Honeypot Behaviour Patterns Learning for Mass Email Marketing Detection

Author:

Kamati Toivo Herman1,Jat Dharm Singh1

Affiliation:

1. Computer Science Department, Namibia University of Science and Technology, Windhoek Namibia

Publisher

ACM

Reference24 articles.

1. Flow-Based Approach on Bro Intrusion Detection;Alaidaros H.;Journal of Telecommunication, Electronic and Computer Engineering (JTEC),2017

2. Albahar , M. A. ( 2019 ). Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments. 2019(D1) . Albahar, M. A. (2019). Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments. 2019(D1).

3. Fedynyshyn , G. , Chuah , M. C. , & Tan , G. ( 2014 ). Detection and Classification of Different Botnet C & C Channels Detection and Classification of Different Botnet C & C Channels. May. https://doi.org/10.1007/978-3-642-23496-5 10.1007/978-3-642-23496-5 Fedynyshyn, G., Chuah, M. C., & Tan, G. (2014). Detection and Classification of Different Botnet C & C Channels Detection and Classification of Different Botnet C & C Channels. May. https://doi.org/10.1007/978-3-642-23496-5

4. Preventing Model Overfitting and Underfitting in Convolutional Neural Networks

5. Dropout: A Simple Way to Prevent Neural Networks from Overfitting;Hinton N. S. G.;Journal of Machine Learning Research,2014

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Big Data Enabled Intrusion Detection with Honeypot Intelligence System on Apache Flink (BDE-IDHIS);2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06

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