A Hybrid Approach for the Detection and Classification of MQTT-based IoT-Malware
Author:
Affiliation:
1. Graphic Era Hill University,Department of Computer Science and Engineering,Dehradun,India
2. Graphic Era Deemed to be University,Department of Computer Science and Engineering,Dehradun,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10104520/10104522/10104820.pdf?arnumber=10104820
Reference19 articles.
1. A Quantitative Study of Small Dataset Machining by Agglomerative Hierarchical Cluster and K-Medoid;garikapati;Lecture Notes in Electrical Engineering,2022
2. Malware Detection Issues, Challenges, and Future Directions: A Survey;aboaoja;Applied Sciences,2022
3. IoT-Based Android Malware Detection Using Graph Neural Network With Adversarial Defense
4. Cloud-based secure data storage and access control for internet of medical things using federated learning
5. BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning
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1. SmartGuardML: ML-based MQTT Data Analysis Approach for Threat Prediction in Smart Homes;2024 8th International Conference on Smart Cities, Internet of Things and Applications (SCIoT);2024-05-14
2. A Review of State-of-the-Art Malware Attack Trends and Defense Mechanisms;IEEE Access;2023
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