Matches in SemOpenAlex for { <https://semopenalex.org/work/W4287395004> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W4287395004 abstract "Domain Generation Algorithms (DGAs) are used by adversaries to establish Command and Control (C&C) server communications during cyber attacks. Blacklists of known/identified C&C domains are often used as one of the defense mechanisms. However, since blacklists are static and generated by signature-based approaches, they can neither keep up nor detect never-seen-before malicious domain names. Due to this shortcoming of blacklist domain checking, machine learning algorithms have been used to address the problem to some extent. However, when training is performed with limited datasets, the algorithms are likely to fail in detecting new DGA variants. To mitigate this weakness, we successfully applied a DGA-based malicious domain classifier using the Long Short-Term Memory (LSTM) method with a novel feature engineering technique. Our model's performance shows a higher level of accuracy compared to a previously reported model from prior research. Additionally, we propose a new method using adversarial machine learning to generate never-before-seen malware-related domain families that can be used to illustrate the shortcomings of machine learning algorithms in this regard. Next, we augment the training dataset with new samples such that it makes training of the machine learning models more effective in detecting never-before-seen malicious domain name variants. Finally, to protect blacklists of malicious domain names from disclosure and tampering, we devise secure data containers that store blacklists and guarantee their protection against adversarial access and modifications." @default.
- W4287395004 created "2022-07-25" @default.
- W4287395004 creator A5037236351 @default.
- W4287395004 creator A5063272576 @default.
- W4287395004 creator A5072509690 @default.
- W4287395004 date "2021-01-02" @default.
- W4287395004 modified "2023-10-17" @default.
- W4287395004 title "Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning" @default.
- W4287395004 doi "https://doi.org/10.48550/arxiv.2101.00521" @default.
- W4287395004 hasPublicationYear "2021" @default.
- W4287395004 type Work @default.
- W4287395004 citedByCount "0" @default.
- W4287395004 crossrefType "posted-content" @default.
- W4287395004 hasAuthorship W4287395004A5037236351 @default.
- W4287395004 hasAuthorship W4287395004A5063272576 @default.
- W4287395004 hasAuthorship W4287395004A5072509690 @default.
- W4287395004 hasBestOaLocation W42873950041 @default.
- W4287395004 hasConcept C110875604 @default.
- W4287395004 hasConcept C119857082 @default.
- W4287395004 hasConcept C134306372 @default.
- W4287395004 hasConcept C136764020 @default.
- W4287395004 hasConcept C138885662 @default.
- W4287395004 hasConcept C154945302 @default.
- W4287395004 hasConcept C2776401178 @default.
- W4287395004 hasConcept C2779797433 @default.
- W4287395004 hasConcept C2781345505 @default.
- W4287395004 hasConcept C33923547 @default.
- W4287395004 hasConcept C35026560 @default.
- W4287395004 hasConcept C36503486 @default.
- W4287395004 hasConcept C37736160 @default.
- W4287395004 hasConcept C38652104 @default.
- W4287395004 hasConcept C41008148 @default.
- W4287395004 hasConcept C41895202 @default.
- W4287395004 hasConcept C51632099 @default.
- W4287395004 hasConcept C541664917 @default.
- W4287395004 hasConcept C95623464 @default.
- W4287395004 hasConceptScore W4287395004C110875604 @default.
- W4287395004 hasConceptScore W4287395004C119857082 @default.
- W4287395004 hasConceptScore W4287395004C134306372 @default.
- W4287395004 hasConceptScore W4287395004C136764020 @default.
- W4287395004 hasConceptScore W4287395004C138885662 @default.
- W4287395004 hasConceptScore W4287395004C154945302 @default.
- W4287395004 hasConceptScore W4287395004C2776401178 @default.
- W4287395004 hasConceptScore W4287395004C2779797433 @default.
- W4287395004 hasConceptScore W4287395004C2781345505 @default.
- W4287395004 hasConceptScore W4287395004C33923547 @default.
- W4287395004 hasConceptScore W4287395004C35026560 @default.
- W4287395004 hasConceptScore W4287395004C36503486 @default.
- W4287395004 hasConceptScore W4287395004C37736160 @default.
- W4287395004 hasConceptScore W4287395004C38652104 @default.
- W4287395004 hasConceptScore W4287395004C41008148 @default.
- W4287395004 hasConceptScore W4287395004C41895202 @default.
- W4287395004 hasConceptScore W4287395004C51632099 @default.
- W4287395004 hasConceptScore W4287395004C541664917 @default.
- W4287395004 hasConceptScore W4287395004C95623464 @default.
- W4287395004 hasLocation W42873950041 @default.
- W4287395004 hasOpenAccess W4287395004 @default.
- W4287395004 hasPrimaryLocation W42873950041 @default.
- W4287395004 hasRelatedWork W10100871 @default.
- W4287395004 hasRelatedWork W10719664 @default.
- W4287395004 hasRelatedWork W10917997 @default.
- W4287395004 hasRelatedWork W3968525 @default.
- W4287395004 hasRelatedWork W4081608 @default.
- W4287395004 hasRelatedWork W449952 @default.
- W4287395004 hasRelatedWork W482721 @default.
- W4287395004 hasRelatedWork W4861383 @default.
- W4287395004 hasRelatedWork W7655147 @default.
- W4287395004 hasRelatedWork W9657784 @default.
- W4287395004 isParatext "false" @default.
- W4287395004 isRetracted "false" @default.
- W4287395004 workType "article" @default.