There is an increasing emphasis on enhancing the efficiency traffic management systems. Information is exchanged between the vehicular nodes to efficiently monitor and control huge volumes of vehicle. All existing applications in this area have focused on reliable data exchange and authentication process of vehicular nodes to forward messages. This study proposes a new entity centric trust framework using decision tree classification and artificial neural networks. Decision tree classification is used to derive rules for trust calculation and artificial neural networks are used to self-train the vehicular nodes, when expected value is not met. This model uses multifaceted role and distance based metrics like Euclidean distance to estimate the trust. The proposed entity centric trust model, uses a versatile new direct and recommended trust evaluation strategy to compute trust values. The suggested model is simple, reliable and efficient in comparison to the other popular entity centric trust models.
Networks have an important role in our modern life. In the network, Cyber security plays a crucial role in Internet security. An Intrusion Detection System (IDS) acts as a cyber security system which monitors and detects any security threats for software and hardware running on the network. There we have many existing IDS but still we face challenges in improving accuracy in detecting security vulnerabilities, not enough methods to reduce the level of alertness and detecting intrusion attacks. Many researchers have tried to solve the above problems by focusing on developing IDSs by machine learning methods. Machine learning methods can detect datas from past experience and differentiate normal and abnormal data. In our work, the Convolutional Neural Network(CNN) deep learning method was developed in solving the problem of identifying intrusion in a network. Using the UNSW NB15 public dataset we trained the CNN algorithm. The Dataset contains binary types of ‘0’ and ‘1’ in general for normal and attack datas. The experimental results showed that the proposed model achieves maximum accuracy in detection and we also performed evaluation metrics to analyze the performance of the CNN algorithm.
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