A novel approach for phishing emails real time classifica-tion using k-means algorithm

Abstract

The dangers phishing becomes considerably bigger problem in online networking, for example, Facebook, twitter and Google+. The phishing is normally completed by email mocking or texting and it frequently guides client to enter points of interest at a phony sites whose look and feel are practically indistinguishable to the honest to goodness. Non-technical user resists learning of anti-phishing technic. Also not permanently remember phishing learning. Software solutions such as authentication and security warnings are still depending on end user action. In this paper we are mainly focus on a novel approach of real time phishing email classification using K-means algorithm. For this we uses 160 emails of last year computer engineering students. we get True positive of legitimate and phishing as 67% and 80% and true negative is 30 % and 20%.,which is very high so we ask same users reasons which I mainly categories into three categories ,look and feel of email, email technical parameters, and email structure.

Improving of Fingerprint Segmentation Images Based on K-MEANS and DBSCAN Clustering

“…Characteristics such as gradient cohrence and ridge direction can be classified into gradient based characteristics. The gradient coherence and ridge direction features are calculated by (16) and (20) respectively.…”

“…The characteristic extraction vector for each block is represented by (23). On the one hand, the K-means algorithm is a popularly unsupervised machine learning models [20] used for clustering technique of the data [21]. On the other hand, it is simplified to implementation, eased of interpretation, faster and adapted to sparse data.…”

An Increasing Performance of Fingerprint Image Segmentation Based on Clustering and Algorithms

“…The gradient is used to obtain the directional variation of the intensity value along with an image I mg direction of size x Xy. Characteristics like gradient coherence and direction of the ridge can be classified into characteristics based on gradient.The coherence of the gradient and the direction of the ridge are calculated respectively by (16) and (20).…”

“…K-Means Clustering K-means classifier is used in our suggested technique to classify the five extracted features, variance, mean difference, gradient consistency, ridge direction, and energy spectrum from each local block in the fingerprint image to distinguish the foreground region from the noisy background region.The characteristic vector of extraction is represented for each block by (23). The K-means algorithm, on the one hand, is a popularly unsupervised machine learning model [20] used in data clustering technique [21]. On the other hand, it is simplified to implement, easier to interpret, faster and adapted to sparse data.…”