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The Jamming-style Denial of Service (J-DoS) attacks are significant causes of malfunctioning of Wireless Sensor Networks (WSNs). The nodes of WSNs are prone to external disturbances especially when they are used in hostile environments. The attackers mainly operate in the wireless communication medium by following a couple of diverse scenarios. In this paper, we have developed two detection mechanisms used for J-DoS attacks in order to differentiate the legitimate and adversary scenarios. The detection mechanisms designed utilize some network parameters and additional packets to separate and classify normal conditions and adversary ones. Having detected the type of the attacker, appropriate counter measures can be applied.

Aim:The aim was to develop a high-performance computer-aided diagnosis (CAD) system with image processing and pattern recognition in diagnosing pancreatic cancer by using endosonography images.Materials and Methods:On the images, regions of interest (ROI) of three groups of patients (<40, 40-60 and >60) were extracted by experts; features were obtained from images using three different techniques and were trained separately for each age group with an Artificial Neural Network (ANN) to diagnose cancer. The study was conducted on endosonography images of 202 patients with pancreatic cancer and 130 noncancer patients.Results:122 features were identified from the 332 endosonography images obtained in the study, and the 20 most appropriate features were selected by using the relief method. Images classified under three age groups (in years; <40, 40-60 and >60) were tested via 200 random tests and the following ratios were obtained in the classification: accuracy: 92%, 88.5%, and 91.7%, respectively; sensitivity: 87.5%, 85.7%, and 93.3%, respectively; and specificity: 94.1%, 91.7%, and 88.9%, respectively. When all the age groups were assessed together, the following values were obtained: accuracy: 87.5%, sensitivity: 83.3%, and specificity: 93.3%.Conclusions:It was observed that the CAD system developed in the study performed better in diagnosing pancreatic cancer images based on classification by patient age compared to diagnosis without classification. Therefore, it is imperative to take patient age into consideration to ensure higher performance.

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Detailed experiments showed that CADBOSS outperforms state-of-the-art computer-aided diagnosis. (CAD) systems and reasonably improves physician' diagnostic success.

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Several techniques of data embedding and data hiding have been proposed and developed especially during the last two decades due to continually increasing needs of secure communication. Still image, audio and video files are the most promising digital mediums for steganography applications. However, video files have a vast potential for embedding secret data compared to other alternatives in terms of storage size. Selecting the most appropriate pixels is of great importance in the procedure of embedding secret data into video files. Unsuccessful pixel selection can trigger some negative spatial and/or temporal awareness, which eventually causes an ineffective data embedding process. In this paper, we have proposed and developed an effective blind steganography method, which uses an appropriate pixel selection mechanism, based on histogram techniques. The method we have proposed proves its success by means of perceptibility of the secret data in both spatial and temporal domains.