A New Bio-Inspired Method for Spam Image-Based Emails Filtering

2021 ◽  
Vol 11 (2) ◽  
pp. 29-50
Author(s):  
Abdelkrim Latreche ◽  
Kadda Benyahia
Keyword(s):  
Electronic Mail ◽  
Online Communication ◽  
Gray Level ◽  
Spam Filtering ◽  
New Techniques ◽  
The Social ◽  
Texture Characteristics ◽  
Security Problem ◽  
Occurrence Matrix ◽  
E Mail

Electronic mail has become one of the most popular and frequently used channels for personal and professional online communication. Despite its benefits, e-mail faces a major security problem, which is the daily reception of a large number of unsolicited electronic messages, known as “spam emails.” Today, most electronic mail systems have simple spam filtering mechanisms based on text spam filtering technologies. To circumvent these filters, spammers are introducing new techniques of embedding spam messages in the image attached to the mail. In this article, the authors propose a new method for spam image filtering. The proposed system can distinguish between legitimate images from spam images based on the texture characteristics of the image attached to an email. From each image, around 20 characteristics can be extracted from the gray level co-occurrence matrix (GLCM). Then, to classify the images as spam or ham, the authors use a new metaheuristic nature-inspired model for building classifiers based on the social worker bees and enhanced nearest-centroid classification method.

scholarly journals Hybrid Spam Filtration Method using Machine Learning Techniques

2019 ◽  
Vol 8 (9) ◽  
pp. 1818-1821
Keyword(s):  
Electronic Mail ◽  
Online Communication ◽  
True Positive Rate ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Filtration Method ◽  
Learning Techniques ◽  
Positive Rate ◽  
Bayes Algorithm ◽  
E Mail

Electronic mail (e-mail) is one of the most prevalent approaches for online communication and transferring data through web because of its quick and easy distribution of data, low distribution cost and permanency. Despite these benefits there are certain weaknesses of e-mail. Among these, spam also known as junk e-mail tops. Spam is set of unwanted or inappropriate messages sent over the internet to a massive amount of users for the purpose of marketing, phishing, disseminating malware, etc.With the internet becoming the dominant platform anti-spam solutions are of great use today. This paper illustrates an efficient hybrid spam filtration method using Naïve Bayes algorithm and Markov Random Field technique, which detects and filters spam messages. The proposed method is evaluated based on its accurateness, meticulousness and time consumption. The results confirm that the proposed hybrid method achieves high percentage of true positive rate in finding e-mail spam messages.

scholarly journals Face Identification on Login Security Using Algorithm Combination of Viola-Jones and Cosine Similarity

2020 ◽  
Vol 4 (1) ◽  
pp. 203-211
Author(s):  
FADHILLAH AZMI ◽  
Amir Saleh ◽  
N P Dharshinni
Keyword(s):  
Face Recognition ◽  
Data Security ◽  
Image Matching ◽  
Cosine Similarity ◽  
Facial Features ◽  
Gray Level ◽  
Image Detection ◽  
Texture Characteristics ◽  
Occurrence Matrix ◽  
Different Characteristics

Data security by using an alphanumeric combination password is no longer used, so it needs to be added security that is difficult to be manipulated by certain people. One type of security is the type of biometrics technology using face recognition which has different characteristics by combining the Viola-Jones algorithm to detect facial features, GLCM (Gray Level Co-occurrence Matrix) for extracting the texture characteristics of an image, and Cosine Similarity for the measurement of the proximity of the data (image matching). The image will be detected using the Viola-Jones algorithm to get face, eyes, nose, and mouth. The image detection results will be calculated the value of the texture characteristics with the GLCM (Gray Level Cooccurrence Matrix) algorithm. Image matching using cosine similarity will determine or match the data stored in the database with new image input until identification results are obtained. The results obtained in this study get the level of accuracy of the identification of the three algorithms by 77.20% with the amount of data that was correctly identified as many as 386 out of 500 images.Keywords: Security, face recognition, Viola-Jones, Cosine Similarity.

scholarly journals E-Mail Spam Filtering

2021 ◽  
Vol 9 (11) ◽  
pp. 1265-1269
Author(s):  
Rohitkumar R Upadhyay
Keyword(s):  
Nearest Neighbor ◽  
Low Cost ◽  
Online Communication ◽  
Spam Filtering ◽  
K Nearest Neighbor ◽  
Knowledge Based ◽  
Classification Evaluation ◽  
Additive Regression ◽  
E Mail ◽  
Email Spam

Abstract: E-mail is that the most typical method of communication because of its ability to get, the rapid modification of messages and low cost of distribution. E-mail is one among the foremost secure medium for online communication and transferring data or messages through the net. An overgrowing increase in popularity, the quantity of unsolicited data has also increased rapidly. Spam causes traffic issues and bottlenecks that limit the quantity of memory and bandwidth, power and computing speed. To filtering data, different approaches exist which automatically detect and take away these untenable messages. There are several numbers of email spam filtering technique like Knowledge-based technique, Clustering techniques, Learning-based technique, Heuristic processes so on. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. This paper illustrates a survey of various existing email spam filtering system regarding Machine Learning Technique (MLT) like Naive Bayes, SVM, K-Nearest Neighbor, Bayes Additive Regression, KNN Tree, and rules. Henceforth here we give the classification, evaluation and comparison of some email spam filtering system and summarize the scenario regarding accuracy rate of various existing approaches. Keywords: e-mail spam, unsolicited bulk email, spam filtering methods.

Detection of image region-duplication forgery based on gray level co-occurrence matrix

2012 ◽  
Vol 31 (6) ◽  
pp. 1628-1630
Author(s):  
Jia-jia OU ◽  
Bi-ye CAI ◽  
Bing XIONG ◽  
Feng LI
Keyword(s):  
Gray Level ◽  
Image Region ◽  
Region Duplication ◽  
Occurrence Matrix

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

Transnational Precursors of American Realism

2019 ◽  
pp. 17-39
Author(s):  
Renate von Bardeleben
Keyword(s):  
Gustave Flaubert ◽  
Honore De Balzac ◽  
Formative Period ◽  
Guy De Maupassant ◽  
Narrative Strategies ◽  
New Techniques ◽  
Fyodor Dostoevsky ◽  
Industrial Age ◽  
The Social ◽  
American Authors

This chapter concentrates on European realist innovators—Björnstjerne Björnson, Ivan Turgenev, Leo Tolstoy, Fyodor Dostoevsky, Honoré de Balzac, Gustave Flaubert, Guy de Maupassant—and their effect on the formative period of American realism. It studies in detail the transatlantic development of new techniques and discusses the ways in which these new methods were reflected in the works of American authors and critics. Inspired by the theories and practice of their precursors, American writers felt liberated to introduce new narrative strategies to represent America’s rising urbanism, the struggles of the social classes, and the increase of social mobility in the industrial age. They also dealt with the emancipated “New Woman” and the changing relationship between the sexes. The guiding principles on which writers on both sides of the Atlantic agreed were truth, sincerity, and frankness.

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