Novel Tsetlin Machine Mechanisms for Logic-based Regression and Classification with Support for Continuous Input, Clause Weighting, Confidence Assessment, Deterministic Learning, and Convolution

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A Markov-Based Intrusion Tolerance Finite Automaton

It is inevitable for networks to be invaded during operation. The intrusion tolerance technology comes into being to enable invaded networks to provide the necessary network services. This paper introduces an automatic learning mechanism of the intrusion tolerance system to update network security strategy, and derives an intrusion tolerance finite automaton model from an existing intrusion tolerance model. The proposed model was quantified by the Markov theory to compute the stable probability of each state. The calculated stable probabilities provide the theoretical guidance and basis for administrators to better safeguard network security. Verification results show that it is feasible, effective, and convenient to integrate the Markov model to the intrusion tolerance finite automaton

A New Approach of Detecting Network Anomalies using Improved ID3 with Horizontal Partioning Based Decision Tree

In this paper we are proposing a new approach of Detecting Network Anomalies using improved ID3 with horizontal portioning based decision tree. Here we first apply different clustering algorithms and after that we apply horizontal partioning decision tree and then check the network anomalies from the decision tree. Here in this paper we find the comparative analysis of different clustering algorithms and existing id3 based decision tree