# Performance Comparison between Backpropagation Algorithms Applied to Intrusion Detection in Computer Network Systems

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2 2. Neural Networks The first artificial neuron was produced in 943 by the neurophysiologist Warren McCulloch and the logician Walter Pits [9]. An artificial neuron is a processing element with many inputs and one output. An artificial neural network consists of a group of processing elements that are greatly interconnected and convert a set of inputs to a set of preferred outputs. The result of the transformation is determined by the characteristics of the elements and the weights associated with the interconnections among them. By modifying the connections between the nodes the network is able to adapt to the desired outputs [0]. The application of neural network in intrusion detection is an on going area. It offers the potential to resolve a number of the problems encountered by the other current approaches to intrusion detection. Artificial neural networks are alternatives. The first advantage in the use of a neural network in the intrusion detection would be the flexibility that the network would provide.. A neural network would be capable of analyzing the data from the network, even if the data is incomplete or unclear. Similarly, the network would possess the ability to conduct an analysis with data in a non-linear fashion. Further, because some attacks may be conducted against the network in a coordinated attack by multiple attackers, the ability to process data from a number of sources in a non-linear fashion is especially important. The natural speed of neural networks is another advantage of this approach. However, the most significant advantage of neural networks in intrusion detection is the ability of the neural network to "learn" the characteristics of attacks and identify instances that have been observed before by the network [8, ]. 3.0 Architecture The design of our system consists of one input, two hidden and one output layer. The Input layer takes input from the input file that contains data for training of the net. The hidden layers take inputs from the outputs of the input layer and apply its activation function. After this the output is sent to the output layer. The output layer allows a neural network to write output patterns in a file that are used for analysis of intrusion. The general architecture of our system is shown. 4 Input Layer 4 Hidden Layers Fig.: General Architecture The design of our mechanism consists of one input, two hidden and one output layer with 4,4,9 and 2 neurons. The input layer consists of 4 neurons because the Kddcup data set contains 4 fields for a network packet to be used for intrusion detection training [, 0, 2, 3, 4]. There is no exact formula for the selection of hidden layers so we can make it by comparison and select which one is best [5]. For choosing best set of hidden layers and its no. of neuron a comparison is made for many cases and best one is selected that is two hidden layers with 4 and 9 neurons as shown in the table below. Sr.# Hidden Layers Neurons RMSE H H2 H H H2 H3 H H H2 H H H2 H H H2 H H H H Table : A comparison for choosing best set of hidden layers and its no. of neuron The architecture used in our system is feed forward. A feed forward neural net is composed of a number of consecutive layers/components, each one 9 2 Output Layer ISBN: ISSN:

4 Sigmoid transfer function is used the gradient can have a very small magnitude, causing small changes in the weights and biases, even though the weights and biases are far from their optimal values. This modified algorithm is a batch training algorithm, and uses only the batch size property. The value of the learning rate and the momentum properties doesn't affect the calculus of the Rprop algorithm. 5.0 Data set The data set chosen for the experiment is the KDD Cup 99 data set. This data set is available at [, 2]. The DARPA data set is the most widely used data set for testing IDS [6, 7, 8, 9, 2, 22]. This DARPA project was prepared and executed by the Massachusetts Institute of Technology (MIT) Lincoln Laboratory. One of the reasons for choosing this data set is that the data set is standard. This will make it easy to compare the results of our work with other similar works. Another reason is that it is difficult to get another data set which contains so rich a variety of attacks as the one used here. Usage of the data set This section describes how the data set is used for our experiment. The 80% from the samples are used for training while others are used for testing the net. The data set is preprocessed so that it may be able to give it as an input to our developed system. This data set consists of numeric and symbolic features and we converted it in numeric form so that it can be given as inputs to our neural network. We replaced symbolic with specific numeric, comma with semicolon, normal with 0, and attack with, 0. Now this modified data set is ready to be used as training and testing of the neural network. 6.0 Implementation The design and the implementation are modular. The programming language used for the implementations of the algorithms and the experiment is Java and its JOONE API [3, 4]. As an example of object oriented programming language, Java supports modularity. Furthermore Java is an efficient and flexible programming language. The efficiency is an important issue in our research because of the large size of the data set. The architecture of the system is composed of two modules: the training module, and the evaluation module. The interface of the application is shown below. Fig.2: An Interface of IDS The training module This involves the training of the neural networks. It is necessary for accurate detection of intrusions in the computer network systems. The accuracy of the system depends on the correctness of data and its training algorithms. We trained the system on preprocessed data using online, batch and resilient backpropagation. A sample of training pattern of POD attack and its normal is shown below. 0;8;2;20;480;0;0;;0;0;0;0;0;0;0;0;0;0 ;0;0;0;0;;;0.00;0.00;0.00;0.00;.00;0.00 Attack ;0.00;;;.0;0.00;.00; 0.00; 0.00; 0.00; 0.00; 0.00; ; 0 0;8;2;20;480;0;0;;0;0;0;0;0;0;0;0;0;0 ;0;0;0;0;2;4;0.00;0.00;0.00;0.00;.00;0.00 Normal ;0.50;2;4;.00;0.00;.00;0.50;0.00;0.00;0. 00;0.00;0; Table 2: A POD attack and its normal pattern The evaluation module This involves the testing of the trained net. After training, the net only involves computation of the feed forward phase. The output of the net is saved in file that will be used for intrusion analysis. If the global error value is nearest to 0 and then it is normal packet otherwise consider as attack. 7.0 Results First of all we make a performance comparison between above described algorithms. We tested the above implemented algorithms on preprocessed data of the kddcup data set for different epochs on same parameters (alpha=0., m=0.9). The computed results by our application are shown below. ISBN: ISSN:

5 Iteration Epochs Online Batch Resilient Global Error Table 3: First five iterations Performance Comparison Iterations Fig. 3: Performance of algorithms Series Series2 Series3 Iteration Epochs Online Batch Resilient E E E E E E E E E E-04 Global Error Table 4: Next five iterations Performance Comparison Iterations Fig. 4: Performance of algorithms Series Series2 Series3 Initially online and batch backpropagation algorithms converge more quickly but as the time passes the RPROP algorithm shows its performance best. It uses the sign of the backpropagated gradient to change the weights of the network, instead of the magnitude of the gradient itself. This is because the gradients have a very small magnitude causing small changes in the weights even though the weights are far from their optimal values. Teaching neural network using RPROP requires fewer steps as compared to other algorithms. Moreover it requires similar computational effort. Another feature of RPROP is its robustness which is explained by the ability of escaping flat areas of the error surface much faster than the other methods. This characteristics make the less dependent on the initialization of the weights and also more capable of avoiding local minima. Now for more satisfaction we perform another test of eight iterations on different epochs as shown below. Iteration Epochs Online Batch RPROP M S E Table 5: Iterations at different epochs Mean-squared error for training Epochs Fig. 5: Mean-squared error Series Series2 Series3 The comparative analysis of algorithms attests the performance and effectiveness of RPROP. By ISBN: ISSN:

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