Spam image email filtering using K-NN and SVM

Abstract

<p>The developing utilization of web has advanced a simple and quick method for e-correspondence. The outstanding case for this is e-mail. Presently days sending and accepting email as a method for correspondence is prominently utilized. Be that as it may, at that point there stand up an issue in particular, Spam mails. Spam sends are the messages send by some obscure sender just to hamper the improvement of Internet e.g. Advertisement and many more. Spammers introduced the new technique of embedding the spam mails in the attached image in the mail. In this paper, we proposed a method based on combination of SVM and KNN. SVM tend to set aside a long opportunity to prepare with an expansive information set. On the off chance that "excess" examples are recognized and erased in pre-handling, the preparation time could be diminished fundamentally. We propose a k-nearest neighbor (k-NN) based example determination strategy. The strategy tries to select the examples that are close to the choice limit and that are effectively named. The fundamental thought is to discover close neighbors to a question test and prepare a nearby SVM that jelly the separation work on the gathering of neighbors. Our experimental studies based on a public available dataset (Dredze) show that results are improved to approximately 98%.</p>

Phishing Image Spam Classification Research Trends: Survey and Open Issues

“…This is displayed in table VII. Table VIII presents the keys of the abbreviations as used in Tables IV, V, VI and VII [3] and has been adopted by many researchers in their works [44], [35], [17], [33], [32], [31]. SVM is suitable for binary classification problems but difficult to handle large datasets [27].…”

“…Convolutional Neural Network has recently been used to create a text-based spam classifier with the introduction of long short time memory neural network (LSTM NN) and an accuracy of more than 92-98% has been achieved [18]. [28], [44] used KNN and Naïve Bayes to implemented his work with the Dredze image dataset. The authors used a distributed associative memory tree to extract features of the image.…”

“…This concept which was recently used by [1] to detect phishing websites produced high detection accuracy. Also, the authors revealed that fog-based services are faster than cloud-based services and that it is manageable and easy [20], [31], [32], [33], [34], [35], [17], [27], [9], [44] 4. K-Nearest Neighbor 3 y y [22], [28], [29], [44] 5 to implement a machine learning algorithm on fog nodes than on the cloud.…”

Parking entrance control using license plate detection and recognition

“…SVM becomes popular because it gave high rate in handwritten digit classification. SVM was used for image classification in [16]. This algorithm search for the maximum separating hyperplane, which is defined by the hyperplane with the maximum distance between the training tuples.…”

Image classification of malaria using hybrid algorithms: convolutional neural network and method to find appropriate K for K-nearest neighbor

“…The process of recognition of data used in the teaching is obtained from fully connected layer of CNN. This process makes the value of main features or variables of 1000 variables sent to KNN algorithm [17], which is an approach suitable for classification of medical image data. With respect to learning characteristics of KNN algorithm, no classification model is pre-established.…”