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This paper explores the immunological model and implements it in the domain of intrusion detection on computer networks. The main objective of the paper is to monitor, log the network traffic and apply detection algorithms for detecting intrusions within the network. The proposed model mimics the natural Immune System (IS) by considering both of its layers, innate immune system and adaptive immune system respectively. The current work proposes Statistical Modeling based Anomaly Detection (SMAD) as the first layer of Intrusion Detection System (IDS). It works as the Innate Immune System (IIS) interface and captures the initial traffic of a network to find out the first-hand vulnerability. The second layer, Adaptive Immune-based Anomaly Detection (AIAD) has been considered for determining the features of the suspicious network packets for detection of anomaly. It imitates the adaptive immune system by taking into consideration the activation of the T-cells and the B-cells. It captures relevant features from header and payload portions for effective detection of intrusion. Experiments have been conducted on both the real-time network traffic and the standard datasets KDD99 and UNSW-NB15 for intrusion detection. The SMAD model yields as high as 96.04% true positive rate and around 97% true positive rate using real-time traffic and standard data sets. Highly suspicious traffic detected in the SMAD model is further tested for vulnerability in the AIAD model. Results show significant true positive rate, closer to almost 99% of accurately detecting the file-based and user-based anomalies for both the real-time traffic and standard data sets.

Computer Aided Diagnosis (CAD) systems have improved diagnosis of abnormalities in mammogram images. The principal feature within the breast region is the breast contour. Extraction of the breast region and delineation of the breast contour allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. After performing an essential pre-processing step to suppress artifacts and accentuate the breast region, the exact breast region as the region of interest (ROI), has to be segmented. In this paper we present a fully automated segmentation and boundary detection method for mammographic images.In this research paper we have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA) for digital mammogram. This is followed by a novel approach for edge detection (EDA) and finally obtaining the breast boundary by using our proposed Breast Border Boundary Enhancement Algorithm. This composite method have been implemented and applied to mini-MIAS, one of the most well-known mammographic database consisting of 322 medio-lateral oblique (MLO) view obtained via a digitization procedure. To demonstrate the capability of our segmentation algorithm it was extensively tested on mammograms using ground truth images and quantitative metrics to evaluate its performance characteristics. The experimental results indicate that the breast boundary regions were extracted accurately characterize the corresponding ground truth images. The algorithm is fully autonomous, and is able to preserve, skin and nipple (if in profile), a task very few existing mammogram segmentation algorithms can claim.

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This paper presents a method of conversion of color image of histopathological slides to grayscale as a preprocessing step for image segmentation. The method utilizes characteristics of these images to reduce the amount of information lost during the conversion. It is a global mapping scheme and the same color does not map to two different grayscale values. Color information is added to the brightness information to reduce information loss and the weights for the added color are dependent on the distribution of the pixels in a particular image. The method was tested on a large set of histopathological slide images and convincing results were obtained some of which has been published in this paper.

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