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US Patent 11961021 Complex application attack quantification, testing, detection and prevention

Patent 11961021 was granted and assigned to Ivanti on April, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Applicant
RiskSense
RiskSense
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Current Assignee
Ivanti
Ivanti
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119610210
Patent Inventor Names
Srinivas Mukkamala0
Anand Paturi0
Date of Patent
April 16, 2024
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Patent Application Number
169325130
Date Filed
July 17, 2020
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Patent Citations
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US Patent 10699017 Determining coverage of dynamic security scans using runtime and static code analyses
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US Patent 7941855 Computationally intelligent agents for distributed intrusion detection system and method of practicing same
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US Patent 9306962 Systems and methods for classifying malicious network events
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US Patent 9407606 Methods and systems for context-based application firewalls
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US Patent 10050999 Security threat based auto scaling
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US Patent 11297091 HTTP log integration to web application testing
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Patent Citations Received
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US Patent 12081569 Graph-based analysis of security incidents
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Patent Primary Examiner
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Bharat Barot
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CPC Code
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G06N 5/04
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H04L 67/02
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G06N 20/00
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H04L 63/1416
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H04L 63/1433
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G06F 21/54
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G06F 2221/033
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Patent abstract

An apparatus and method for cyber risk quantification calculated from the likelihood of a cyber-attack on the target enterprise and/or cyber ecosystem based on its security posture. The cyber-attack likelihood can be derived as a probability-based time-to-event (TTE) measure using survivor function analysis. The likelihood probability measure can also be passed to cyber risk frameworks to determine financial impacts of the cyber-attacks. Embodiments of the present invention also relate to an apparatus and method (1) to identify and validate application attack surfaces and protect web applications against business logic-based attacks, sensitive data leakage and privilege escalation attacks; and/or (2) that protects web applications against business logic-based attacks, sensitive data leakage and privilege escalation attacks. This can include implementing an intelligent learning loop using artificial intelligence that creates an ontology-based knowledge base from application request and response sequences. Stochastic probabilistic measures are preferably applied to a knowledge base for predicting malicious user actions in real time.

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