Optimization and Improvement of Spam Email Detection using Deep Learning Approaches

سال انتشار: 1401
محل انتشار: دوفصلنامه مجله کامپیوتر و رباتیک، دوره: 15، شماره: 2
کد COI اختصاصی: JR_JCR-15-2_006
زبان مقاله: انگلیسی
تعداد مشاهده: 113

نویسندگان

Department of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows. Iran

چکیده

Today, one of the widely used fields in artificial intelligence is text mining methods, which due to the expansion of virtual space and the increase in the use of media and social messengers, and on the other hand, the ability of these methods to extract the desired information from a very large volume of Unstructured text files have a special place. for example, one of its applications can be mentioned in spam detection. Nowadays, the presence of spam content in social media is increasing drastically, and therefore spam detection has become critical. Users receive many text messages through social networks. These messages contain malicious links, programs, etc., and it is necessary to identify and control spam texts and emails to improve social media security. There are various techniques for this, among which neural networks have shown more effective results. In this article, an approach based on deep learning using an LSTM neural network and Glove word embedding method is introduced to display text word vectors to detect spam emails. The results of the proposed model have been evaluated using accuracy criteria. This model has shown successful and acceptable performance by achieving ۹۸.۳۹% and ۹۹.۴۹% accuracy on two different data sets.

کلیدواژه ها

spam emails, LSTM, GloVe, deep learning

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