Name
Abstract | ||
|---|---|---|
With the evolution of network threat, identifying attack from both external and internal is getting more and more difficult. To detect both known and unknown malicious attacks, several machine learning algorithms are utilized. However, these algorithms have still some limitations such as high false positive and false alarm rate. To overcome above challenge, we propose a threat analysis method combined with kernel principal component analysis (PCA) and long short-term memory recurrent neural network (LSTM-RNN). To achieve high accurate detection rate, data preprocessing, feature extraction, attack detection is seamlessly integrated into an end-to-end detection method. To assess the method, the well-known NSL-KDD dataset has been used. Experimental results show that the proposed threat analysis method greatly outperforms several attack detection methods that use SVM, neural network or Bayesian methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICACI.2018.8377511 | 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI) |
Keywords | Field | DocType |
network security,threat analysis,recurrent neural network,kernal PCA | Pattern recognition,Computer science,Support vector machine,Data pre-processing,Recurrent neural network,Kernel principal component analysis,Feature extraction,Artificial intelligence,Constant false alarm rate,Artificial neural network,Principal component analysis | Conference |
ISBN | Citations | PageRank |
978-1-5386-4363-1 | 1 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fanzhi Meng | 1 | 15 | 2.02 |
| Yunsheng Fu | 2 | 27 | 2.99 |
| Fang Lou | 3 | 17 | 3.07 |