Integrating Machine Learning and Deep Learning Approaches for Efficient Malware Detection in IoT-Based Smart Cities
Clicks: 1
ID: 313181
2023
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #622 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Smart cities have gained popularity because they promise to address some of the biggest challenges facing urban areas today, such as: traffic congestion, air pollution, energy consumption, waste management, and public safety. A comprehensive study is conducted to enhance the malware detection performance in smart cities by integrating machine learning and IoT-based approaches with deep learning. The study aims to address future challenges in malware detection and improve the effectiveness of strategies used in smart cities. Machine learning algorithms are applied to analyze and classify models’ performance, enhancing computation time and categorial attacks. Deep learning techniques are commended to improve the accuracy and efficiency of malware detection in smart cities. The integration of IoT-based approaches and deep learning enables the detection of various types of malwares in smart cities. The study emphasizes the need for continuous research and development to enhance the performance of malware detection methods in the dynamic ecosystem of smart cities. The dataset was developed in a Unix/Linux-based virtual machine for classification purposes and is safe to use with malware software for Android devices based on the characteristics of the observations. 35 features and 100,000 observation data make up the data set. The results show promising results in terms of detecting malware in smart cities IoT devices.
| Reference Key |
imported_1777059605_69ebc715d7ebe
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Latif Jan |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2023 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.