 @default.
- W4205569013 hasConceptScore W4205569013C165696696 @default.
- W4205569013 hasConceptScore W4205569013C203014093 @default.
- W4205569013 hasConceptScore W4205569013C2778403875 @default.
- W4205569013 hasConceptScore W4205569013C2781133158 @default.
- W4205569013 hasConceptScore W4205569013C2781251061 @default.
- W4205569013 hasConceptScore W4205569013C31258907 @default.
- W4205569013 hasConceptScore W4205569013C35525427 @default.
- W4205569013 hasConceptScore W4205569013C38652104 @default.
- W4205569013 hasConceptScore W4205569013C41008148 @default.
- W4205569013 hasConceptScore W4205569013C86803240 @default.
- W4205569013 hasConceptScore W4205569013C8891405 @default.
- W4205569013 hasFunder F4320337807 @default.
- W4205569013 hasFunder F4320338295 @default.
- W4205569013 hasLocation W42055690131 @default.
- W4205569013 hasOpenAccess W4205569013 @default.
- W4205569013 hasPrimaryLocation W42055690131 @default.
- W4205569013 hasRelatedWork W1548771250 @default.
- W4205569013 hasRelatedWork W2044668039 @default.
- W4205569013 hasRelatedWork W2162492390 @default.
- W4205569013 hasRelatedWork W2387494004 @default.
- W4205569013 hasRelatedWork W2936028052 @default.
- W4205569013 hasRelatedWork W4295159184 @default.
- W4205569013 hasRelatedWork W4303198045 @default.
- W4205569013 hasRelatedWork W4386160464 @default.
- W4205569013 hasRelatedWork W4386384599 @default.
- W4205569013 hasRelatedWork W2808001300 @default.
- W4205569013 isParatext "false" @default.
- W4205569013 isRetracted "false" @default.
- W4205569013 workType "article" @default.