Variant Payload Prevention: Applying Data Science to Stop the Stealthiest Threats
Cybereason’s Variant Payload Prevention employs a new proprietary algorithm for fuzzy similarity called Binary Similarity Analysis (BSA).
Yonatan Striem-Amit is a machine learning, big data analytics and visualization technology expert, with over a decade of experience applying analytics to security in the Israeli Defense Forces and Israeli Governmental Agencies.
Tim Amey is a senior technologist with over 20 years of experience helping customers throughout North America solve complex security challenges in highly innovative ways. Serving as the Field CTO for North America, Tim joined Cybereason over 6 years ago. Prior to Cybereason he worked with industry leading technology companies such as TippingPoint, Q1 Labs / IBM, F5, and Bromium.
Cody is a Product Marketing Manager at Cybereason leading the go-to-market strategy for NGAV, endpoint protection and cloud workload security solutions. Before joining Cybereason, Cody led and supported product launches for Dell Technologies in their APEX Cloud and security business, primarily around managed data center services. He also brings over 10 years of experience in the public sector planning for, managing and responding to security threats against the United States.
Cybereason’s Variant Payload Prevention employs a new proprietary algorithm for fuzzy similarity called Binary Similarity Analysis (BSA).
Predictive prevention means that Cybereason stops ransomware with the highest degree of confidence based on subtle behaviors and attacker activity - we automatically see what others miss and infer the attacker’s next move without manual intervention...