TY  - JOUR
ID  - scholars1207
SP  - 597
Y1  - 2010///
A1  - Ahmad, I.
A1  - Abdulah, A.B.
A1  - Alghamdi, A.S.
N2  - The duty of securing networks is very difficult due to their size, complexity, diversity and dynamic situation. Currently applying neural networks in intrusion detection is a robust approach to ensure security in the network system. Further, neural networks are alternatives to other approaches in the area of intrusion detection. The main objective of this research is to present an adaptive, flexible and optimize neural network architecture for intrusion detection system that provides the potential to identify network activity in a robust way. The results of this work give directions to enhance security applications such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), check points and firewalls and further guide to the security implementers. © 2010 Springer-Verlag.
JF  - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
VL  - 6059 L
SN  - 03029743
TI  - Towards the designing of a robust intrusion detection system through an optimized advancement of neural networks
N1  - cited By 8; Conference of 2nd International Conference on Advanced Science and Technology ; Conference Date: 23 June 2010 Through 25 June 2010; Conference Code:81036
EP  - 602
KW  - Adaptive resonance theory; Adaptive Resonance Theory (ART); Batch Backpropagation (BPROP); Network intrusion detection; Resilient backpropagation; Supervised neural networks; Unsupervised neural networks
KW  -  Arts computing; Backpropagation; Computer viruses; Conformal mapping; Neural networks; Optimization; Resonance
KW  -  Intrusion detection
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954579798&doi=10.1007%2f978-3-642-13577-4_53&partnerID=40&md5=0b53a3ee98bc43e9e331d8db452d6626
AV  - none
CY  - Miyazaki
ER  -