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Summary: Mobile Ad hoc Networks (MANETs) are temporary in nature, with security concerns being more vulnerable than the other types of networks. Most of the works concentrate on either authentication or intrusion detection. This framework considers both intrusion detection and authentication continuously. It has been done for different layers of security for finding the intruder and eliminate from the network. Iris images have been used in this work for the purpose of authentication. Detection and response engines have been used for intrusion detection and high security in ad hoc network. The simulation results clearly show the iris‐based continuous authentication detecting most of the intruders and eliminates from the network. [ABSTRACT FROM AUTHOR]

ABSTRACT This article proposes a novel and efficient methodology for the detection of Glioblastoma tumor in brain MRI images. The proposed method consists of the following stages as preprocessing, Non-subsampled Contourlet transform (NSCT), feature extraction and Adaptive neuro fuzzy inference system classification. Euclidean direction algorithm is used to remove the impulse noise from the brain image during image acquisition process. NSCT decomposes the denoised brain image into approximation bands and high frequency bands. The features mean, standard deviation and energy are computed for the extracted coefficients and given to the input of the classifier. The classifier classifies the brain MRI image into normal or Glioblastoma tumor image based on the feature set. The proposed system achieves 99.8% sensitivity, 99.7% specificity, and 99.8% accuracy with respect to the ground truth images available in the dataset. [ABSTRACT FROM AUTHOR]