Application of artificial neural network in detection of probing attacks

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.

[1]  Adeel Akram,et al.  A comparative analysis of artificial neural network technologies in intrusion detection systems , 2006 .

[2]  Electronics Letters , 1965, Nature.

[3]  Gürsel Serpen,et al.  Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context , 2003, MLMTA.

[4]  Yang Wei,et al.  Anomaly Intrusion Detection Approach Using Hybrid MLP/CNN Neural Network , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[5]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[6]  John McHugh,et al.  Testing Intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory , 2000, TSEC.

[7]  Azween Abdullah,et al.  Artificial neural network approaches to intrusion detection: a review , 2009, IEEE ICT 2009.

[8]  James Cannady,et al.  Artificial Neural Networks for Misuse Detection , 1998 .

[9]  Morteza Amini,et al.  RT-UNNID: A practical solution to real-time network-based intrusion detection using unsupervised neural networks , 2006, Comput. Secur..

[10]  M. E. Beall.  U.S. patent and trademark office , 1997 .

[11]  Sergio M. Savaresi,et al.  Unsupervised learning techniques for an intrusion detection system , 2004, SAC '04.

[12]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[13]  Iftikhar Ahmad,et al.  Performance comparison between backpropagation algorithms applied to intrusion detection in computer network systems , 2008 .

[14]  Yen-Chung Huang,et al.  1.5 V large-driving class-AB buffer amplifier with quiescent current control , 2004 .

[15]  Julie Greensmith,et al.  Immune system approaches to intrusion detection – a review , 2004, Natural Computing.

[16]  Y. Tanabe,et al.  Local Positioning with Artificial Neural Network and Time of Arrival Technique , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

[17]  W.L.A. Neves,et al.  Comparison between backpropagation and RPROP algorithms applied to fault classification in transmission lines , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[18]  Erland Jonsson,et al.  Recent Advances in Intrusion Detection: 7th International Symposium, RAID 2004 , 2004 .

[19]  C. Douligeris,et al.  Detecting denial of service attacks using emergent self-organizing maps , 2005, Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005..

[20]  Elizabeth B. Lennon.  Testing Intrusion Detection Systems , 2003 .