Saved in:
| Main Authors: | Dr. K. Vidya, Shreyas S, Sashank G, Phavankumar R L |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2026
|
| Online Access: | https://doi.org/10.5281/zenodo.20378651 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Cognitive Attack Topology (CAT): A Topological Framework for Modeling and Detecting Human-Centric Cyber Attacks
by: DHADI, SAI PRANEETH REDDY, et al.
Published: (2026)
by: DHADI, SAI PRANEETH REDDY, et al.
Published: (2026)
Enhancing Cyber Defense Against Zero-Day Attacks using Ensemble Neural Networks
by: networkcablingdade, networkcablingdade
Published: (2025)
by: networkcablingdade, networkcablingdade
Published: (2025)
A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data
by: Lotfi, Zahra, et al.
Published: (2025)
by: Lotfi, Zahra, et al.
Published: (2025)
Detecting Zero-Day Attacks in Digital Substations via In-Context Learning
by: Manzoor, Faizan, et al.
Published: (2025)
by: Manzoor, Faizan, et al.
Published: (2025)
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV
by: korba, Abdelaziz Amara, et al.
Published: (2024)
by: korba, Abdelaziz Amara, et al.
Published: (2024)
Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids
by: Zeng, Hanyu, et al.
Published: (2024)
by: Zeng, Hanyu, et al.
Published: (2024)
Analysis of Zero Day Attack Detection Using MLP and XAI
by: Dahal, Ashim, et al.
Published: (2025)
by: Dahal, Ashim, et al.
Published: (2025)
SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks
by: Chowdhury, Meghna Roy, et al.
Published: (2025)
by: Chowdhury, Meghna Roy, et al.
Published: (2025)
Using Graph Theory for Improving Machine Learning-based Detection of Cyber Attacks
by: Zonneveld, Giacomo, et al.
Published: (2024)
by: Zonneveld, Giacomo, et al.
Published: (2024)
Self-Supervised Representation Learning for Adversarial Attack Detection
by: Li, Yi, et al.
Published: (2024)
by: Li, Yi, et al.
Published: (2024)
A Hierarchical IDS for Zero-Day Attack Detection in Internet of Medical Things Networks
by: Uddin, Md Ashraf, et al.
Published: (2025)
by: Uddin, Md Ashraf, et al.
Published: (2025)
Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders
by: Babaey, Vahid, et al.
Published: (2025)
by: Babaey, Vahid, et al.
Published: (2025)
Artificial Intelligence in Cyber Security
by: Dr Vanajakshi 2. Dr. Nandini N 3. Dr. Uma devi 4. Dr. Mukshi S L 5. Dr.Nagarathna M L
Published: (2025)
by: Dr Vanajakshi 2. Dr. Nandini N 3. Dr. Uma devi 4. Dr. Mukshi S L 5. Dr.Nagarathna M L
Published: (2025)
Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks
by: korba, Abdelaziz Amara, et al.
Published: (2024)
by: korba, Abdelaziz Amara, et al.
Published: (2024)
A Novel Solution for Zero-Day Attack Detection in IDS using Self-Attention and Jensen-Shannon Divergence in WGAN-GP
by: Mu, Ziyu, et al.
Published: (2026)
by: Mu, Ziyu, et al.
Published: (2026)
Adaptive Ensemble Learning Framework For Zero-Day Ddos Attack Detection In Software-Defined Networks Using Multi-Dimensional Feature Correlation
by: Nashwan, Riaz, et al.
Published: (2026)
by: Nashwan, Riaz, et al.
Published: (2026)
Representing Time-Continuous Behavior of Cyber-Physical Systems in Knowledge Graphs
by: Gill, Milapji Singh, et al.
Published: (2025)
by: Gill, Milapji Singh, et al.
Published: (2025)
Zero Day Attacks: Novel Behaviour or Novel Vulnerability?
by: Jibunoh, Nnamdi, et al.
Published: (2026)
by: Jibunoh, Nnamdi, et al.
Published: (2026)
Interoperability and Explicable AI-based Zero-Day Attacks Detection Process in Smart Community
by: Sayduzzaman, Mohammad, et al.
Published: (2024)
by: Sayduzzaman, Mohammad, et al.
Published: (2024)
Zero Dynamics Attack Detection and Isolation in Cyber-Physical Systems with Event-triggered Communication
by: Eslami, Ali, et al.
Published: (2025)
by: Eslami, Ali, et al.
Published: (2025)
Dynamic Pruned Ensemble Framework for Zero-Day Attack Detection in Cloud-Native Environments via Adaptive AI-Driven Anomaly Detection
by: Omkar, Kulkarni, et al.
Published: (2026)
by: Omkar, Kulkarni, et al.
Published: (2026)
QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs
by: Wang, Pengyu, et al.
Published: (2024)
by: Wang, Pengyu, et al.
Published: (2024)
SCGIN‐ID: A Self‐Supervised Contrastive Learning Framework For Graph‐Based Network Intrusion Detection
by: Mingyan Li, et al.
Published: (2025)
by: Mingyan Li, et al.
Published: (2025)
A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems
by: Ismail, Nasim Abdirahman, et al.
Published: (2026)
by: Ismail, Nasim Abdirahman, et al.
Published: (2026)
Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution System
by: K.Padmanaban, Malempati Ravichandra
Published: (2025)
by: K.Padmanaban, Malempati Ravichandra
Published: (2025)
Natural Mitigation of Catastrophic Interference: Continual Learning in Power-Law Learning Environments
by: Gandhi, Atith, et al.
Published: (2024)
by: Gandhi, Atith, et al.
Published: (2024)
A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov Games
by: R, Shreyas S, et al.
Published: (2024)
by: R, Shreyas S, et al.
Published: (2024)
Dual Detection Framework for Faults and Integrity Attacks in Cyber-Physical Control Systems
by: Xue, Xixing, et al.
Published: (2025)
by: Xue, Xixing, et al.
Published: (2025)
SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning
by: Ivanovska, Marija, et al.
Published: (2025)
by: Ivanovska, Marija, et al.
Published: (2025)
Self-Supervised Learning of Graph Representations for Network Intrusion Detection
by: Guerra, Lorenzo, et al.
Published: (2025)
by: Guerra, Lorenzo, et al.
Published: (2025)
Lightweight Neuromorphic‐Temporal Graph Framework for Proactive Defense Against Evolving Cyber Attacks
by: Haythem Hayouni, et al.
Published: (2025)
by: Haythem Hayouni, et al.
Published: (2025)
A Neural Network Model of Complementary Learning Systems: Pattern Separation and Completion for Continual Learning
by: Jun, James P, et al.
Published: (2025)
by: Jun, James P, et al.
Published: (2025)
Zero-Day Threats Detection for Critical Infrastructures
by: Nkongolo, Mike, et al.
Published: (2023)
by: Nkongolo, Mike, et al.
Published: (2023)
Marlin: Knowledge-Driven Analysis of Provenance Graphs for Efficient and Robust Detection of Cyber Attacks
by: Li, Zhenyuan, et al.
Published: (2024)
by: Li, Zhenyuan, et al.
Published: (2024)
Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches
by: Yin, Tianzhixi, et al.
Published: (2024)
by: Yin, Tianzhixi, et al.
Published: (2024)
PrivFly: A Privacy-Preserving Self-Supervised Framework for Rare Attack Detection in IoFT
by: Menssouri, Safaa, et al.
Published: (2026)
by: Menssouri, Safaa, et al.
Published: (2026)
Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
by: Capellán-Martín, Daniel, et al.
Published: (2024)
by: Capellán-Martín, Daniel, et al.
Published: (2024)
Framework of Resilient Transmission Network Reconfiguration Considering Cyber-Attacks
by: Yang, Chao, et al.
Published: (2024)
by: Yang, Chao, et al.
Published: (2024)
SER-Diff: Synthetic Error Replay Diffusion for Incremental Brain Tumor Segmentation
by: Makanaboyina, Sashank
Published: (2025)
by: Makanaboyina, Sashank
Published: (2025)
NNDM: NN_UNet Diffusion Model for Brain Tumor Segmentation
by: Makanaboyina, Sashank
Published: (2025)
by: Makanaboyina, Sashank
Published: (2025)
Similar Items
-
Cognitive Attack Topology (CAT): A Topological Framework for Modeling and Detecting Human-Centric Cyber Attacks
by: DHADI, SAI PRANEETH REDDY, et al.
Published: (2026) -
Enhancing Cyber Defense Against Zero-Day Attacks using Ensemble Neural Networks
by: networkcablingdade, networkcablingdade
Published: (2025) -
A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data
by: Lotfi, Zahra, et al.
Published: (2025) -
Detecting Zero-Day Attacks in Digital Substations via In-Context Learning
by: Manzoor, Faizan, et al.
Published: (2025) -
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV
by: korba, Abdelaziz Amara, et al.
Published: (2024)