Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes

2017 ◽  
Vol 26 (4) ◽  
pp. 1992-2004 ◽  
Author(s):  
Mohammad Sabokrou ◽  
Mohsen Fayyaz ◽  
Mahmood Fathy ◽  
Reinhard Klette
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Deep Neural Networks ◽  
Detection And Localization ◽  
Crowded Scenes

scholarly journals Anomaly Detection and Localization in Crowded Scenes

2014 ◽  
Vol 36 (1) ◽  
pp. 18-32 ◽  
Author(s):  
Weixin Li ◽  
Vijay Mahadevan ◽  
Nuno Vasconcelos
Keyword(s):  
Anomaly Detection ◽  
Detection And Localization ◽  
Crowded Scenes

scholarly journals Enhanced Network Anomaly Detection Based on Deep Neural Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 48231-48246 ◽  
Author(s):  
Sheraz Naseer ◽  
Yasir Saleem ◽  
Shehzad Khalid ◽  
Muhammad Khawar Bashir ◽  
Jihun Han ◽  
...  
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Deep Neural Networks ◽  
Network Anomaly Detection

scholarly journals Generative Neural Networks for Anomaly Detection in Crowded Scenes

2019 ◽  
Vol 14 (5) ◽  
pp. 1390-1399 ◽  
Author(s):  
Tian Wang ◽  
Meina Qiao ◽  
Zhiwei Lin ◽  
Ce Li ◽  
Hichem Snoussi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Crowded Scenes

scholarly journals Model fusion of deep neural networks for anomaly detection

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nouar AlDahoul ◽  
Hezerul Abdul Karim ◽  
Abdulaziz Saleh Ba Wazir
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Denial Of Service ◽  
Traffic Data ◽  
Model Fusion ◽  
Class Weight ◽  
Network Anomaly Detection

AbstractNetwork Anomaly Detection is still an open challenging task that aims to detect anomalous network traffic for security purposes. Usually, the network traffic data are large-scale and imbalanced. Additionally, they have noisy labels. This paper addresses the previous challenges and utilizes million-scale and highly imbalanced ZYELL’s dataset. We propose to train deep neural networks with class weight optimization to learn complex patterns from rare anomalies observed from the traffic data. This paper proposes a novel model fusion that combines two deep neural networks including binary normal/attack classifier and multi-attacks classifier. The proposed solution can detect various network attacks such as Distributed Denial of Service (DDOS), IP probing, PORT probing, and Network Mapper (NMAP) probing. The experiments conducted on a ZYELL’s real-world dataset show promising performance. It was found that the proposed approach outperformed the baseline model in terms of average macro Fβ score and false alarm rate by 17% and 5.3%, respectively.

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