Abstract-The rapid growth of network based activities makes computer security is a more crucial issue. Many security methods are developed and used, but they are unfit to detect novel intrusions. Therefore, we propose a hybrid intrusion detection framework based on data mining classification and clustering techniques. In the proposed hybrid framework, improves the detection rate by taking the advantages of misuse and anomaly detection. In case of misuse detection, intrusion patterns are built automatically from a training data by the use of the random forest classification method. Then comparing this pattern against network activities to detect intrusions. In case of anomaly detection, the network activities processed to several clusters using weighted k means technique to detect novel intrusions. The whole process is evaluated over KDD'99 dataset.
This paper proposes a new algorithm for encrypting secret data to be communicated over internet. This method combines the advantage of normal encryption and visual cryptography. The secret image is encrypted using a new encryption algorithm producing multiple encrypted images. Images are same as the cover images thus become easy to handle. Then meaningful images are embedded on each share using embedding algorithm. At the receiver side after de embedding these meaningful encrypted images are decoded to form the secret image back. This method can be used for multiple meaningful encrypted images with perfect reconstruction of secret image.
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