Select Publications
Preprints
2022, Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection, http://dx.doi.org/10.21203/rs.3.rs-1631421/v1
,2021, A Deep Learning-based Penetration Testing Framework for Vulnerability Identification in Internet of Things Environments, http://dx.doi.org/10.48550/arxiv.2109.09259
,2021, Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks, http://dx.doi.org/10.48550/arxiv.2108.12722
,2021, Hunter in the Dark: Discover Anomalous Network Activity Using Deep Ensemble Network, http://dx.doi.org/10.48550/arxiv.2105.09157
,2021, Security and Privacy for Artificial Intelligence: Opportunities and Challenges, http://dx.doi.org/10.48550/arxiv.2102.04661
,2020, A Deep Marginal-Contrastive Defense against Adversarial Attacks on 1D Models, http://dx.doi.org/10.48550/arxiv.2012.04734
,2020, Mitigating the Impact of Adversarial Attacks in Very Deep Networks, http://dx.doi.org/10.48550/arxiv.2012.04750
,2020, NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems, http://dx.doi.org/10.48550/arxiv.2011.09144
,2020, Federated TON_IoT Windows Datasets for Evaluating AI-based Security Applications, http://dx.doi.org/10.48550/arxiv.2010.08522
,2020, Pelican: A Deep Residual Network for Network Intrusion Detection, http://dx.doi.org/10.48550/arxiv.2001.08523
,2018, Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset, http://dx.doi.org/10.48550/arxiv.1811.00701
,2017, Collaborative Anomaly Detection Framework for handling Big Data of Cloud Computing, http://dx.doi.org/10.48550/arxiv.1711.02829
,2017, Privacy Preservation Intrusion Detection Technique for SCADA Systems, http://dx.doi.org/10.48550/arxiv.1711.02828
,2017, Probability Risk Identification Based Intrusion Detection System for SCADA Systems, http://dx.doi.org/10.48550/arxiv.1711.02826
,2017, RCNF: Real-time Collaborative Network Forensic Scheme for Evidence Analysis, http://dx.doi.org/10.48550/arxiv.1711.02824
,2017, Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques, http://dx.doi.org/10.48550/arxiv.1711.02825
,2017, A hybrid feature selection for network intrusion detection systems: Central points, http://dx.doi.org/10.48550/arxiv.1707.05505
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