“Mark's diligence and constant pursuit of knowledge make him ideal for positions that involve complex systems and require meticulous detail.”
Activity
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The Telecom Industry has a long history of collaboration to fight abuse that impacts their own industry. The challenge is when the abuse impacts…
The Telecom Industry has a long history of collaboration to fight abuse that impacts their own industry. The challenge is when the abuse impacts…
Liked by Mark Schaaf
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🔎 Hunt for threats at scale with us! Collaborate with talented threat intelligence and defensive research teams while analyzing massive data sets…
🔎 Hunt for threats at scale with us! Collaborate with talented threat intelligence and defensive research teams while analyzing massive data sets…
Liked by Mark Schaaf
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It’s been 2190 days/ 6 years since I officially joined Bank of America. From being an intern to transitioning to full time, contributing to various…
It’s been 2190 days/ 6 years since I officially joined Bank of America. From being an intern to transitioning to full time, contributing to various…
Liked by Mark Schaaf
Experience
Education
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University of South Carolina
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Activities and Societies: Association of Computing Machinery (ACM)
Licenses & Certifications
Patents
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Detecting Malicious Email Campaigns with Unique but Similarly-Spelled Attachments
Issued US 12177246 B2
Email logs from network appliances are retrieved, filtered, normalized, and converted into field-based organized data for comprehensive analysis. Cluster analysis is performed. Filenames of email attachments are transformed into numerical vectors and a cosine similarity termset analysis is performed on the numerical vectors. Data is organized into time bins for burst detection. Statistical analysis is performed on the time bins. Pattern recognition is performed to identify alphanumeric…
Email logs from network appliances are retrieved, filtered, normalized, and converted into field-based organized data for comprehensive analysis. Cluster analysis is performed. Filenames of email attachments are transformed into numerical vectors and a cosine similarity termset analysis is performed on the numerical vectors. Data is organized into time bins for burst detection. Statistical analysis is performed on the time bins. Pattern recognition is performed to identify alphanumeric similarities in the filenames of the attached files in order to detect malicious email campaigns. Machine learning may be used to optimize the cosine similarity threshold and other query variables, and to update existing cybersecurity filters and firewalls. Mitigation can be performed to remove malicious emails that were delivered to recipient mailboxes.
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I recently developed a tool for analyzing Command & Control (C2) infrastructure identification using statistical methods. This project combines my…
I recently developed a tool for analyzing Command & Control (C2) infrastructure identification using statistical methods. This project combines my…
Liked by Mark Schaaf
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