Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Cross-domain information exchange is necessary to obtain information superiority in the military domain, and should be based on assigning appropriate security labels to the information objects. Most of the data found in a defense network is unlabeled, and usually new unlabeled information is produced every day. Humans find that doing the security labeling of such information is labor-intensive and time consuming. At the same time there is an information explosion observed where more and more unlabeled information is generated year by year. This calls for tools that can do advanced content inspection, and automatically determine the security label of an information object correspondingly.  presents a machine learning approach to this problem. To the best of our knowledge, machine learning has hardly been analyzed for this problem, and the analysis on topical classification presented here provides new knowledge and a basis for further work within this area. Presented results are promising and demonstrates that machine learning can become a useful tool to assist humans in determining the appropriate security label of an information object.
['Hugo Lewi Hammer', 'Kyrre Wahl Kongsgård', 'Aleksander Bai', 'Anis Yazidi', 'Nils Agne Nordbotten', 'Paal E. Engelstad']
I am writing a research paper on a idea Automatic security classification by machine learning for cross-domain information exchange
[{"from": "human", "value": "I am writing a research paper on a idea Automatic security classification by machine learning for cross-domain information exchange"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Cross-domain information exchange is necessary to obtain information superiority in the military domain, and should be based on assigning appropriate security labels to the information objects. Most of the data found in a defense network is unlabeled, and usually new unlabeled information is produced every day. Humans find that doing the security labeling of such information is labor-intensive and time consuming. At the same time there is an information explosion observed where more and more unlabeled information is generated year by year. This calls for tools that can do advanced content inspection, and automatically determine the security label of an information object correspondingly.  presents a machine learning approach to this problem. To the best of our knowledge, machine learning has hardly been analyzed for this problem, and the analysis on topical classification presented here provides new knowledge and a basis for further work within this area. Presented results are promising and demonstrates that machine learning can become a useful tool to assist humans in determining the appropriate security label of an information object."}]