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Network Intrusion Detection has been an active area of research with the growing incidences of cybercrimes. This has led to continuous monitoring of network traffic, analysis, and raise alarm if any abnormality is noticed so as to trigger appropriate response in order to curb the possibility of an attack. One of the approaches to deal with the network intrusion problem is to classify the network user behavior as normal or suspicious. Soft computing based techniques are being tried out to classify network users with higher degree of accuracy and low false alarm rate. In this paper, we propose a classification model for the detection of known as well as unknown network attacks based on artificial neural network based techniques namely, RBFN, SOM, LVQ3, SMO, and CNN. Further, in order to improve the performance of the classifier, Z-Score normalization has been applied for preprocessing of data. The performance of the model has been evaluated on the NSL-KDD dataset in terms of Precision, Accuracy, Detection rate, F-Value, and False Alarm rate. Keywords: Convolution Neural Network, Learning Vector Quantization, Normalization, Self-Organizing Map, Sequential Minimal Optimization, and Radial Basis Function Network.

Intrusion detection systems are meant to provide secured network computing environment by protecting against attackers. The challenge in building an intrusion detection model is to deal with unbalanced intrusion datasets, i.e., when one class is represented by a small number of examples (minority class). Most of the time it is observed that the performance of the classification techniques somehow becomes biased toward the majority class due to unequal class distribution. In this work, a layered approach has been proposed to detect network intrusions with the help of certain rule learning classifiers. Each layer is designed to detect an attack type by employing certain nature-inspired search techniques such as ant search, genetic search, and PSO. The performance of the model has been evaluated in terms of accuracy, efficiency, detection rate, and false alarm rate.

Advancements in Information and Communication Technology have led to widespread use of Internet and deployment of services that can be electronically accessed over a computer network. However, the security of computer networks has become a critical issue due to infrastructural vulnerabilities. One of the approaches to address this issue is to build efficient intrusion detection models which can detect and raise appropriate alerts in the event of any possible attacks. In this work, we try to develop intrusion detection models based on fuzzy-rough classifiers. Our main objective is to improve the intrusion detection accuracy and reduce false alarms as far as possible through hybridization of fuzzy-rough set based classification techniques such as Fuzzy NN, Fuzzy Rough NN, Fuzzy Ownership NN, Vaguely Quantified Nearest Neighbors and Ordered Weighted Average Nearest Neighbors for classifying intrusion data. The standard NSL-KDD data set has been used to experiment and evaluate the performance of the proposed hybrid models along different evaluation criteria such as accuracy, detection rate, precision, and false alarm rate.

Intrusion Detection Systems (IDS) are intended to protect computer networks from malicious users. Several data mining techniques have been used to build intrusion detection models for analyzing anomalous behavior of network users. However, the performance of such techniques largely depends on their ability to analyze intrusion data and raise alarm whenever suspicious activities are observed. In this paper, some rule based classification techniques, viz., Decision Table, DTNB, NNGE, JRip, and RIDOR have been applied to build intrusion detection models. Further, in order to improve the performance of the classifiers, six rank based feature selection methods, viz., Chi squared attribute evaluator, One-R, Relief-F, information Gain, Gain Ratio, and Symmetrical Uncertainty have been employed to select the most relevant features. Performance of different combinations of classifiers and feature selection techniques have been studied using a set of performance criteria, viz., accuracy, precision, detection rate, false alarm rate, and efficiency.