Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1750
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dc.contributor.authorMadhuvantha, K. A. N-
dc.contributor.authorHussain, M. H-
dc.contributor.authorDe Silva, H. W. D. T-
dc.contributor.authorLiyanage, U. I. D-
dc.contributor.authorRupasinghe, L-
dc.contributor.authorLiyanapathirana, C-
dc.date.accessioned2022-03-23T03:21:44Z-
dc.date.available2022-03-23T03:21:44Z-
dc.date.issued2021-12-09-
dc.identifier.citationK. A. N. Madhuvantha, M. H. Hussain, H. W. D. T. De Silva, U. I. D. Liyanage, L. Rupasinghe and C. Liyanapathirana, "Autonomous Cyber AI for Anomaly Detection," 2021 3rd International Conference on Advancements in Computing (ICAC), 2021, pp. 85-90, doi: 10.1109/ICAC54203.2021.9671203.en_US
dc.identifier.isbn978-1-6654-0862-2-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1750-
dc.description.abstractSince available signature-based Intrusion Detection systems (IDS) are lacking in performance to identify such cyber threats and defend against novel attacks. It does not have the ability to detect zero-day or advanced malicious activities. To address the issue with signature-based IDS, a possible solution is to adopt anomaly-based detections to identify the latest cyber threats including zero days. We initially focused on network intrusions. This research paper discusses detecting network anomalies using AI-based technologies such as machine learning (ML) and natural language processing (NLP). In the proposed solution, network traffic logs and HTTP traffic data are taken as inputs using a mechanism called beats. Once relevant data has been extracted from the captured traffic, it will be passed to the AI engine to conduct further analysis. Algorithms such as Word2vec, Convolution Neural Network (CNN), Artificial Neural networks (ANN), and autoencoders are used in order to conduct the threat analysis. HTTP DATASET CSIC 2010, that NSL-KDD, CICIDS are the benchmarking datasets used in parallel with the above algorithms in order to receive high accuracy in detection. The outputted data is integrated and visualized using the Kibana dashboard and blockchain model is implemented to maintain and handle all the data.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2021 3rd International Conference on Advancements in Computing (ICAC);Pages 85-90-
dc.subjectAutonomousen_US
dc.subjectCyber AIen_US
dc.subjectAnomaly Detectionen_US
dc.titleAutonomous Cyber AI for Anomaly Detectionen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC54203.2021.9671203en_US
Appears in Collections:Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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