An Improved Deep Belief Network IDS on IoT-Based Network for Traffic Systems
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
- Compromised Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Paper Mill;Unreliable Results and/or Conclusions;
- Date
- 12/13/2023 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
Internet of things (IoT) services are turning out to be more domineering with the rising security considerations fading with time. All this owes to the propagating heterogeneity and budding technologies teamed up with resource-constrained IoT systems, sculpting smart systems to be more susceptible to cyber-attacks. The security challenges such as privacy, scalability, authenticity, trust, and centralization thwart the quick adaptation of the smart services; hence, effective solutions are needed to be in place. Traditional approaches of intrusion detection mechanisms have become irrelevant now, as the bad actors often use obfuscation techniques to evade detections. Moreover, these techniques collapse, while detecting zero-day attacks. Hence, there is a need to use an intelligent mechanism based on machine learning (ML) and deep learning (DL), to detect attacks. In this study, the authors have proposed an intrusion detection engine with a deep belief network (DBN) being the core. The implementation of DBN_Classifier is performed using TensorFlow 2.0 and evaluated using a sample of the TON_IOT_Weather dataset. The findings indicate that the proposed engine outperforms the other state-of-the-art techniques with an average accuracy of 86.3%.
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 Advanced Transportation
- Topic
- Network Security and Intrusion Detection
- Field
- Computer Science
- Canadian institutions
- —
- Funders
- —
- Keywords
- Computer scienceScalabilityComputer securityIntrusion detection systemInternet of ThingsArtificial intelligenceObfuscationDeep learningDeep belief networkMachine learningNetwork security
- Has abstract in OpenAlex
- yes