- In recent years, cyber-attacks against businesses and organizations have increased. Such attacks usually result in significant damage to the organization, such as the loss and/or leakage of sensitive and confidential information. Because email communication is an integral part of daily business operations, attackers frequently leverage email as an attack vector in order to initially penetrate the targeted organization. Email message allows the attacker to deliver dangerous content to the victim, such as malicious attachments or links to malicious websites. Existing email analysis solutions analyze only specific parts of the email using rule-based methods, while other important parts remain unanalyzed. Existing anti-virus engines primarily use signature-based detection methods, and therefore are insufficient for detecting new unknown malicious emails. Machine learning methods have been shown to be effective at detecting maliciousness in various domains and particularly in email. Previous works which used machine learning methods suggested sets of features which offer a limited perspective over the whole email message. In this paper, we propose a novel set of general descriptive features extracted from all email components (header, body, and attachments) for enhanced detection of malicious emails using machine learning methods. The proposed features are extracted just from the email itself; therefore, our features are independent, since the extraction process does not require an Internet connection or the use of external services or other tools, thereby meeting the needs of real-time detection systems. We conducted an extensive evaluation of our new novel features against sets of features suggested by previous academic work using a collection of 33,142 emails which contains 38.73% malicious and 61.27% benign emails. The results show that malicious emails can be detected effectively when using our novel features with machine learning algorithms. Moreover, our novel features enhance the detection of malicious emails when used in conjunction with features suggested by related work. The Random Forest classifier achieved the highest detection rates, with an AUC of 0.929, true positive rate (TPR) of 0.947, and false positive rate (FPR) of 0.03. We also present the IDR (integrated detection rate), a new measure which helps calibrate the threshold of a machine learning classifier in order to achieve the optimal TP and FP rates, which are the most important measures for a real-time and practical cyber-security application.