Abstract: Arrangements for dynamic generation of interactive mapping interfaces are provided. Transaction data may be received by a computing platform and analyzed to extract user and location information for each transaction. The computing platform may generate, based on the extracted user and location data, an interactive mapping interface providing a visual display of transactions within a geographic area. The interactive mapping interface may include a plurality of interactive icons that identify a physical location of a transaction. The interactive mapping interface may be transmitted to a user computing device for display by a display of the user computing device. The computing platform may receive user input associated with user interaction with one of the interactive icons and may generate and transmit, to the user computing device for display, an additional user interface that may include transaction details and a selectable option to report the respective transaction as fraudulent.

Abstract: Arrangements for dynamic generation of interactive fraud mapping interfaces are provided. In some examples, fraud reporting data may be received by a computing platform. The fraud reporting data may include incidents of potentially fraudulent activity reported by a plurality of users. If at least a threshold volume of fraud reporting data is received, the fraud reporting data may be analyzed using a machine learning engine. For instance, the fraud reporting data may be input to the machine learning engine to output one or more compromised or potentially compromised payment terminals, vendor or retail locations, or the like. In some examples, the computing platform may generate an interactive fraud mapping interface that includes an interactive icon associated with each compromised location of the one or more compromised locations. The interactive fraud mapping interface may be transmitted or sent to a user computing device for display.

Abstract: Arrangements for haptic feedback control are provided. In some aspects, registration data may be received from a user. For instance, a user may identify one or more devices (e.g., payment devices such as credit or debit cards, mobile devices, or the like) associated with the user. In some examples, the user may also select customized haptic feedback associated with different feedback outputs. After receiving registration data, the computing platform may receive a request to process a transaction. The transaction may be initiated at a point-of-sale system of an entity via the payment device or card of the user. Transaction details may be analyzed to determine or identify a feedback output (e.g., approved, denied, request additional information or the like). Based on the identified feedback output, a feedback output command or instruction may be generated and transmitted to the payment device for execution.

Abstract: Arrangements for detecting unauthorized activity based on input method analysis and monitoring are provided. In some aspects, identity information associated with a user may be received and be stored. An input may be received from a computing device of the user. An input pattern of the received input may be determined. Using a machine learning model, the input pattern of the received input may be compared to input patterns of humans and input patterns of machines. Based on the comparison, it may be determined whether the user is a human user or a non-human user. Responsive to determining that the user is a non-human user, a request may be transmitted to the user to provide increased authentication credentials. Responsive to determining that the user is a human user, an identity of the user may be verified by comparing the input pattern of the received input to the stored identity information.

Abstract: Arrangements for comprehensive threat mitigation are provided. In some aspects, an indication of threat or potential threat may be received from an external entity. In some examples, the threat may include a cybersecurity threat. In response to receiving the indication of threat, identifying data associated with the external entity may be extracted from the indication and used to retrieve pre-stored customizations associated with the desired mitigating actions of the external entity. The one or more mitigating actions may be identified and instructions to execute the one or more mitigating actions may be generated and transmitted to one or more computing devices for execution.

Abstract: Arrangements for comprehensive threat mitigation are provided. In some aspects, an indication of threat or potential threat may be received from an external entity. In some examples, the threat may include a cybersecurity threat. In response to receiving the indication of threat, identifying data associated with the external entity may be extracted from the indication and used to retrieve pre-stored customizations associated with the desired mitigating actions of the external entity. The one or more mitigating actions may be identified and instructions to execute the one or more mitigating actions may be generated and transmitted to one or more computing devices for execution.

Abstract: In a public setting, an audio/visual (A/V) obfuscation device can be used to provide privacy during a conversation occurring either in-person or via an electronic device. The A/V obfuscation device is configured to provide one or both of an electronic or physical barrier to ensure privacy for at least part of the conversation. The A/V obfuscation device provides audio masking and/or an optical block to impede third-parties from overhearing or observing the conversation. The A/V obfuscation device includes devices capable of capturing audio and/or video information of conversation participants and/or electronic devices. The captured audio and/or video information may be analyzed and processed to generate obfuscation audio and/or video signals to obscure at least a portion of aspects of the conversation.

Abstract: Arrangements for haptic feedback control are provided. In some aspects, registration data may be received from a user. For instance, a user may identify one or more devices (e.g., payment devices such as credit or debit cards, mobile devices, or the like) associated with the user. In some examples, the user may also select customized haptic feedback associated with different feedback outputs. After receiving registration data, the computing platform may receive a request to process a transaction. The transaction may be initiated at a point-of-sale system of an entity via the payment device or card of the user. Transaction details may be analyzed to determine or identify a feedback output (e.g., approved, denied, request additional information or the like). Based on the identified feedback output, a feedback output command or instruction may be generated and transmitted to the payment device for execution.

Abstract: Arrangements for comprehensive threat mitigation are provided. In some aspects, an indication of threat or potential threat may be received from an external entity. In some examples, the threat may include a cybersecurity threat. In response to receiving the indication of threat, identifying data associated with the external entity may be extracted from the indication and used to retrieve pre-stored customizations associated with the desired mitigating actions of the external entity. The one or more mitigating actions may be identified and instructions to execute the one or more mitigating actions may be generated and transmitted to one or more computing devices for execution.

Abstract: An information security system that includes an information security engine configured to detect an attack by a malicious software element in a network. The information security engine is further configured to transfer the malicious software element from the network to the emulated network in response to detecting an attack. The information security engine is further configured to select defense strategies for restricting communications using different port configurations and to implement each defense strategy within the emulated network. The information security engine is further configured to execute a duplicate of the malicious software element in the emulated network and to determine a performance level for each of the defense strategies against the duplicate of the malicious software element. The information security engine is further configured to select a defense strategy with a highest performance level and to implement the selected defense strategy within the network.

Abstract: An information security system that includes an information security engine configured to monitor data transmissions within a network and to detect a first attack by a malicious software element. The information security engine is further configured to detect a second attack by the malicious software element within a predetermined time interval from the first attack and to transfer the malicious software element from the network to an emulated network in response to detecting the second attack. The information security engine is further configured to generate an attack log comprising behavior characteristics for attacks performed by the malicious software element in the emulated network and to train a machine learning model based on behavior characteristics from the attack log.

Abstract: An information security system that includes an information security engine configured to detect an attack by a malicious software element in a network. The information security engine is further configured to transfer the malicious software element from the network to the emulated network in response to detecting an attack. The information security engine is further configured to select defense strategies for restricting communications using different port configurations and to implement each defense strategy within the emulated network. The information security engine is further configured to execute a duplicate of the malicious software element in the emulated network and to determine a performance level for each of the defense strategies against the duplicate of the malicious software element. The information security engine is further configured to select a defense strategy with a highest performance level and to implement the selected defense strategy within the network.

Abstract: An information security system that includes an information security engine configured to monitor data transmissions within a network and to detect a first attack by a malicious software element. The information security engine is further configured to detect a second attack by the malicious software element within a predetermined time interval from the first attack and to transfer the malicious software element from the network to an emulated network in response to detecting the second attack. The information security engine is further configured to generate an attack log comprising behavior characteristics for attacks performed by the malicious software element in the emulated network and to train a machine learning model based on behavior characteristics from the attack log.