If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.
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All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com
Sale & Press
If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com
Help
If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.
Customer service
All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com
Sale & Press
If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com
Malware (Malicious Software) has become one of the major threats to todays computing world. Although Antivirus programs provide primary line of defense and detect previously known malware, they, along with other detection mechanisms falling short of detecting present day new and unknown complex malware. In this work, a new approach to detect malware, which uses reverse engineering and machine learning techniques was proposed and implemented. While Reverse Engineering was used to analyze malware, genuine software and extract important features and construct datasets from those features, machine learning techniques were used to build classification models, which would classify a new executable as either malware or genuine software.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
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⚠️ California Proposition 65 Warning: Some products sold on this website may expose you to chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm. For more information, visit www.P65Warnings.ca.gov.