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E-mail is the traditional, primary and most vital part of communication within professional organizations. They keep minutes of important discussions, confidential documents in the form of attachments, prominent business contact information and much more. As a result, hackers or intruders often use e-mail as a means of delivering dangerous content to the victim via attachments or by providing links to malicious websites. Businesses around the world are working hard to detect malicious content in their communications media by deploying robust antivirus firewalls.
But how secure are they? Many choose antivirus engines based on their popularity rather than their performance. The myth that the famous antivirus packages give you maximum security is now debunked by Email-sec-360 ° . According to Phys Org, it surpbades 60 other popular antivirus packages known to us.
Email-sec-360 ° is developed by Aviad Cohen, a Ph.D. student, and researcher at the Ben-Gurion University of the Negev (BGU) Malware Lab researchers. It detects unknown malicious e-mails much more precisely than popular antivirus products such as Kaspersky, McAfee, Avast, etc.
Email-sec-360 ° vs other popular antivirus engines
Current antivirus engines use rule-based methods to parse specific mail sections. These often overlook other important parts of the email. Dr. Nir Nissim, head of David's Malware Lab Family and Janet Polak at Cyber @ BGU, said that existing antivirus engines use signature-based detection methods. These methods are sometimes insufficient to detect new and unknown malicious e-mails.
However, Email-sec-360 ° is based on machine learning methods and exploits 100 general descriptive features extracted from all messaging components, including header, body and attachments . Another interesting aspect of this method is that it does not require internet access. Thus, it provides transparent, real-time threat detection and can be easily deployed by any individual or organization.
A well-tested approach by the Malware Lab
Researchers used a collection of 33,142 emails, of which 12,835 20,307 malicious emails were obtained between 2013 and 2016. Later, they compared their detection model to 60 advanced antivirus engines as well as to previous research. In doing so, they found that their system outperformed the best antivirus engine, Cyren, by a 13% range.
The Malware Lab method of BGU against the others
BGU Malware Lab plans to Extend this method by including the search and badysis of attachments (PDF and Microsoft Office documents) in the Email-sec-360 °. Dr. Nissim adds, "since these are often used by hackers to get users to open and spread viruses and malware." They also plan to develop an online system that badesses the security risk posed by an e-mail message. This system will be based on advanced methods of machine learning and would also allow users to submit suspicious e-mails and quickly obtain a malicious score. The system will also recommend how to handle email and help to collect benign and malicious emails for research purposes.
Read more about Email-sec-360 ° in the blog Phys Org
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