An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing

An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing

In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by updating the system daily. We introduce an autonomous function for a server to generate training examples, in which double-bounce emails are automatically collected and their class...

Locality-sensitive hashing and biological network alignment

Large biological networks contain much information about the functionality of protein-protein interactions and other macromolecular relationships. There are certain small subsets of these graphs which display similar characteristics that appear more frequently than others; these motifs can be used to uncover key information about the structure of the networks. Existing algorithms use exact iden...

Neural Network Model for Email-Spam Detection

Email spam is a word that we come across in our daily life. The word spam means junk mails. The unsolicited emails that are received by any person in his/her mailbox are called spam. These junk mails are usually sent in bulk for advertising and marketing some products. This work presents a neural network approach to intrusion detection. A Multi-Layer Perceptron using Back Propagation Algorithm ...

Beyond Locality-Sensitive Hashing

We present a new data structure for the c-approximate near neighbor problem (ANN) in the Euclidean space. For n points in R, our algorithm achieves Oc(n + d logn) query time and Oc(n + d logn) space, where ρ ≤ 7/(8c2) + O(1/c3) + oc(1). This is the first improvement over the result by Andoni and Indyk (FOCS 2006) and the first data structure that bypasses a locality-sensitive hashing lower boun...

Locality-Sensitive Hashing