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Brain tumour classification allows for successful treatment. The aim of this paper is to identify two categories of brain tumors using a benchmark dataset of Magnetic resonance image (MRI) brain tumor. Due to a lack of huge training data, this paper using data augmentation so that training data is increased. In this aspect, the original images are processed with median filtering and the obtained results are used as total training dataset In this study, we propose a novel method for classifying pictures of brain tumors and cancer using pre-trained convolution neural networks (CNN) and support vector machines (SVM). In this appraoch, the features are derived using two pre-trained convolution neural networks models namely Alexnet and ResNet. Support vector machine (SVM) is used to classify tumor picture after characteristics have been extracted. This paper is addressing issues like: to reduce training time, improve image classification accuracy and prevent over fitting. Instead of training with same dataset, using of segmented images as training data will achieves good performance. We trained our architectures with segmented images and limited pre-processing for three epochs to test classification accuracy and time consumption. The experimental performance attained is 97.34% with transfer learning.

Nowadays security is major concern for any user connected to the internet. Various types of attacks are to be performed by intruders to obtaining user information as man- inmiddle attack, denial of service, malware attacks etc. Malware attacks specifically ransomware attack become very famous recently. Ransomware attack threaten the users by encrypting their most valuable data, lock the user screen, play some random videos and by various more means. Finally attacker takes benefits by users through paid ransom. In this paper, we propose a framework which prevents the ransomware attack more appropriately using various techniques as block chain, honeypot, cloud & edge computing. This framework is analyzed mainly through the IoT devices and generalized to the any malware attack.

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