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Sohail Asghar
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Unresolved referenced work
2002
Unresolved referenced work
Kept as external metadata until matched
Internet holes—part 11: internet lightning rods
10.1016/1353-4858(96)83738-x · 1996
A comprehensive survey on cyber deception techniques to improve honeypot performance
10.1016/j.cose.2024.103792 · 2024
Leveraging computational intelligence techniques for defensive deception: a review, recent advances, open problems and future directions
10.3390/s22062194 · 2022
Cyber deception: taxonomy, state of the art, frameworks, trends, and open challenges
2025
Deep q-network-based open-set intrusion detection solution for industrial internet of things
10.1109/jiot.2023.3333903 · 2023
A survey on hybrid-CNN and LLMs for intrusion detection systems: recent iot datasets
10.1109/access.2024.3506604 · 2024
A collaborative cyber incident management system for european interconnected critical infrastructures
2017
Designing energy-aware collaborative intrusion detection in iot networks
2024
An intelligent deployment policy for deception resources based on reinforcement learning
10.1109/access.2020.2974786 · 2020
A survey on advanced persistent threats: techniques, solutions, challenges, and research opportunities
10.1109/comst.2019.2891891 · 2019
Collaborative intrusion detection in resource-constrained iot environments: challenges, methods, and future directions a review
2025
Hybrid machine learning model for efficient botnet attack detection in iot environment
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Comparative analysis of intrusion detection systems and machine learning-based model analysis through decision tree
10.1109/access.2023.3296444 · 2023
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10.1007/s42979-024-03288-0 · 2024
Dl-sklstm approach for cyber security threats detection in 5g enabled iiot
2024
Zero-day attack detection: a systematic literature review
10.1007/s10462-023-10437-z · 2023
Machine learning approaches to securing iot networks: a comprehensive review of recent advances
2024
Attack classification of an intrusion detection system using deep learning and hyperparameter optimization
2021
A multilayered deep learning framework for cyber attack detection and mitigation in a heterogeneous IIot ecosystem
2026
Bpso-ahdl-ids: binary particle swarm optimization-based automated hybrid deep learning model for intrusion detection of internet of things
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Anomaly detection in industrial control systems based on cross-domain representation learning
2024
A high performance hybrid LSTM CNN secure architecture for iot environments using deep learning
10.1038/s41598-025-94500-5 · doi-reference
Optimizing random forests to detect intrusion in the internet of things
10.1016/j.compeleceng.2024.109860 · doi-reference
Machine learning and deep learning approaches for cybersecurity: a review
10.1109/access.2022.3151248 · doi-reference
A tutorial and use case example of the extreme gradient boosting (XGBoost) artificial intelligence algorithm for drug development applications
10.1111/cts.70172 · doi-reference
Evaluating standard feature sets towards increased generalisability and explainability of ML-based network intrusion detection
10.1016/j.bdr.2022.100359 · doi-reference
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10.1109/jiot.2021.3102056 · doi-reference
Throughput and latency of virtual switching with open vswitch: a quantitative analysis
10.1007/s10922-017-9417-0 · doi-reference
Deception techniques in computer security: a research perspective
10.1145/3214305 · doi-reference
Federated learning on non-IID data: a survey
10.1016/j.neucom.2021.07.098 · doi-reference
Federated learning for iot intrusion detection
10.3390/ai4030028 · doi-reference
A cooperative intrusion detection system for the internet of things using convolutional neural networks and black hole optimization
10.3390/s24154766 · doi-reference
Zero-day attack detection: a systematic literature review
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A deep learning approach for the detection of intrusions with an ensemble feature selection method
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10.1109/comst.2019.2891891 · doi-reference
An intelligent deployment policy for deception resources based on reinforcement learning
10.1109/access.2020.2974786 · doi-reference
A survey on hybrid-CNN and LLMs for intrusion detection systems: recent iot datasets
10.1109/access.2024.3506604 · doi-reference
Deep q-network-based open-set intrusion detection solution for industrial internet of things
10.1109/jiot.2023.3333903 · doi-reference
Leveraging computational intelligence techniques for defensive deception: a review, recent advances, open problems and future directions
10.3390/s22062194 · doi-reference
A comprehensive survey on cyber deception techniques to improve honeypot performance
10.1016/j.cose.2024.103792 · doi-reference
Internet holes—part 11: internet lightning rods
10.1016/1353-4858(96)83738-x · doi-reference
Analytical validation and integration of CIC-bell-DNS-EXF-2021 dataset on security information & event management
10.1109/access.2024.3409413 · doi-reference
Novel intrusion detection strategies with optimal hyper parameters for industrial internet of things based on stochastic games and double deep q-networks
10.1109/jiot.2024.3406386 · doi-reference
Use of machine learning and deep learning in intrusion detection for iot
10.4236/ait.2025.152002 · doi-reference
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10.1007/s10586-024-04509-0 · doi-reference
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10.3390/electronics13132630 · doi-reference
A comprehensive survey on machine learning-based intrusion detection systems for secure communication in internet of things
10.1155/2023/8981988 · doi-reference
Explainable intrusion detection for cyber defences in the internet of things: opportunities and solutions
10.1109/comst.2023.3280465 · doi-reference
Enhancing internet of things security using performance gradient boosting for network intrusion detection systems
10.1016/j.aej.2024.12.106 · doi-reference
Learning-based intrusion detection for high-dimensional imbalanced traffic
10.1016/j.comcom.2023.10.018 · doi-reference
Internet of things: security and solutions survey
10.3390/s22197433 · doi-reference
Sgd3qn: joint stochastic games and dueling double deep q-networks for defending malware propagation in edge intelligence-enabled internet of things
10.1109/tifs.2024.3420233 · doi-reference
A comprehensive survey of cybersecurity threats, attacks, and effective countermeasures in industrial internet of things
10.3390/technologies11060161 · doi-reference
Ai techniques for iot-based ddos attack detection: taxonomies, comprehensive review and research challenges
10.1016/j.cosrev.2024.100631 · doi-reference
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