NDLI: Application of artificial neural network in detection of probing attacks

Content Provider	IEEE Xplore Digital Library
Author	Ahmad, I. Abdullah, A.B. Alghamdi, A.S.
Copyright Year	2009
Description	Author affiliation: DCIS, UTP, Bandar Seri Iskandar, 31750, Tronoh, Perak, Malaysia (Ahmad, I.) \|\| Department of Software Engineering, College of Computer & Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Kingdom of Saudi Arabia (Alghamdi, A.S.) \|\| Department of Computer & Information Sciences, Universiti Teknologi, PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia (Abdullah, A.B.)
Abstract	The prevention of any type of cyber attack is indispensable because a single attack may break the security of computer and network systems. The hindrance of such attacks is entirely dependent on their detection. The detection is a major part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), check points and firewalls. Consequently, in this paper, we are contemplating the feasibility of an approach to probing attacks that are the basis of others attacks in computer network systems. Our approach adopts a supervised neural network phenomenon that is majorly used for detecting security attacks. The proposed system takes into account Multiple Layered Perceptron (MLP) architecture and resilient backpropagation for its training and testing. The system uses sampled data from Kddcup99 dataset, an attack database that is a standard for evaluating the security detection mechanisms. The developed system is applied to different probing attacks. Furthermore, its performance is compared to other neural networks' approaches and the results indicate that our approach is more precise and accurate in case of false positive, false negative and detection rate.
Starting Page	557
Ending Page	562
File Size	373752
Page Count	6
File Format	PDF
ISBN	9781424446810
DOI	10.1109/ISIEA.2009.5356382
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-04
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Backpropagation Adaptive systems Multiple Layered Perceptron Data security Dataset Artificial neural networks Resilient Backpropagation Remote to User Application software Detection Rate Learning Neural Network False Negative User to root Neural networks Intrusion detection Computer architecture Computer networks Probing attack False Positive Computer security
Content Type	Text
Resource Type	Article

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