A global survey conducted by arXiv in 2016 showed that 58% of arXiv users thought arXiv should have a peer review system. The current opinion is that arXiv should adopt the Community Peer Review model. This paper evaluates and identifies two weak points of Community Peer Review and proposes a new peer review model – Self‐Organizing Peer Review. We propose a model in which automated methods of matching reviewers to articles and ranking both users and articles can be implemented. In addition, we suggest a strategic plan to increase recognition of articles in preprint databases within academic circles so that second generation preprint databases can achieve faster and cheaper publication.

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This thesis proposes a Distributed Intrusion Detection System for Smart Grids by developing and deploying intelligent modules in multiple layers of the smart grid in order to handle cyber security threats. Multiple Analyzing Modules are embedded at different levels of the smart grid-the Home Area Network, Neighborhood Area Network, and Wide Area Network. These intelligent modules employ Support Vector Machines and Artificial Immune System to detect and classify malicious data and possible cyber attacks. Analyzing Modules at different levels are trained using data that are relevant to their levels and will also be able to communicate with each other in order to improve the detection performance. Simulation results demonstrate that this is a promising methodology for improving system security through the identification of malicious network traffic, and the detection efficiency is improved by applying the optimal communication routing.