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The Internet of Things is one of the most adapting technology with the applications in multiple domains. Bringing together, huge number of devices and networks to access the information at one platform is called an IoT network. IoT is shifting its security priorities to consider data access control from a data-centric perspective. Recently, Muhammad et.al. has proposed a scheme of two-factor user authentication in WSN and claimed that his scheme is secured against various attacks [1]. However, we have found some pitfalls in his work and based on that, proposed a work, which is an improvement of the earlier works done by M.L. Das on access control policy. In our work, we have modified the existing algorithm by adding new computational load without disturbing the work done in the previous algorithm. The integration of our proposed work with the earlier work makes the whole process more protective from various insider attack. The proposed algorithm "Disjunctive XOR Two Factor Authentication Method " can be implemented with real life applications. The security patches implemented in the proposed algorithm, attempts to fix receptiveness of the gateway and makes the scheme more secured and adaptive. The proposed work is tested in an unsecured wireless sensor network and found to be capable of protecting from various attacks such as Bypass and Insider attacks. With addition, it also gives the flexibility, to allow user to change or update its login password within a secured environment. In our work, we have also proposed, the Mutual Authentication between the Gateway(GY-node) and Sensor Node(Sn) such that it gets protected by an inside attacker and secondly, to allow user to change/update password independently and thirdly proved that the work proposed is secured from insider and Bypass attacker.
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As known that the Co-training algorithm in semi-supervised learning is a classical algorithm in semi-supervised learning algorithm, but it needs sufficient redundant view of data set in the process of learning, but this requirement usually can not be met in actual application. There are some improved algorithms, one of which is Tri-training algorithm and it uses three classifiers. It is an important algorithm used in Semi-supervised Learning. Although it no longer requires the attribute set of data, its time complexity is high. In the paper, it improves the Co-training algorithm thorugh using two classifiers. The difference is that one classifier only trains its own labeled data, one classifier trains its own labeled data and some unmarked data with good confidence, and one classifier is supervised learning and the other is semi-supervised learning. Then, the two classifiers are used to predict all unlabeled data, and the unlabeled data with high confidence and prediction tags are added to the training set of classifier 2. Experiments show that the accuracy and running time of the proposed algorithm in image classification can be significantly improved.
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