Abstract
E-mail is the most common method of communication because due to its ability to obtain, the rapid modification of messages and low cost of distribution. Spam causes traffic issues and bottlenecks that limit the amount of memory and bandwidth, power and computing speed. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. A methodology based on Sine- Cosine Algorithm (SCA) introduced which address the problem of space and time complexities are increased in E-Mail spam detection. In this method, WordNet optimized semantic ontology applies different methods based on semantics and similarity measures to reduce the large number of extracted textual features. This paper proposed the Enriched Firefly Optimization Algorithm (EFOA) method effectively selecting suitable features from an upper dimensional space using the fitness function. Once the best feature space is identified through EFOA, the spam classification is done using ANN. Intially, E-mail spam dataset is preprocessed, then the extracted textual features are Semantic-based reduction and Features weights updated using optimized semantic WordNet. The results obtained showed that the ANN classifier after selection of features using EFOA was able to classify e-mails as spam and non-spam. This EFOA demonstrates that the proposed method has led to a remarkable improvement compared to the SCA methods.
Highlights
With the growth in number of Internet users, e-mail has become the most widely used communication mechanism
The use of e-mails has led to a noticeable improvement in group communications, the impact of which is seen in growth of enterprises worldwide [2]
Most attributes denote the frequency of a particular word or character in the email which matches the instance
Summary
With the growth in number of Internet users, e-mail has become the most widely used communication mechanism. Over the past few years, the increased use of emails has led to the emergence and aggravation of the problems caused by spam [1]. The use of e-mails has led to a noticeable improvement in group communications, the impact of which is seen in growth of enterprises worldwide [2]. People use it for illegal and infernal purposes, phishing and fraud. It is necessary to identify these spam mails which are frauds using ANN techniques
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