Abstract By design, Wi‐Fi networks inherently lack reliable positional knowledge of the origin of individual network packets, and instead use the media access control (MAC) address of a received packet to assign packet identity. Given that there are a variety of tools that allow one to construct arbitrary network packets and transmit over a network, a potential security risk arises in that it is difficult to discriminate legitimate packets from packets generated by an intruder solely on the basis of packet content. We describe a data clustering approach to discriminating network nodes based on signal strength data measured from multiple angles through a distributed sensor network, and demonstrate that relatively little network data are sufficient for high discrimination accuracy. Copyright © 2009 John Wiley & Sons, Ltd.

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD '99 intrusion data set show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.

Automated blood vessel segmentation is an important issue for assessing retinal abnormalities and diagnoses of many diseases. The segmentation of vessels is complicated by huge variations in local contrast, particularly in case of the minor vessels. In this paper, we propose a new method of texture based vessel segmentation to overcome this problem. We use Gaussian and L*a*b* perceptually uniform color spaces with original RGB for texture feature extraction on retinal images. A bank of Gabor energy filters are used to analyze the texture features from which a feature vector is constructed for each pixel. The fuzzy C-means (FCM) clustering algorithm is used to classify the feature vectors into vessel or non-vessel based on the texture properties. From the FCM clustering output we attain the final output segmented image after a post processing step. We compare our method with hand-labeled ground truth segmentation of five images and achieve 84.37% sensitivity and 99.61% specificity.

The Ground penetrating radar (GPR) and Infrared (IR) imaging have become two established sensors for detecting buried anti-personnel mines (APM) which contain no or a little metal. The paper introduces the GPR and IR techniques briefly and compares the two sensors with respect to their strengths and weaknesses for target detection and emphasizes the necessity of fusion to harness the advantages of each of the methods. We propose a geometrical feature based sensor fusion framework, combining GPR and IR, as an effective technique for detection and classification of APM, which will reduce the false alarm rate significantly. We consider the basic geometrical shape descriptor features of an object and construct a feature vector for each of the objects. These feature vectors are used to train a Probabilistic Neural Network (PNN) for the classification of APMs. The method gives almost perfect detection accuracy.

This paper presents a hidden Markov model (HMM) based fuzzy rule extraction technique for predicting a time series generated by a chaotic dynamical system. The model uses three sequential phases. Firstly, the HMM is used to partition the input dataset based on the ordering of the calculated log-likelihood values (similarity measures). Then, a recursive top-down algorithm is used to generate the minimum number of rules required to accurately predict the next value in the time series using the training dataset. Finally, a gradient descent method is applied to the extracted fuzzy rules in order to fine-tune the model parameters. The performance of the proposed model is evaluated using a benchmark dataset -the Mackey-Glass time series. The results obtained clearly demonstrate significant improvement in prediction capabilities of the proposed HMM-fuzzy model when compared to the other techniques.

This paper presents hidden Markov models (HMM) approach for forecasting stock price for interrelated markets. We apply HMM to forecast some of the airlines stock. HMMs have been extensively used for pattern recognition and classification problems because of its proven suitability for modelling dynamic systems. However, using HMM for predicting future events is not straightforward. Here we use only one HMM that is trained on the past dataset of the chosen airlines. The trained HMM is used to search for the variable of interest behavioural data pattern from the past dataset. By interpolating the neighbouring values of these datasets forecasts are prepared. The results obtained using HMM are encouraging and HMM offers a new paradigm for stock market forecasting, an area that has been of much research interest lately.