UNSAFE URL DETECTION FOR SECURE WEB SEARCH

Malicious URL (or) malicious website is a common and serious threat to cyber security. Naturally, search engine becomes the backbone of information management. Nevertheless, the flooding of large number of malicious websites on search engine has posed tremendous threat to our users. Most of exiting systems to detect malicious websites focus on specific attack. At the same time, available browser extensions based on blacklist are powerless to countless websites. Therefore, it is essential that any data leaving the client side should be effectively masked such that the server cannot interpret any valuable information from the masked data. Here propose the first PPSB service. It provides strong security guarantees that are missing in existing SB services. In particular, it inherits the capability of detecting unsafe URLs, while at the same time protects both the user’s privacy (search history) and blacklist provider’s proprietary assets (the list of malicious URLs). In this work, proposed a model which encrypts the users’ sensitive data to prevent privacy from both outside analysts and service provider. Also, completely supports selective aggregate functions for online user behavior analysis and guaranteeing differential privacy. Here AES (Advanced Encryption Standard) algorithm is used for encrypting users’ online search behavior data. Authenticated users can only decrypt the history with the help of secret key sharing and verification process. User feedback system helps to intimate the any malicious URL to the administrator of the search engine. Administrator can update the malicious URL database frequently.