Abstract: A method of predicting a SYN flooding attack on a server. The method tracks the number of SYN signals received (or SYN+ACK signals sent) over the communications port of the server in a specified time interval, the arrival estimation window. The invention then predicts the number of anticipated ACK, RST or ACK+RST signals to be received over the communication port within a predetermined time length prediction window. The prediction may be made at multiple points within the prediction window. The prediction window is offset in time from the arrival estimation window. The prediction of ACK signals to be received is based upon the number of SYN signals received or SYN+ACK signals sent in the arrival estimation window. In one embodiment, a polynomial is fit to the data in the Arrival estimation window and extrapolated to the prediction window.

Abstract: The invention is a method of predicting a SYN flooding attack on a server. The method tracks the number of SYN signals received (or SYN+ACK signals sent) over the communications port of the server in a specified time interval, the arrival estimation window. The invention then predicts the number of anticipated ACK signals to be received over the communication port within a predetermined time length prediction window. The prediction may be made at multiple points within the prediction window. The prediction window is offset in time from the arrival estimation window. The prediction of ACK signals to be received is based upon the number of SYN signals received or SYN+ACK signals sent in the arrival estimation window. In one embodiment, a polynomial is fit to the data in the Arrival estimation window and extrapolated to the prediction window.

Abstract: The invention is a computer implemented technique for id entifying anomalous data in a data set. The method uses cascaded k-Means clustering and the ID3 decision tree learning methods to characterize a training data set having data points with known characterization. The k-Means clustering method first partitions the training instances into k clusters using Euclidean distance similarity. On each training cluster, representing a density region of normal or anomaly instances, the invention builds an ID3 decision tree. The decision tree on each cluster refines the decision boundaries by learning the sub-groups within the cluster. A test data point is then subjected to the clustering and decision trees constructed form the training instances. To obtain a final decision on classification, the decisions of the k-Means and ID3 methods are combined using rules: (1) the Nearest-neighbor rule, and (2) the Nearest-consensus rule.