Various embodiments are directed to a password security warning system. An artificial neural network or other types of models may be used to determine whether a password that is created, input, or proposed by a user via an interface includes one or more predictable or typical transformations or combinations of characters derived from user-specific information. Based on the determination, a warning may be provided to the user.

Various embodiments are directed to a password security warning system. An artificial neural network or other types of models may be used to determine whether a password that is created, input, or proposed by a user via an interface includes one or more predictable or typical transformations or combinations of characters derived from user-specific information. Based on the determination, a warning may be provided to the user.

Various embodiments are directed to securely generating and managing passwords using a near-field communication (NFC) enabled contactless smart card. For example, a secure password may be generated by generating a random number via a random number generator of the contactless smart card and converting the random number to one or more human-readable characters. In another example, a secure cryptographic hash function of the contactless smart card may generate a hash output value, which may be converted to one or more human-readable characters. The human-readable characters may be used as the secure password or it may be transformed to add more layers of security and complexity.

Various embodiments are directed to securely generating and managing passwords using a near-field communication (NFC) enabled contactless smart card. For example, a secure password may be generated by generating a random number via a random number generator of the contactless smart card and converting the random number to one or more human-readable characters. In another example, a secure cryptographic hash function of the contactless smart card may generate a hash output value, which may be converted to one or more human-readable characters. The human-readable characters may be used as the secure password or it may be transformed to add more layers of security and complexity.

Methods and apparatus for protecting trace data of a remote debug session for a computing system. In one embodiment, a method includes storing trace data received from one or more trace interfaces to a storage location of a target device, where the trace data is generated from execution at the target device, and where the trace data is protected from an unauthorized access. The method continues with transmitting the trace data to a debug host computer with encryption through a communication channel between the target device and the debug host computer.

Provided are a computer system, a method and a program for monitoring an IoT device that improve the security. The computer system that monitors a connected IoT device 100 monitors the login state of the IoT device 100, detects unauthorized access based on the result of the monitoring, learns at least one of an ID and a password for the unauthorized access, judges whether at least one of an ID and a password that are previously stored for the IoT device 100 are easily decrypted by access to the IoT device, and controls the access to an IoT device for the judgment in a predetermined priority order.

A computer system is provided. The computer system includes a memory and at least one processor coupled to the memory and configured to detect a request for a sign-up form from a client device to a remote server. The at least one processor is further configured to generate a code module based on the detection. The code module is configured to request a credential vulnerability check from an application management server. The at least one processor is further configured to provide the code module to the client device for execution on the client device in response to an attempted submission of the sign-up form. The at least one processor is further configured to receive a result of the credential vulnerability check from the client device and perform a security action in response to the credential vulnerability check indicating vulnerable credentials.

A specialized in-memory database health check process is utilized to resolve dependencies in a resource indicating requirements for an instance of an in-memory database. Specifically, when an instance of an in-memory database is created in response to a request, a list of one or more component handlers are obtained. These component handlers are modular functions, separate from each other but potentially dependent on one or more other component handlers, and act to validate various requirements listed in a resource for the request. Each of the component handlers are executed individually during execution of a Reconcile function. To the extent that the execution of any component handlers in the list is unsuccessful, the Reconcile function is rerun for another iteration. These iterations continue until all component handlers report back as successful. Instance creation is then considered successful and the instance of the in-memory database can be utilized by users.

A specialized in-memory database health check process is utilized to resolve dependencies in a resource indicating requirements for an instance of an in-memory database. Specifically, when an instance of an in-memory database is created in response to a request, a list of one or more component handlers are obtained. These component handlers are modular functions, separate from each other but potentially dependent on one or more other component handlers, and act to validate various requirements listed in a resource for the request. Each of the component handlers are executed individually during execution of a Reconcile function. To the extent that the execution of any component handlers in the list is unsuccessful, the Reconcile function is rerun for another iteration. These iterations continue until all component handlers report back as successful. Instance creation is then considered successful and the instance of the in-memory database can be utilized by users.

Disclosed herein are methods, systems, processes, and machine learning paradigms to implement a password semantic analysis pipeline. A password semantic analysis pipeline model is trained according to one or more machine learning techniques to at least (a) determine, based on given characteristics data of a given network environment, whether each of several tokens that are chunked portions of a data structure input as a password in an application is a known syntax type or a recognized entity, (b) generate, using the password semantic analysis pipeline model, a password strength score that is a combination of a confidence score determined for each of the plurality of tokens and a weight factor assigned to the known syntax type or the recognized entity, (c) apply the password strength score to the data structure input as the password in the application, and (d) provide an output to the application indicating whether the data structure input as the password is acceptable or unacceptable for continued access to the application.