dynamic identification of spam words phishing urls misleading products and agencies
MALATHI S,Bhavani R,DhanaLakshmi K ,Varshini M
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 22 April,2024 Pages:1886-1893
Abstract
In general, Spam has become the platform of choice use by cyber-criminals to spread malicious payloads such as viruses and trojans. Collaborative spam detection techniques can deal with large scale e-mail data contributed by multiple sources; however, they have the well-known problem of requiring disclosure of e-mail content. In our Project, we are designing Social media like web page to filter the Spam messages that are getting posted. This will filter the irrelevant and spam posts so that wrong information can be avoided. Phishing URLs are also avoided in order to remove the duplicate websites. Wrong information can be removed based on the public votes. We are using Big Data and Block chain Technology for further Data analysis and data security process. Hadoop distributed File System (HDFS), is used for Big Data analysis. In Block chain, 4 different Algorithms are used namely, Asymmetric Key Algorithm, Digital Signature Algorithm, Secured Hash 256 (SHA256) Algorithm & Merkle hash Tree Algorithm.
Kewords
spam detection, Phishing links, Big data, Hadoop distributed file System, Block chain.
Reference
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