Developing object-oriented database for web applications may not remain constant and may vary to a large extent due to a variety of reasons like correcting mistakes, adding new features or any changes in the structure of the real world artifacts modeled in the database. Class versioning is one of the evolution strategy employed t h a t addressing the above issues. The existing strategy for instance adaptation introduces the adaptation code directly into the class versions upon evolution. Consequently, if the behavior of a routine needs to be changed and maintenance has to be performed on all the class versions it was introduced. A new approach for instance adaptation is achieved by encapsulating the instance adaptation code through aspects -abstractions introduced by aspect-oriented programming that localize cross-cutting concerns. A web-based student database system was developed with different versions and the versioning problem was solved using update/backdate aspects with selective lazy conversion. The update/backdate aspects are invoked whenever version incompatibility arises and selective lazy conversion aspect is invoked when the condition for converting objects into new version is satisfied and this converts only a subset of the old version objects into new version.
We propose an anomaly-based network intrusion detection system, which analyzes traffic features to detect anomalies. The proposed system can be used both in online as well as off-line mode for detecting deviations from the expected behavior. Although our approach uses network packet or flow data, it is general enough to be adaptable for use with any other network variable, which may be used as a signal for anomaly detection. It differs from most existing approaches in its use of wavelet transform for generating different time scales for a signal and using these scales as an input to a two-stage neural network predictor. The predictor predicts the expected signal value and labels considerable deviations from this value as anomalies. The primary contribution of our work would be to empirically evaluate the effectiveness of multi resolution analysis as an input to neural network prediction engine specifically for the purpose of intrusion detection. The role of Intrusion Detection Systems (IDSs), as special-purpose devices to detect anomalies and attacks in a network, is becoming more important. First, anomaly-based methods cannot achieve an outstanding performance without a comprehensive labeled and up-to-date training set with all different attack types, which is very costly and time-consuming to create if not impossible. Second, efficient and effective fusion of several detection technologies becomes a big challenge for building an operational hybrid intrusion detection system.
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