Smart intrusion detection system with balanced data in IoMT infra
Pourquoi ce travail est-il dans la base ?
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Aucune affiliation canadienne. Une base fondée sur la seule affiliation (le devis habituel) n'aurait jamais vu ce travail. C'est l'un des travaux qui justifient l'inversion de la base.
Dossier post-publication
- Nature
- Retraction
- Motif
- Computer-Aided Content or Computer-Generated Content;Concerns/Issues about Authorship/Affiliation;Concerns/Issues about Referencing/Attributions;Concerns/Issues about Third Party Involvement;Compromised Peer Review;False/Forged Authorship;Investigation by Journal/Publisher;Investigation by Third Party;Paper Mill;Unreliable Results and/or Conclusions;
- Date
- 1/15/2025 0:00
- Signalé par OpenAlex ?
- Oui
Source : Retraction Watch, jointe par DOI. OpenAlex consigne la rétractation dans is_retracted, un booléen sur un espace d'états à au moins quatre valeurs ; il ne peut donc exprimer ni une expression de préoccupation, ni une correction, ni un rétablissement, et les rapporte comme false, ce qui se lit comme « rien à signaler ».
Résumé
The IoMT (Internet of Medical Things) has allowed for uninterrupted, critical patient observation, improved diagnosis precision, and efficient therapy. However, despite the usefulness of such medical things (devices), they also raise a lot of confidentiality and security issues since they provide potential entry points for hackers to exploit. Therefore, there is a pressing need for a technique for detecting network intrusions that combines precision, flexibility, and consistency. Addressing diverse information sources is challenging for finding negligible intrusions in sophisticated network systems, a core problem for current Intrusion Detection Systems (IDS). In this research, we propose a deep learning-based method for efficient network IDS in cases when data is unevenly distributed. Therefore, to address the poor identification rate of intrusions, we present a unique CGAN-CNN (Conditional Generative Adversarial Network-Convolutional Neural Network) IDS approach that oversamples from the unbalanced information based on the CGAN paradigm to overcome the functional deterioration induced by such unbalanced data, especially during intrusion detection. In addition, the sub-networks’ critic and generator each get additional constraints as part of the CGAN’s standard operating procedure, which helps to reduce the amount of leeway in the convergence process and speeds up the impact of convergence. To validate the effectiveness of the suggested model, we conducted an investigation using the most contemporary publicly available datasets, namely NIDS (Network Intrusion Detection System), and the CICDDoS2019 (Canadian Institute for Cybersecurity Distributed Denial of Service 2019) dataset from the Canadian Institution for Cybersecurity, and for healthcare-oriented image datasets Kaggle, respectively. The experimental findings validated the superiority of the CGAN-CNN approach described in this research. Notified as more trustworthy indications, F1-score and precision performed at 97.88%, and 97.15%, respectively.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
La notice
- Revue
- Journal of Intelligent & Fuzzy Systems
- Thématique
- Network Security and Intrusion Detection
- Domaine
- Computer Science
- Établissements canadiens
- —
- Organismes subventionnaires
- —
- Mots-clés
- Computer scienceIntrusion detection systemData miningExploitArtificial intelligenceBotnetComputer securityMachine learningThe Internet
- Résumé présent dans OpenAlex
- oui