Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: b spam is a widely-recognized threat to the quality and security of the b. b spam pages pollute search engine indexes, burden b crawlers and b mining services, and expose users to dangerous b-borne malware. To defend against b spam, most previous research analyzes the contents of b pages and the link structure of the b graph. Unfortunately, these heavyight approaches require full downloads of both legitimate and spam pages to be effective, making real-time deployment of these techniques infeasible for b browsers, high-performance b crawlers, and real-time b applications. ,  present a lightight,  predictive  approach to b spam classification that relies exclusively on HTTP session information (i.e., hosting IP addresses and HTTP session headers). Concretely,  built an HTTP session classifier based on our predictive technique, and by incorporating this classifier into HTTP retrieval operations,  are able to detect b spam pages before the actual content transfer. As a result, our approach protects b users from b-propagated malware, and it generates significant bandwidth and storage savings. By applying our predictive technique to a corpus of almost 350,000 b spam instances and almost 400,000 legitimate instances,  re able to successfully detect 88.2% of the b spam pages with a false positive rate of only 0.4%. These classification results are superior to previous evaluation results obtained with traditional link-based and content-based techniques. Additionally, our experiments show that our approach saves an average of 15.4 KB of bandwidth and storage resources for every successfully identified b spam page, while only adding an average of 101 microseconds to each HTTP retrieval operation. Therefore, our predictive technique can be successfully deployed in applications that demand real-time spam detection.
['Steve Webb', 'James Caverlee', 'Calton Pu']
I am writing a research paper on a idea Predicting web spam with HTTP session information
[{"from": "human", "value": "I am writing a research paper on a idea Predicting web spam with HTTP session information"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: b spam is a widely-recognized threat to the quality and security of the b. b spam pages pollute search engine indexes, burden b crawlers and b mining services, and expose users to dangerous b-borne malware. To defend against b spam, most previous research analyzes the contents of b pages and the link structure of the b graph. Unfortunately, these heavyight approaches require full downloads of both legitimate and spam pages to be effective, making real-time deployment of these techniques infeasible for b browsers, high-performance b crawlers, and real-time b applications. ,  present a lightight,  predictive  approach to b spam classification that relies exclusively on HTTP session information (i.e., hosting IP addresses and HTTP session headers). Concretely,  built an HTTP session classifier based on our predictive technique, and by incorporating this classifier into HTTP retrieval operations,  are able to detect b spam pages before the actual content transfer. As a result, our approach protects b users from b-propagated malware, and it generates significant bandwidth and storage savings. By applying our predictive technique to a corpus of almost 350,000 b spam instances and almost 400,000 legitimate instances,  re able to successfully detect 88.2% of the b spam pages with a false positive rate of only 0.4%. These classification results are superior to previous evaluation results obtained with traditional link-based and content-based techniques. Additionally, our experiments show that our approach saves an average of 15.4 KB of bandwidth and storage resources for every successfully identified b spam page, while only adding an average of 101 microseconds to each HTTP retrieval operation. Therefore, our predictive technique can be successfully deployed in applications that demand real-time spam detection."}]