Code vulnerability evaluator
AssigneeBank of America Corporation
InventorsPushkar Taneja, Yash Pant Dashputra, Sakshi Bakshi, Amar Deep Reddy
Life of this patent
Estimating the term…
Abstract
A computing platform provides code vulnerability detection and evaluation. The computing platform may use a quantum graph categorizer along with quantum polymorphism execution channels to reduce false positives and provide optimized real-time vulnerability evaluation of code and code segments. The computing platform may use a hybrid approach comprising a mix of static and dynamic vulnerability validation and real-time parallel technique execution.
Claims20
A computing platform comprising:
- at least one processor;
- a communication interface communicatively coupled to the at least one processor;
- and
- memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
- receive data related to code vulnerabilities;
- generate a quantum graph network using a quantum graph categorizer and the received data related to code vulnerabilities, wherein the quantum graph network is a dynamic quantum-graph network supported by a quantum deep embedding neural network (QDENN) and a graphical neural artificial intelligence engine;
- determine a reconciliation ratio, wherein the reconciliation ratio is a ratio of vulnerability scoring between static and dynamic vulnerability analysis;
- based on the reconciliation ratio, determine a vulnerability test case execution plan;
- and
- execute the determined vulnerability test case execution plan to determine code vulnerabilities, wherein executing the determined vulnerability test case execution plan to determine code vulnerabilities comprises using quantum entanglement to ensure evaluation results of one set of test cases are reconciled with other test results to flag any discrepancy.
A method comprising:
- at a computing platform comprising at least one processor, a communication interface, and memory:
- receiving data related to code vulnerabilities;
- generating a quantum graph network using a quantum graph categorizer and the received data related to code vulnerabilities, wherein the quantum graph network is a dynamic quantum-graph network supported by a quantum deep embedding neural network (QDENN) and a graphical neural artificial intelligence engine;
- determining a reconciliation ratio, wherein the reconciliation ratio is a ratio of vulnerability scoring between static and dynamic vulnerability analysis;
- based on the reconciliation ratio, determining a vulnerability test case execution plan;
- and
- executing the determined vulnerability test case execution plan to determine code vulnerabilities, wherein executing the determined vulnerability test case execution plan to determine code vulnerabilities comprises using quantum entanglement to ensure evaluation results of one set of test cases are reconciled with other test results to flag any discrepancy.
One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:
- receive data related to code vulnerabilities;
- generate a quantum graph network using a quantum graph categorizer and the received data related to code vulnerabilities, wherein the quantum graph network is a dynamic quantum-graph network supported by a quantum deep embedding neural network (QDENN) and a graphical neural artificial intelligence engine;
- determine a reconciliation ratio, wherein the reconciliation ratio is a ratio of vulnerability scoring between static and dynamic vulnerability analysis;
- based on the reconciliation ratio, determine a vulnerability test case execution plan;
- and
- execute the determined vulnerability test case execution plan to determine code vulnerabilities, wherein executing the determined vulnerability test case execution plan to determine code vulnerabilities comprises using quantum entanglement to ensure evaluation results of one set of test cases are reconciled with other test results to flag any discrepancy.
Dependencies are inferred from the claim text.