Malicious email detection via machine learning approaches

Jinyu Ding; Yutong Ge; Mingxuan Gong

doi:10.1117/12.2624886

13 December 2021 Malicious email detection via machine learning approaches

Jinyu Ding, Yutong Ge, Mingxuan Gong

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120871E (2021) https://doi.org/10.1117/12.2624886
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

Nowadays, despite the popularity of smartphones in our daily lives, emails are still the most widespread way for people to communicate and exchange information in business and many other circumstances. However, a tremendous problem called malicious email, also known as spam, bothers people and demands constant detection and block. This paper discusses machine learning approaches to achieve malicious email detection. The data for training is more than 10,000 raw emails with Chinese text as well as features including server name, IP address, timestamp, and content. First, the contents are split into words via feature engineering. Then, the malicious email detection is carried out by multiple machines learning methods, including Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and K Nearest Neighbor (KNN), respectively. The performance of these models is evaluated by criteria like precision, recall score, F1 score, and time cost. It is shown that the Naïve Bayes model yields the best results, with the F1 score being higher than 97%, which indicates that our model is promising in practice.

Citation Download Citation

Jinyu Ding, Yutong Ge, and Mingxuan Gong "Malicious email detection via machine learning approaches", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120871E (13 December 2021); https://doi.org/10.1117/12.2624886

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