A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS
Fuente:
arXiv
Saved in:
| Main Authors: | elShehaby, Mohamed, Matrawy, Ashraf |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
by: elShehaby, Mohamed, et al.
Published: (2026)
by: elShehaby, Mohamed, et al.
Published: (2026)
Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones
by: elShehaby, Mohamed, et al.
Published: (2023)
by: elShehaby, Mohamed, et al.
Published: (2023)
Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness
by: elShehaby, Mohamed, et al.
Published: (2024)
by: elShehaby, Mohamed, et al.
Published: (2024)
Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems
by: ElShehaby, Mohamed, et al.
Published: (2025)
by: ElShehaby, Mohamed, et al.
Published: (2025)
Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks
by: Fang, Junyuan, et al.
Published: (2025)
by: Fang, Junyuan, et al.
Published: (2025)
Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS
by: Ennaji, Sabrine, et al.
Published: (2025)
by: Ennaji, Sabrine, et al.
Published: (2025)
Mind the Gap: Missing Cyber Threat Coverage in NIDS Datasets for the Energy Sector
by: Tory, Adrita Rahman, et al.
Published: (2025)
by: Tory, Adrita Rahman, et al.
Published: (2025)
Mitigation of Camouflaged Adversarial Attacks in Autonomous Vehicles--A Case Study Using CARLA Simulator
by: Martinez, Yago Romano, et al.
Published: (2025)
by: Martinez, Yago Romano, et al.
Published: (2025)
Adversarial Attacks on Transformers-Based Malware Detectors
by: Jakhotiya, Yash, et al.
Published: (2022)
by: Jakhotiya, Yash, et al.
Published: (2022)
DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
by: Kang, Mintong, et al.
Published: (2023)
by: Kang, Mintong, et al.
Published: (2023)
StealthRL: Reinforcement Learning Paraphrase Attacks for Multi-Detector Evasion of AI-Text Detectors
by: Ranganath, Suraj, et al.
Published: (2026)
by: Ranganath, Suraj, et al.
Published: (2026)
Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation
by: Liu, Peizhuo
Published: (2025)
by: Liu, Peizhuo
Published: (2025)
EGAN: Evolutional GAN for Ransomware Evasion
by: Commey, Daniel, et al.
Published: (2024)
by: Commey, Daniel, et al.
Published: (2024)
Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability
by: Gao, Junqi, et al.
Published: (2024)
by: Gao, Junqi, et al.
Published: (2024)
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
by: Zhou, Yuyang, et al.
Published: (2023)
by: Zhou, Yuyang, et al.
Published: (2023)
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
by: He, Ping, et al.
Published: (2025)
by: He, Ping, et al.
Published: (2025)
Mitigating the Structural Bias in Graph Adversarial Defenses
by: Fang, Junyuan, et al.
Published: (2025)
by: Fang, Junyuan, et al.
Published: (2025)
SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation
by: Kerdabadi, Mohsen Nayebi, et al.
Published: (2024)
by: Kerdabadi, Mohsen Nayebi, et al.
Published: (2024)
Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks
by: Eykholt, Kevin, et al.
Published: (2024)
by: Eykholt, Kevin, et al.
Published: (2024)
Untargeted Adversarial Attack on Knowledge Graph Embeddings
by: Zhao, Tianzhe, et al.
Published: (2024)
by: Zhao, Tianzhe, et al.
Published: (2024)
On the Robustness of Bayesian Neural Networks to Adversarial Attacks
by: Bortolussi, Luca, et al.
Published: (2022)
by: Bortolussi, Luca, et al.
Published: (2022)
Relationship between Uncertainty in DNNs and Adversarial Attacks
by: Ogonna, Mabel, et al.
Published: (2024)
by: Ogonna, Mabel, et al.
Published: (2024)
Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection
by: Mukherjee, Kunal, et al.
Published: (2026)
by: Mukherjee, Kunal, et al.
Published: (2026)
Fair Finetuning Mitigates Distribution Inference Attacks
by: Naidu, Rakshit
Published: (2026)
by: Naidu, Rakshit
Published: (2026)
FlowPure: Continuous Normalizing Flows for Adversarial Purification
by: Collaert, Elias, et al.
Published: (2025)
by: Collaert, Elias, et al.
Published: (2025)
Syntax- and Compilation-Preserving Evasion of LLM Vulnerability Detectors
by: Sun, Luze, et al.
Published: (2026)
by: Sun, Luze, et al.
Published: (2026)
Vision Transformer with Adversarial Indicator Token against Adversarial Attacks in Radio Signal Classifications
by: Zhang, Lu, et al.
Published: (2025)
by: Zhang, Lu, et al.
Published: (2025)
ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks
by: Ren, Zhiyao, et al.
Published: (2025)
by: Ren, Zhiyao, et al.
Published: (2025)
A White-Box Adversarial Attack Against a Digital Twin
by: Patterson, Wilson, et al.
Published: (2022)
by: Patterson, Wilson, et al.
Published: (2022)
Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems
by: Yakubu, Paul Badu, et al.
Published: (2024)
by: Yakubu, Paul Badu, et al.
Published: (2024)
Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning
by: Domico, Kyle, et al.
Published: (2025)
by: Domico, Kyle, et al.
Published: (2025)
Generalist++: A Meta-learning Framework for Mitigating Trade-off in Adversarial Training
by: Wang, Yisen, et al.
Published: (2025)
by: Wang, Yisen, et al.
Published: (2025)
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
by: Zhang, Yuxiang, et al.
Published: (2024)
by: Zhang, Yuxiang, et al.
Published: (2024)
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis
by: He, Zhipeng, et al.
Published: (2024)
by: He, Zhipeng, et al.
Published: (2024)
Counter-Samples: A Stateless Strategy to Neutralize Black Box Adversarial Attacks
by: Bokobza, Roey, et al.
Published: (2024)
by: Bokobza, Roey, et al.
Published: (2024)
Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights
by: Pourhanifeh, Saba, et al.
Published: (2026)
by: Pourhanifeh, Saba, et al.
Published: (2026)
The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring
by: Hossain, Ismail, et al.
Published: (2026)
by: Hossain, Ismail, et al.
Published: (2026)
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks
by: Zhang, Jiaming, et al.
Published: (2023)
by: Zhang, Jiaming, et al.
Published: (2023)
Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs
by: Panfilov, Alexander, et al.
Published: (2026)
by: Panfilov, Alexander, et al.
Published: (2026)
Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection
by: Zeng, Qirun, et al.
Published: (2025)
by: Zeng, Qirun, et al.
Published: (2025)
Similar Items
-
A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
by: elShehaby, Mohamed, et al.
Published: (2026) -
Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones
by: elShehaby, Mohamed, et al.
Published: (2023) -
Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness
by: elShehaby, Mohamed, et al.
Published: (2024) -
Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems
by: ElShehaby, Mohamed, et al.
Published: (2025) -
Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks
by: Fang, Junyuan, et al.
Published: (2025)