Cyber-XAI-Block: an end-to-end cyber threat detection & fl-based risk assessment framework for iot enabled smart organization using xai and blockchain technologies

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dc.contributor.authorGwassi, Omar Abboosh Hussein
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorNavarro, Enrique A.
dc.date.accessioned2024-09-18T13:28:06Z
dc.date.available2024-09-18T13:28:06Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThe growing integration of the Internet of Things (IoT) in smart organizations is increasing the vulnerability of cyber threats, necessitating advanced frameworks for effective threat detection and risk assessment. Existing works provide achievable results but lack effective solutions, such as detecting Social Engineering Attacks (SEA). Using Deep Learning (DL) and Machine Learning (ML) methods whereas they are limited to validating user behaviors. Like high false positive rates, attack reoccurrence, and increases in numerous attacks. To overcome this problem, we use explainable (DL) techniques to increase cyber security in an IoT-enabled smart organization environment. This paper firstly, implements Capsule Network (CapsNet) to process employee fingerprints and blink patterns. Secondly, the Quantum Key Secure Communication Protocol (QKSCP) was also used to decrease communication channel vulnerabilities like Man In The Middle (MITM) and reply attacks. After Dual Q Network-based Asynchronous Advantage Actor-Critic algorithm DQN-A3C algorithm detects and prevents attacks. Thirdly, employed the explainable DQN-A3C model and the Siamese Inter Lingual Transformer (SILT) transformer for natural language explanations to boost social engineering security by ensuring the Artificial Intelligence (AI) model and human trustworthiness. After, we built a Hopping Intrusion Detection & Prevention System (IDS/IPS) using an explainable Harmonized Google Net (HGN) model with SHAP and SILT explanations to appropriately categorize dangerous external traffic flows. Finally, to improve global, cyberattack comprehension, we created a Federated Learning (FL)-based knowledge-sharing mechanism between Cyber Threat Repository (CTR) and cloud servers, known as global risk assessment. To evaluate the suggested approach, the new method is compared to the ones that already exist in terms of malicious traffic (65 bytes/sec), detection rate (97%), false positive rate (45%), prevention accuracy (98%), end-to-end response time (97 s), recall (96%), false negative rate (42%) and resource consumption (41). Our strategy's performance is examined using numerical analysis, and the results demonstrate that it outperforms other methods in all metrics.en_US
dc.identifier.citationGwassi, O. A. H., Uçan, O. N., Navarro, E. A. (2024). Cyber-XAI-Block: an end-to-end cyber threat detection & fl-based risk assessment framework for iot enabled smart organization using xai and blockchain technologies. Multimedia Tools and Applications. 10.1007/s11042-024-20059-4en_US
dc.identifier.issn1380-7501
dc.identifier.scopus2-s2.0-85203495382
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4921
dc.indekslendigikaynakScopus
dc.institutionauthorGwassi, Omar Abboosh Hussein
dc.institutionauthorUçan, Osman Nuri
dc.language.isoen
dc.publisherSpringeren_US
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.isversionof10.1007/s11042-024-20059-4en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCapsNet algorithmen_US
dc.subjectCyber securityen_US
dc.subjectCyber Threat Repositoryen_US
dc.subjectHarmonized Google Neten_US
dc.subjectIoTen_US
dc.subjectSHapley Additive exPlanationsen_US
dc.titleCyber-XAI-Block: an end-to-end cyber threat detection & fl-based risk assessment framework for iot enabled smart organization using xai and blockchain technologies
dc.typeArticle

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