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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.