Abdun Naser Mahmood
Computer Networks and Communications · 2016–2020
A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Computer Networks and Communications — 42 citations into it; #7,911 of 9,886 by trust here, #3,638 by citations
- Artificial Intelligence — 36 citations into it; #28,991 of 35,345 by trust here, #17,412 by citations
- Signal Processing — 25 citations into it; #6,215 of 7,572 by trust here, #3,673 by citations
- Control and Systems Engineering — 7 citations into it
Whom Abdun Naser Mahmood trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Matthew V. Mahoney
- Gideon Creech
- Kingsly Leung
- Marina K. Thottan
- Yufeng Kou
- Trevor J. Hastie
- Yossi Rubner
- Hervé Debar
- Wenjie Hu
- Gerhard Münz
Who trusts Abdun Naser Mahmood overall
Papers from Abdun Naser Mahmood's perspective
Ordered by how strongly the researchers Abdun Naser Mahmood trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Learning
- A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
- Anomaly Detection over Noisy Data using Learned Probability Distributions (International Conference on Machine Learning)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long short-term memory recurrent neural network architectures for large scale acoustic modeling
- ImageNet classification with deep convolutional neural networks
- An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection
- Service specific anomaly detection for network intrusion detection (ACM Symposium on Applied Computing)
Their papers
- TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems (2020) — 684 citations
- A survey of network anomaly detection techniques (2016) — 1,637 citations