Smart intrusion detection system with balanced data in IoMT infra
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Post-publication record
- Nature
- Retraction
- Reason
- 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
- Flagged by OpenAlex?
- Yes
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement — it reports them as false, which reads as “fine”.
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Journal of Intelligent & Fuzzy Systems
- Topic
- Network Security and Intrusion Detection
- Field
- Computer Science
- Canadian institutions
- —
- Funders
- —
- Keywords
- Computer scienceIntrusion detection systemData miningExploitArtificial intelligenceBotnetComputer securityMachine learningThe Internet
- Has abstract in OpenAlex
- yes