An Approach for Host-Based Intrusion Detection System Design Using Convolutional Neural Network

Author:

Tran Nam Nhat,Sarker Ruhul,Hu Jiankun

Publisher

Springer International Publishing

Reference39 articles.

1. A Guide to TF Layers: Building a Convolutional Neural Network. https://www.tensorflow.org/tutorials/layers . Accessed 08 Mar 2017

2. A path to unsupervised learning through adversarial networks. https://code.facebook.com/posts/1587249151575490/a-path-to-unsupervised-learning-through-adversarial-networks/ . Accessed 03 Mar 2017

3. Ahmed, M., Mahmood, A.N., Hu, J.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19–31 (2016)

4. Ashfaq, R.A.R., et al.: Fuzziness based semi-supervised learning approach for intrusion detection system. Inf. Sci. 378, 484–497 (2017)

5. Canzanese, R., Mancoridis, S., Kam, M.: System call-based detection of malicious processes. In: 2015 IEEE International Conference on Software Quality, Reliability and Security (QRS), pp. 119–124. IEEE (2015)

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