Vitorino, 2021 - Google Patents
IoT intrusion detection through machine learningVitorino, 2021
View PDF- Document ID
- 3291736407072031411
- Author
- Vitorino J
- Publication year
External Links
Snippet
The digital transformation faces great security challenges. In particular, the Internet of Things (IoT), a concept that expresses the interconnection of physical objects with the Internet, is exposed to several threats. The growing number of cyber attacks targeting IoT systems …
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | Federated feature selection for horizontal federated learning in IoT networks | |
US11770398B1 (en) | Guided anomaly detection framework | |
Han et al. | Unicorn: Runtime provenance-based detector for advanced persistent threats | |
US11785104B2 (en) | Learning from similar cloud deployments | |
Mahindru et al. | MLDroid—framework for Android malware detection using machine learning techniques | |
US11894984B2 (en) | Configuring cloud deployments based on learnings obtained by monitoring other cloud deployments | |
US11818156B1 (en) | Data lake-enabled security platform | |
US11973784B1 (en) | Natural language interface for an anomaly detection framework | |
US9323938B2 (en) | Holistic XACML and obligation code automatically generated from ontologically defined rule set | |
US12058160B1 (en) | Generating computer code for remediating detected events | |
Silva et al. | Towards federated learning: An overview of methods and applications | |
Nobakht et al. | SIM-FED: Secure IoT malware detection model with federated learning | |
US12323449B1 (en) | Code analysis feedback loop for code created using generative artificial intelligence (‘AI’) | |
Collins et al. | Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence | |
Alkhabbas et al. | Assert: A blockchain-based architectural approach for engineering secure self-adaptive iot systems | |
Patel | Attack detection and mitigation scheme through novel authentication model enabled optimized neural network in smart healthcare | |
Arjunan | Fraud Detection in NoSQL Database Systems using Advanced Machine Learning | |
Gamal et al. | Improving intrusion detection using LSTM-RNN to protect drones’ networks | |
Kanna et al. | An enhanced hybrid intrusion detection using mapreduce-optimized black widow convolutional lstm neural networks | |
Saied et al. | A comparative analysis of using ensemble trees for botnet detection and classification in IoT | |
Amjath et al. | Graph representation federated learning for malware detection in internet of health things | |
Rysbekov et al. | Advancing network security: a comparative research of machine learning techniques for intrusion detection. | |
Sui et al. | Edge computing and AIoT based network intrusion detection mechanism | |
Vitorino | IoT intrusion detection through machine learning | |
Sharma et al. | Blockchain-based zero trust networks with federated transfer learning for IoT security in industry 5.0 |