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公开(公告)号：US11729213B2
公开(公告)日：2023-08-15
申请号：US17062903
申请日：2020-10-05
Applicant: SAP SE
Inventor： Cedric Hebert , Merve Sahin , Anderson Santana de Oliveira , Rocio Cabrera Lozoya , Aicha Mhedhbi
IPC: H04L9/40 , G06F9/54 , H04L67/133
CPC classification number: H04L63/1491 , G06F9/547 , H04L63/1416 , H04L67/133
Abstract: Systems, methods, and computer media for securing software applications are provided herein. Using deceptive endpoints, attacks directed to API endpoints can be detected, and attackers can be monitored or blocked. Deceptive endpoints can be automatically generated by modifying valid endpoints for an application. Deceptive endpoints are not valid endpoints for the application, so if a deceptive endpoint is accessed, it is an indication of an attack. When a deceptive endpoint is deployed, accessing the deceptive endpoint can cause an alert to be generated, and an account, user, or device associated with accessing the deceptive endpoint can be blocked or monitored.
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公开(公告)号：US20230252114A1
公开(公告)日：2023-08-10
申请号：US17668208
申请日：2022-02-09
Applicant: SAP SE
Inventor： Rocio Cabrera Lozoya , Slim Trabelsi , Carlos Rafael Ocanto Davila
IPC: G06F21/31 , G06F40/253 , G06F40/263 , G06F40/40
CPC classification number: G06F21/31 , G06F40/253 , G06F40/263 , G06F40/40
Abstract: In an example embodiment, an efficient, automated method to generate password guesses is provided by leveraging online text sources along with natural language processing techniques. Specifically, semantic structures in passwords are exploited to aid system in generating better guesses. This not only helps cover instances where traditional password meters would indicate a password is safe when it is not, but also makes the solution robust against fast-evolving domains such as new slang in natural languages or new vocabulary arising from new products, product updates, and services.
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公开(公告)号：US12001530B2
公开(公告)日：2024-06-04
申请号：US17668208
申请日：2022-02-09
Applicant: SAP SE
Inventor： Rocio Cabrera Lozoya , Slim Trabelsi , Carlos Rafael Ocanto Davila
IPC: G06F21/00 , G06F21/31 , G06F40/253 , G06F40/263 , G06F40/40
CPC classification number: G06F21/31 , G06F40/253 , G06F40/263 , G06F40/40
Abstract: In an example embodiment, an efficient, automated method to generate password guesses is provided by leveraging online text sources along with natural language processing techniques. Specifically, semantic structures in passwords are exploited to aid system in generating better guesses. This not only helps cover instances where traditional password meters would indicate a password is safe when it is not, but also makes the solution robust against fast-evolving domains such as new slang in natural languages or new vocabulary arising from new products, product updates, and services.
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公开(公告)号：US20230418599A1
公开(公告)日：2023-12-28
申请号：US17850380
申请日：2022-06-27
Applicant: SAP SE
Inventor： Rocio Cabrera Lozoya , Antonino Sabetta , Michele Bezzi
IPC: G06F8/77
CPC classification number: G06F8/77
Abstract: Systems and methods are provided for training a machine learning model to generate a score indicating a level of discrepancy between a commit message and a corresponding code change. The computing system receives a commit comprising a given commit message and a given corresponding code change and analyzes, using the trained machine learning model, the given commit message and given corresponding code change to generate a score indicating the level of discrepancy between the given commit message and the given corresponding code change of the received commit.
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Patent Agency Ranking
公开(公告)号：US20240111522A1
公开(公告)日：2024-04-04
申请号：US17955786
申请日：2022-09-29
Applicant: SAP SE
Inventor： Niccolo Togni , Antonino Sabetta , Rocio Cabrera Lozoya
Abstract: Systems and methods are provided for analyzing a commit comprising an updated version of software code against a previous version of software code to determine a plurality of methods in the commit that have been changed, identifying a previous version and an updated version for each method that has been changed, and generating graphical representations of each previous version and each updated version of each method that has been changed. The systems and methods further provide for extracting path contexts from each graphical representation for each previous version and each updated version of each method, determining path contexts that are different by comparing each path context for each previous version with an associated updated version of each method, and encoding each path context that is different to generate at least one commit vector representation of the commit.
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公开(公告)号：US20220109692A1
公开(公告)日：2022-04-07
申请号：US17062903
申请日：2020-10-05
Applicant: SAP SE
Inventor： Cedric Hebert , Merve Sahin , Anderson Santana de Oliveira , Rocio Cabrera Lozoya , Aicha Mhedhbi
Abstract: Systems, methods, and computer media for securing software applications are provided herein. Using deceptive endpoints, attacks directed to API endpoints can be detected, and attackers can be monitored or blocked. Deceptive endpoints can be automatically generated by modifying valid endpoints for an application. Deceptive endpoints are not valid endpoints for the application, so if a deceptive endpoint is accessed, it is an indication of an attack. When a deceptive endpoint is deployed, accessing the deceptive endpoint can cause an alert to be generated, and an account, user, or device associated with accessing the deceptive endpoint can be blocked or monitored.
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公开(公告)号：US20240289427A1
公开(公告)日：2024-08-29
申请号：US18659463
申请日：2024-05-09
Applicant: SAP SE
Inventor： Rocio Cabrera Lozoya , Slim Trabelsi , Carlos Rafael Ocanto Davila
IPC: G06F21/31 , G06F40/253 , G06F40/263 , G06F40/40
CPC classification number: G06F21/31 , G06F40/253 , G06F40/263 , G06F40/40
Abstract: In an example embodiment, an efficient, automated method to generate password guesses is provided by leveraging online text sources along with natural language processing techniques. Specifically, semantic structures in passwords are exploited to aid system in generating better guesses. This not only helps cover instances where traditional password meters would indicate a password is safe when it is not, but also makes the solution robust against fast-evolving domains such as new slang in natural languages or new vocabulary arising from new products, product updates, and services.
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公开(公告)号：US11972258B2
公开(公告)日：2024-04-30
申请号：US17850380
申请日：2022-06-27
Applicant: SAP SE
Inventor： Rocio Cabrera Lozoya , Antonino Sabetta , Michele Bezzi
IPC: G06F8/77
CPC classification number: G06F8/77
Abstract: Systems and methods are provided for training a machine learning model to generate a score indicating a level of discrepancy between a commit message and a corresponding code change. The computing system receives a commit comprising a given commit message and a given corresponding code change and analyzes, using the trained machine learning model, the given commit message and given corresponding code change to generate a score indicating the level of discrepancy between the given commit message and the given corresponding code change of the received commit.
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公开(公告)号：US20220129261A1
公开(公告)日：2022-04-28
申请号：US17080520
申请日：2020-10-26
Applicant: SAP SE
Inventor： Rocio Cabrera Lozoya , Antonino Sabetta , Michele Bezzi , Arnaud Baumann
Abstract: Distributed vector representations of source code commits, are generated to become part of a data corpus for machine learning (ML) for analyzing source code. The code commit is received, and time information is referenced to split the source code into pre-change source code and post-change source code. The pre-change source code is converted into a first code representation (e.g., based on a graph model), and the post-change source code into a second code representation. A first particle is generated from the first code representation, and a second particle is generated from the second code representation. The first particle and the second particle are compared to create a delta. The delta is transformed into a first commit vector by referencing an embedding matrix to numerically encode the first particle and the second particle. Following classification, the commit vector is stored in a data corpus for performing ML analysis upon source code.
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