Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing

By Abebe Abeshu Diro, Naveen K. Chilamkurti. IEEE Communications Magazine, 2018

326 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a302000c290f3a36954de8dab01dcec409132430

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Citations in the corpus

Federated Learning in Edge Computing: A Systematic Survey (2022, background): “Furthermore, the authors of [149] proposed a distributed deep-learning scheme of cyber-attack detection in fog-to-things computing, which they considered in terms of accuracy, detection rate, and scalability, as the metrics of the model.”
Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “[2] apply federated learning to network attack detection, and propose a network attack detection model based on federated learning-authorized edge network.”
Deep Learning Models for Cyber Security in IoT Networks (2019, unknown): “proposed in [12].”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, methodology): “[56] Distributed DL Reduce the burden of model training and improve the accuracy of the model”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, background): “Chilamkurti [137] proposed a distributed DL-based attack detection mechanism in IoT.”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “Similarly, [265] proposed a distributed DL model to deliver”
A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “Diro and Chilamkurti [146] tested a DL algorithm’s ability to detect intrusion detection in IoT with a fog ecosystem, which is an architectural style in which computation is shifted from the cloud to the edge of the network routed over the network backbone closer to the user and devices.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “The use of DL for cyber-attack detection in edge networks was considered in [254], where the experiments demonstrate that the DL based model is better than that with a shallow model in terms of learning accuracy, detection rate, and false alarm rate.”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “In both [172] and [173], it is assumed that the participants, i.”
Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations (2019, background): “IoT security [204]–[207], deep learning methodologies for inferring and characterizing IoT maliciousness [208], [209], and the adoption of SDN and cloud paradigms for IoT resiliency [210]–[212].”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “Reference [262] uses deep learning to detect the IoT cyber-attacks in edge networks.”
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, unknown): “Security & Privacy [66, 82, 99, 102, 104, 105, 108, 114, 163, 165, 178, 282, 283, 304, 313, 326, 337, 339, 341, 349, 364, 368, 371, 373, 389, 419–433]”