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Local outlier factor and stronger one class classifier based hierarchical model for detection of attacks in network intrusion detection dataset

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Frontiers of Computer Science 2016年第4期10卷 755-766页

作者： Alampallam Ramaswamy VASUDEVAN subramanian selvakumar CDBR-SSE Lab Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli (NITT) Tiruchirappalli 620015 India

Identification of attacks by a network intrusion detection system （NIDS） is an important task. In signature or rule based detection, the previously encountered attacks are modded, and signatures/rules are extracted. These rules are used to detect such attacks in future, but in anomaly or outlier detection system, the normal network traffic is modeled. Any deviation from the normal model is deemed to be an outlier/attack. Data mining and machine learning techniques are widely used in offline NIDS. Unsupervised and supervised learning techniques differ the way NIDS dataset is treated. The characteristic features of unsupervised and supervised learning are finding patterns in data, detecting outliers, and determining a learned function for input features, generalizing the data instances respectively. The intuition is that if these two techniques are combined, better performance may be obtained. Hence, in this paper the advantages of unsupervised and supervised techniques are inherited in the proposed hierarchical model and devised into three stages to detect attacks in NIDS dataset. NIDS dataset is clustered using Dirichiet process （DP） clustering based on the underlying data distribution. Iteratively on each cluster, local denser areas are identified using local outlier factor （LOF） which in turn is discretized into four bins of separation based on LOF score. Further, in each bin the normal data instances are modeled using one class classifier （OCC）. A combination of Density Estimation method, Reconstruction method, and Boundary methods are used for OCC model. A product rule combination of the three methods takes into consideration the strengths of each method in building a stronger OCC model. Any deviation from this model is considered as an attack. Experiments are conducted on KDD CUP＇99 and SSENet-2011 datasets. The results show that the proposed model is able to identify attacks with higher detection rate and low false alarms.

关键词： hierarchical model DP clustering LOF Dis-cretizer one class classifier NIDS

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Wavelet based detection of ventricular arrhythmias with neural network classifier

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Journal of Biomedical Science and Engineering 2009年第6期2卷 439-444页

作者： Sankara subramanian Arumugam Gurusamy Gurusamy selvakumar Gopalasamy 不详

This paper presents an algorithm based on the wavelet decomposition, for feature extraction from the Electrocardiogram (ECG) signal and recognition of three types of Ventricular Arrhythmias using neural networks. A set of Discrete Wavelet Transform (DWT) coefficients, which contain the maximum information about the arrhythmias, is selected from the wavelet decomposition. These coefficients are fed to the feed forward neural network which classifies the arrhythmias. The algorithm is applied on the ECG registrations from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases. We applied Daubechies 4 wavelet in our algorithm. The wavelet decomposition enabled us to perform the task efficiently and produced reliable results.

关键词： Daubechies 4 Wavelet ECG Feed Forward Neural Network Ventricular Arrhythmias Wavelet De-composition

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