The Optimization Analysis of Phishing Email Filtering in Network Fraud based on Improved Bayesian Algorithm

Mail transmission was not only the main function of information system, but also the main way of network virus and Trojan horse transmission, which has a key impact on the running state of information. In order to deal with the threats of network viruses and Trojans and improve the level of e-mail management, this paper studies the filtering of information system, and proposes a phishing e-mail filtering method based on Improved Bayesian model. MATLAB simulation results show that the consistency p between the amount of data sent by e-mail and the amount received is good, the consistency rate reached 92.3%. the data security level is 95%, encryption proportion / data proportion ratio under Bayesian optimization are higher than those of unfiltered method，which up to 97.2%. Therefore, the Bayesian optimization model constructed in this paper can meet the needs of phishing email filtering in information communication at this stage.

Python for Automating Machine Learning Tasks

Machine learning is used in a variety of computational tasks where designing and programming explicit algorithms with good performance is not easy. Applications include email filtering, recognition of network intruders or malicious insiders working towards a data breach. In this article we will focus on basics of machine learning, tasks and problems and various machine learning algorithms. The article discusses the Python programming language as the best language for automating machine learning tasks.

Northern Bald Ibis Algorithm-Based Novel Feature Selection Approach

Emails have become one of the popular and flexible web or mobile-based applications that enables users to communicate. For decades, the most severe problem identified in email applications was unwanted emails. Electronic spam is also referred as spam emails, in which unsolicited and unwanted mails are Received. Making an email mailbox clean by detecting and eliminating all the spam mails is a challenging task. Classification-based email filtering is one of the best approaches used by many researchers to deal with the spam email filtering problem. In this work, the NOA optimization algorithm and the SVM classifier are used for getting an optimal feature subset of the Enron-spam dataset and classifying the obtained optimal feature subset. NOA is a recently developed metaheuristic algorithm which is driven by mimicking the energy saving flying pattern of the Northern Bald Ibis (Threskiornithidae). The performance comparisons have been made with other existing methods. The superiority of the proposed novel feature selection approach is evident in the analysis and comparison of the classification results.

Spam image email filtering using K-NN and SVM

<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>