As a widely adopted official communications medium across the world, email is often a target of cybersecurity attacks. Spam e-mail is a growing security concern and is frequently used in phishing attacks, data stealing, and denial-of-service (DoS) attacks. Spam has increased these threats due to its fast expansion and proliferation. Most of the current research is concentrated on spam email detection as a classification problem, and very little is discussed about the implications or broadening of spam email detection in the aspect of cyber security. In this paper, this is done by analyzing spam emails from a security perspective, applying threat modeling, and using a spambot-based simulator to demonstrate how the spam emails can be used as an origin for a DoS attack. We employed a count-based vectorization approach for feature extraction to train a Naïve Bayes classifier for spam email detection. Moreover, a comparative analysis was performed by employing different machine learning classifiers, such as Adaboost, Random Forest, Support Vector Machine, and K-Nearest Neighbors, to compare the effectiveness of Naive Bayes for this task. We evaluated the performance of our method on a publicly available spam email dataset of 5728 samples, including 4360 legitimate and 1368 spam data entries. Synthetic Minority Over-Sampling Technique (SMOTE) was applied during model training to counter class imbalance. The accuracy of 98.517% indicates the effectiveness of the proposed method for spam email detection.