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Sharing outsourced data between owners and data mining experts is becoming a challenging issue in biomedical and healthcare fields. Watermarking has been proved as a right-protection mechanism that can provide detectable evidence for the legal ownership of a shared dataset, without compromising its usability. However, the main disadvantage of these conventional techniques is unintelligent, rule-based...
Biometric-based cryptographic key generation is regarded as a data mining approach that uses knowledge discovery techniques to extract biometric information to generate cryptographic keys for protecting secured data by encryption. This application has been widely used in security systems to limit the weakness of passwords. Although conventional biometrics such as fingerprint, face, voice, and handwriting...
Nowadays, detecting and filtering are still the most feasible ways of fighting spam emails . There are many reasonably successful spam email filters in operation. However, proactively catching new strains of spam emails, where no previous knowledge is available, is still a major challenge. Negative selection is a branch of artificial immune systems. It has a strong temporal nature and is especially...
This paper presents a multi-agent security architecture, which utilizes the agent characteristics to cater for security processes in online communications. The multilayer communication approach (MLC) is used to determine the security processes, which uses cryptography protocols to secure data and communication channel. Agents are skilled to perform certain tasks. At the sender's host, agents interact...
This paper presents a novel fuzzy subspace-based approach to hidden Markov model. Features extracted from patterns are considered as feature vectors in a multi-dimensional feature space. Current hidden Markov modeling techniques treat features equally, however this assumption may not be true. We propose to consider subspaces in the feature space and assign a weight to each feature to determine the...
A common problem for network intrusion detection systems is that there are many available features describing network traffic and feature values are highly irregular with burst nature. Some values such as octets transferred range several orders of magnitudes, from several bytes to million bytes. The role of network features depends on which pattern to be detected: normal or intrusive one. Intrusion...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
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