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https://doi.org/10.2139/ssrn.4597143

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10207-019-00482-7openalex-extracted
A context-aware robust intrusion detection system: a reinforcement learning-based approach
inferredcites →https://doi.org/10.1016/b978-0-32-385787-1.00010-5openalex-extracted
Recurrent neural networks
inferredcites →https://doi.org/10.1016/j.comnet.2018.07.025openalex-extracted
An evaluation of the performance of Restricted Boltzmann Machines as a model for anomaly network intrusion detection
inferredcites →https://doi.org/10.1016/j.comnet.2019.05.013openalex-extracted
Adversarial environment reinforcement learning algorithm for intrusion detection
inferredcites →https://doi.org/10.1016/j.cose.2019.101645openalex-extracted
A dynamic MLP-based DDoS attack detection method using feature selection and feedback
inferredcites →https://doi.org/10.1016/j.cose.2020.102108openalex-extracted
Automating post-exploitation with deep reinforcement learning
inferredcites →https://doi.org/10.1016/j.cose.2023.103210openalex-extracted
CPS-GUARD: Intrusion detection for cyber-physical systems and IoT devices using outlier-aware deep autoencoders
inferredcites →https://doi.org/10.1016/j.eswa.2019.112963openalex-extracted
Application of deep reinforcement learning to intrusion detection for supervised problems
inferredcites →https://doi.org/10.1016/j.ins.2018.04.092openalex-extracted
Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders
inferredcites →https://doi.org/10.1080/23742917.2018.1495375openalex-extracted
Improving adaptive honeypot functionality with efficient reinforcement learning parameters for automated malware
inferredcites →https://doi.org/10.1109/access.2020.3022862openalex-extracted
TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems
inferredcites →https://doi.org/10.1109/bigdata50022.2020.9378491openalex-extracted
Using Knowledge Graphs and Reinforcement Learning for Malware Analysis
inferredcites →https://doi.org/10.1109/ccwc57344.2023.10099349openalex-extracted
Applying Deep Reinforcement Learning for Detection of Internet-of- Things Cyber Attacks
inferredcites →https://doi.org/10.1109/cisda.2009.5356528openalex-extracted
A detailed analysis of the KDD CUP 99 data set
inferredcites →https://doi.org/10.1109/comst.2015.2402161openalex-extracted
Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset
inferredcites →https://doi.org/10.1109/eurospw51379.2020.00010openalex-extracted
Automated Penetration Testing Using Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/iacs.2017.7921994openalex-extracted
Automated malicious advertisement detection using VirusTotal, URLVoid, and TrendMicro
inferredcites →https://doi.org/10.1109/jiot.2021.3085194openalex-extracted
ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets
inferredcites →https://doi.org/10.1109/milcis.2015.7348942openalex-extracted
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
inferredcites →https://doi.org/10.1109/noms47738.2020.9110453openalex-extracted
A Hybrid Game Theory and Reinforcement Learning Approach for Cyber-Physical Systems Security
inferredcites →https://doi.org/10.1109/smartcloud.2019.00046openalex-extracted
An adversarial reinforcement learning based system for cyber security
inferredcites →https://doi.org/10.1109/trustcom50675.2020.00100openalex-extracted
Data Analytics-Enabled Intrusion Detection: Evaluations of ToN_IoT Linux Datasets
inferredcites →https://doi.org/10.1109/trustcom50675.2020.00114openalex-extracted
Federated TON_IoT Windows Datasets for Evaluating AI-Based Security Applications
inferredcites →https://doi.org/10.1145/2882969openalex-extracted
Dynamic Scheduling of Cybersecurity Analysts for Minimizing Risk Using Reinforcement Learning
inferredcites →https://doi.org/10.1186/s42400-019-0038-7openalex-extracted
Survey of intrusion detection systems: techniques, datasets and challenges
inferredcites →https://doi.org/10.3390/info11010006openalex-extracted
Reinforcement Learning for Efficient Network Penetration Testing
inferredcites →https://doi.org/10.3390/s20113048openalex-extracted
Teaching and Learning IoT Cybersecurity and Vulnerability Assessment with Shodan through Practical Use Cases
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-extracted
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1507.06527openalex-extracted
Deep Recurrent Q-Learning for Partially Observable MDPs
inferredcites →https://doi.org/10.48550/arxiv.2004.00668openalex-extracted
Understanding Global Feature Contributions With Additive Importance Measures
inferredcites →https://doi.org/10.5220/0006197105590566openalex-extracted
Adversarial Reinforcement Learning in a Cyber Security Simulation

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inferred← citeshttps://doi.org/10.1007/978-3-032-07915-2_15openalex-snapshot
A Comprehensive Review of Cyber-Attack Detection in IoT Environments: Datasets and Approaches
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