This paper deals with the theory and application of Cellular Automata (CAI for a class of block ciphers and stream ciphers. Based on CA state transitions certain fundamental transformations are defined which are block ciphering functions of the proposed enciphering scheme. These fundamental transformations are found to generate the simple (alternating) group of even permutations which in turn is a subgroup of the permutation group. These functions are implemented with a class of programmable cellular automata (PCA) built around rules 51, 153, and 195. Further, high quality pseudorandom pattern generators built around rule 90 and 150 programmable cellular automata with a rule selector (Le., combining function) has been proposed as running key generators in stream ciphers. Both the schemes provide better security against different types of attacks. With a simple, regular, modular and cascadable structure of CA, hardware implementation of such schemes idealy suit for VLSI implementation.
Abstract-In this paper we propose a clustering based method to capture outliers. We apply K-means clustering algorithm to divide the data set into clusters. The points which are lying near the centroid of the cluster are not probable candidate for outlier and we can prune out such points from each cluster. Next we calculate a distance based outlier score for remaining points. The computations needed to calculate the outlier score reduces considerably due to the pruning of some points. Based on the outlier score we declare the top n points with the highest score as outliers. The experimental results using real data set demonstrate that even though the number of computations is less, the proposed method performs better than the existing method.
Intrusion Detection Systems (IDSs) are used to find the security violations in computer networks. Usually IDSs produce a vast number of alarms that include a large percentage of false alarms. One of the main reason for such false alarm generation is that, in most cases IDSs are run with default set of signatures. In this paper, a scheme for network specific false alarm reduction in IDS is proposed. A threat profile of the network is created and IDS generated alarms are correlated using neural network. Experiments conducted in a test bed have successfully filtered out most of the false alarms for a range of attacks yet maintaining the Detection Rate.
scite is a Brooklyn-based startup that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.