Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Intrusion Detection Using Variable-Length Audit Trail Patterns
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
Learning nonstationary models of normal network traffic for detecting novel attacks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Intrusion detection using sequences of system calls
Journal of Computer Security
A sense of self for Unix processes
SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
Towards new areas of security engineering
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Improving the performance of neural networks with random forest in detecting network intrusions
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
| Hi-index | 0.00 |
The weak foundation of the computing environment caused information leakage and hacking to be uncontrollable. Therefore, dynamic control of security threats and real-time reaction to identical or similar types of accidents after intrusion are considered to be important. As one of the solutions to solve the problem, studies on intrusion detection systems are actively being conducted. To improve the anomaly intrusion detection system using system calls, this study focuses on techniques of neural networks and fuzzy membership function using the Soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the Soundex algorithm, this study conducted neural networks learning by using a back-propagation algorithm and fuzzy membership function. The proposed method and N-gram technique are applied for anomaly intrusion detection of system calls using Sendmail data of UNM to demonstrate its performance.