Using an Artificial Neural Network to Improve Email Security

Email is one of the most widely used features of internet, and it is the most convenient method of transferring messages electronically. However, email productivity has been decreased due to phishing attacks, spam emails, and viruses. Recently, filtering the email flow is a challenging task for researchers due to techniques that spammers used to avoid spam detection. This research proposes an email spam filtering system that filters the spam emails using artificial back propagation neural network (BPNN) technique. Enron1 dataset was used, and after the preprocessing, TF-IDF algorithm was used to extract features and convert them into frequency. To select best features, mutual information technique has been applied. Performance of classifiers were measured using BoW, n-gram, and chi-squared methods. BPNN model was compared with Naïve Bayes and support vector machine based on accuracy, precision, recall, and f1-score. The results show that the proposed email spam system achieved 98.6% accuracy with cross-validation.

Overview of topical issues in basic spam filtering methods and analysis of their effectiveness

Представлен анализ основных проблем фильтрации почтового спама, современных методов фильтрации нежелательных писем и способов обхода систем защиты. Вводится понятие « легитимного спама » - новой проблемы, с которой сталкиваются пользователи электронной почты. Рассмотрены методы представления текста: bag-of-words и Embedding-пространство, а также методы классификации: искусственные нейронные сети, метод опорных векторов, наивный байесовский классификатор. В работе определены эффективные методы, построенные на анализе текста, для решения задач обнаружения различных видов спама: типичного ( известного системе ) , составленного при помощи методов обхода систем детекции спама, и легитимного. An analysis of the main problems of filtering mail spam, modern methods of filtering unwanted letters and methods of bypassing security systems is presented. The concept of “legitimate spam” is being introduced - a new problem that email users face. Methods of text presentation are considered: bag-of-words and Embedding-space, as well as classification methods: artificial neural networks, the method of reference vectors, naive Bayesian classifier. The work identifies effective methods based on text analysis, for solving the problems of detecting various types of spam: a typical (known to system), compiled using methods of bypassing spam detection systems, and legitimate.

E-Mail Spam Filtering

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.

Research on the Methods and Key Techniques of Web Archive Oriented Social Media Information Collection

Social media information collection and preservation is a hot issue in the field of Web Archive. This paper makes a comparative analysis of the different social media information collection methods, deeply analyzes the key techniques of the three important parts-collection, evaluation and preservation in the information collection process, and provides the solutions for the problems in the key techniques. Through analysis, the collection method suitable for the social media information is found. In terms of the problem that social websites impose restrictions on the call frequency of API, the paper provides solutions, for example, use the multiplexing mechanism, use the naive Bayesian algorithm to solve the spam filtering problem, and use MongoDB Dbased distributed storage to store collected massive data.

Analysis of Machine Learning Methods Using Spam Filtering

The paper considers methods of the naive Bayesian classifier. Experiments that show independence between traits are described. Describes the naive Bayesian classifier used to filter spam in messages. The aim of the study is to determine the best method to solve the problem of spam in messages. The paper considers three different variations of the naive Bayesian classifier. The results of experiments and research are given.

Study of Chinese spam filtering Based on Improved Naive Bayesian Classification Algorithm

Spam is a growing threat to mobile communications. This paper puts forward some mitigation technologies, including white list and blacklist, challenge response and content-based filtering. However, none are perfect and it makes sense to use an algorithm with higher accuracy for classification. Bayesian classification method shows high accuracy in spam processing, so it has attracted extensive attention. In this paper, a Bayesian classification method based on annealing evolution algorithm is introduced into Chinese spam filtering to improve the accuracy of classification. Our simulation results show that the algorithm has better performance in spam filtering.

Boyer Moore string-match framework for a hybrid short message service spam filtering technique

Advances in technology and the proliferation of mobile device have continued to advance the ubiquitous nature of computing alongside their many prowess and improved features it brings as a disruptive technology to aid information sharing amongst many online users. This popularity, usage and adoption ease, mobility, and portability of the mobile smartphone devices have allowed for its acceptability and popularity. Mobile smartphones continue to adopt the use of short messages services accompanied with a scenario for spamming to thrive. Spams are unsolicited message or inappropriate contents. An effective spam filter studies are limited as short-text message service (SMS) are 140bytes, 160-characters, and rippled with abbreviation and slangs that further inhibits the effective training of models. The study proposes a string match algorithm used as deep learning ensemble on a hybrid spam filtering technique to normalize noisy features, expand text and use semantic dictionaries of disambiguation to train underlying learning heuristics and effectively classify SMS into legitimate and spam classes. Study uses a profile hidden Markov network to select and train the network structure and employs the deep neural network as a classifier network structure. Model achieves an accuracy of 97% with an error rate of 1.2%.