Phishing attacks are one of the most common techniques used to acquire sensitive information including passwords, credit card information or account details. While many technologies seek to detect phishing, it's effectiveness relies on circumventing those sensors. With social engineering tactics, hackers use terms like "Urgent! Attention required in order to keep your account active" to trick employees into clicking on bad links. The newest phishing scam today involves sending a fake invoice loaded with malware.  

Modern machine learning algorithms can detect the change in a user's behavior from the moment the credentials are compromised. Detection can be tied to specific activities such as a series of failed login attempts, an atypical IP address or unusual activity in general. 

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Speaker: Robert Pavone, Senior Solutions Architect, Gurucul

Bob Pavone brings over 25 years of experience of cross-functions IT solutions. Bob has extensive experience in cybersecurity operations, management and analytics. He is skilled in managing technical teams developing and implementing IT strategies. Bob’s has held senior solutions architect and sales engineering positions with security providers including S1, Netegrity (acquired by CA), Siemens Enterprise Communications, Unify Inc. and Network Computing Architects. Bob holds a B.S. in Computer Science from New York Institute of Technology.  

 

 

Webinar Replay

"Hands down the most sophisticated example of behavior analytics..."

- SC Magazine


Gurucul is changing the way enterprises protect themselves against insider threats, fraud, account compromise and data exfiltration in both on-premises and cloud environments. The company’s Unified Security and Risk Analytics platform uses machine learning and predictive anomaly detection algorithms to reduce the attack surface for accounts, and to eliminate unnecessary access rights and privileges. Identify, predict and prevent breaches with Gurucul Risk Analytics.



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