Privacy Preserving Spam Detection Using Hadoop
| dc.contributor.author | SANDEEP, SK | |
| dc.contributor.author | SAMBUDDHA, BISWAS | |
| dc.contributor.author | SAURABH, RAJNALA | |
| dc.date.accessioned | 2018-06-19T10:28:22Z | |
| dc.date.available | 2018-06-19T10:28:22Z | |
| dc.date.issued | 2018-06-19T10:28:22Z | |
| dc.description.abstract | Spam has become the platform of choice used by cyber-criminals to spread malicious payloads such as viruses and trojans. In this project, we consider the problem of early detection of spam campaigns. Existing collaborative spam detection techniques can deal with a lot of e-mail data contributed by various sources; however, they have a common and major problem of requiring disclosure of e-mail content. These hashes which preserve distance are one of the common solutions used for maintaining the privacy of the content of the e-mail while allowing the messages to get classified for detecting the spam. However, distance-preserving hashes are not scalable, thus making large-scale collaborative solutions difficult to implement. To solve this, in this project, we propose a method using Big Data which uses privacy-preserving collaborative spam detection platform built based on a standard Map Reduce facility. It uses a highly parallel encoding technique that enables the detection of spam campaigns in competitive times. The evaluation of our system’s performance is done using a huge elaborate spam base and show that our technique performs very well against the creation and delivery overhead of the current spam generation tools. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/9442 | |
| dc.language.iso | en | en_US |
| dc.subject | SANDEEP SK | en_US |
| dc.subject | SAMBUDDHA BISWAS | en_US |
| dc.subject | SAURABH RAJNALA | en_US |
| dc.subject | 1NH14CS168 | en_US |
| dc.subject | 1NH14CS113 | en_US |
| dc.subject | 1NH14CS166 | en_US |
| dc.title | Privacy Preserving Spam Detection Using Hadoop | en_US |
| dc.type | Other | en_US |