Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367309662> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W4367309662 endingPage "119000" @default.
- W4367309662 startingPage "119000" @default.
- W4367309662 abstract "Although the field of eXplainable Artificial Intelligence (XAI) has a significant interest these days, its implementation within cyber security applications still needs further investigation to understand its effectiveness in discovering attack surfaces and vectors. In cyber defence, especially anomaly-based Intrusion Detection Systems (IDS), the emerging applications of machine/deep learning models require the interpretation of the models' architecture and the explanation of models' prediction to examine how cyberattacks would occur. This paper proposes a novel explainable intrusion detection framework in the Internet of Things (IoT) networks. We have developed an IDS using a Short-Term Long Memory (LSTM) model to identify cyberattacks and explain the model's decisions. This uses a novel set of input features extracted by a novel SPIP (S: Shapley Additive exPlanations, P: Permutation Feature Importance, I: Individual Conditional Expectation, P: Partial Dependence Plot) framework to train and evaluate the LSTM model. The framework was validated using the NSL-KDD, UNSW-NB15 and TON_IoT datasets. The SPIP framework achieved high detection accuracy, processing time, and high interpretability of data features and model outputs compared with other peer techniques. The proposed framework has the potential to assist administrators and decision-makers in understanding complex attack behaviour." @default.
- W4367309662 created "2023-04-29" @default.
- W4367309662 creator A5015993565 @default.
- W4367309662 creator A5022626159 @default.
- W4367309662 creator A5056050345 @default.
- W4367309662 creator A5063382171 @default.
- W4367309662 creator A5075314147 @default.
- W4367309662 creator A5089327837 @default.
- W4367309662 date "2023-08-01" @default.
- W4367309662 modified "2023-09-23" @default.
- W4367309662 title "An explainable deep learning-enabled intrusion detection framework in IoT networks" @default.
- W4367309662 cites W2017380649 @default.
- W4367309662 cites W2064675550 @default.
- W4367309662 cites W2107878631 @default.
- W4367309662 cites W2150355110 @default.
- W4367309662 cites W2150847526 @default.
- W4367309662 cites W2625602624 @default.
- W4367309662 cites W2896632748 @default.
- W4367309662 cites W2904539465 @default.
- W4367309662 cites W2911964244 @default.
- W4367309662 cites W2948056727 @default.
- W4367309662 cites W2986055611 @default.
- W4367309662 cites W2990268365 @default.
- W4367309662 cites W3025093231 @default.
- W4367309662 cites W3043799819 @default.
- W4367309662 cites W3085955590 @default.
- W4367309662 cites W3092771185 @default.
- W4367309662 cites W3149084432 @default.
- W4367309662 cites W3151784567 @default.
- W4367309662 cites W3161599138 @default.
- W4367309662 cites W3162956350 @default.
- W4367309662 doi "https://doi.org/10.1016/j.ins.2023.119000" @default.
- W4367309662 hasPublicationYear "2023" @default.
- W4367309662 type Work @default.
- W4367309662 citedByCount "2" @default.
- W4367309662 countsByYear W43673096622023 @default.
- W4367309662 crossrefType "journal-article" @default.
- W4367309662 hasAuthorship W4367309662A5015993565 @default.
- W4367309662 hasAuthorship W4367309662A5022626159 @default.
- W4367309662 hasAuthorship W4367309662A5056050345 @default.
- W4367309662 hasAuthorship W4367309662A5063382171 @default.
- W4367309662 hasAuthorship W4367309662A5075314147 @default.
- W4367309662 hasAuthorship W4367309662A5089327837 @default.
- W4367309662 hasBestOaLocation W43673096621 @default.
- W4367309662 hasConcept C119857082 @default.
- W4367309662 hasConcept C124101348 @default.
- W4367309662 hasConcept C138885662 @default.
- W4367309662 hasConcept C154945302 @default.
- W4367309662 hasConcept C177264268 @default.
- W4367309662 hasConcept C199360897 @default.
- W4367309662 hasConcept C202444582 @default.
- W4367309662 hasConcept C2776401178 @default.
- W4367309662 hasConcept C2781067378 @default.
- W4367309662 hasConcept C33923547 @default.
- W4367309662 hasConcept C35525427 @default.
- W4367309662 hasConcept C41008148 @default.
- W4367309662 hasConcept C41895202 @default.
- W4367309662 hasConcept C9652623 @default.
- W4367309662 hasConceptScore W4367309662C119857082 @default.
- W4367309662 hasConceptScore W4367309662C124101348 @default.
- W4367309662 hasConceptScore W4367309662C138885662 @default.
- W4367309662 hasConceptScore W4367309662C154945302 @default.
- W4367309662 hasConceptScore W4367309662C177264268 @default.
- W4367309662 hasConceptScore W4367309662C199360897 @default.
- W4367309662 hasConceptScore W4367309662C202444582 @default.
- W4367309662 hasConceptScore W4367309662C2776401178 @default.
- W4367309662 hasConceptScore W4367309662C2781067378 @default.
- W4367309662 hasConceptScore W4367309662C33923547 @default.
- W4367309662 hasConceptScore W4367309662C35525427 @default.
- W4367309662 hasConceptScore W4367309662C41008148 @default.
- W4367309662 hasConceptScore W4367309662C41895202 @default.
- W4367309662 hasConceptScore W4367309662C9652623 @default.
- W4367309662 hasFunder F4320334704 @default.
- W4367309662 hasLocation W43673096621 @default.
- W4367309662 hasOpenAccess W4367309662 @default.
- W4367309662 hasPrimaryLocation W43673096621 @default.
- W4367309662 hasRelatedWork W2366221835 @default.
- W4367309662 hasRelatedWork W3006943036 @default.
- W4367309662 hasRelatedWork W3163334550 @default.
- W4367309662 hasRelatedWork W4200511449 @default.
- W4367309662 hasRelatedWork W4206534706 @default.
- W4367309662 hasRelatedWork W4229079080 @default.
- W4367309662 hasRelatedWork W4299487748 @default.
- W4367309662 hasRelatedWork W4385957992 @default.
- W4367309662 hasRelatedWork W4385965371 @default.
- W4367309662 hasRelatedWork W4386025632 @default.
- W4367309662 hasVolume "639" @default.
- W4367309662 isParatext "false" @default.
- W4367309662 isRetracted "false" @default.
- W4367309662 workType "article" @default.