Abstract: Methods, apparatus, systems, and articles of manufacture for detecting anomalous activity of an IoT device are disclosed. An example apparatus includes a communications aggregator to aggregate communications from a device communicating via a communications interface, a statistical property extractor to extract statistical properties of the aggregated communications, an image generator to generate an image based on the extracted statistical properties, a persona identifier to identify a persona associated with the device, and a machine learning model trainer to train a machine learning model using the generated image and the persona.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for classification of unknown samples using agglomerative clustering.

Abstract: Methods, systems, articles of manufacture and apparatus are disclosed to reduce spoofing vulnerabilities. An example apparatus to verify token integrity for a computing device includes an artifact engine to store first artifact data with a first timestamp in a device artifact storage, and store second artifact data with a second timestamp in the device artifact storage. The example apparatus includes an offchain identification protector (OIP) controller to generate a first token based on the first artifact data and the first timestamp, the first token to establish a first entry in a blockchain at a first time, and generate a second token based on the second artifact data, the second timestamp, and the first token, the second token to establish a second entry in the blockchain at a second time after the first time.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for classification of unknown samples using agglomerative clustering.

Abstract: Methods, apparatus, systems, and articles of manufacture for detecting anomalous activity of an IoT device are disclosed. An example apparatus includes a communications aggregator to aggregate communications from a device communicating via a communications interface, a statistical property extractor to extract statistical properties of the aggregated communications, an image generator to generate an image based on the extracted statistical properties, a persona identifier to identify a persona associated with the device, and a machine learning model trainer to train a machine learning model using the generated image and the persona.

Abstract: Methods, systems, and media for determining application permissions are provided. In some embodiments, the method comprises: receiving a description of an application to be installed on a user device and a group of permissions required by the application; identifying a subset of words in the description of the application; determining an expected group of permissions based on the subset of words; comparing the group of permissions required by the application and the expected group of permissions; determining a privacy score associated with the application based on the comparison of the group of permissions required by the application and the expected group of permissions; and causing the application to be installed on the user device based on the privacy score associated with the application.

Abstract: Methods, systems, articles of manufacture and apparatus are disclosed to reduce spoofing vulnerabilities. An example apparatus to verify token integrity for a computing device includes an artifact engine to store first artifact data with a first timestamp in a device artifact storage, and store second artifact data with a second timestamp in the device artifact storage. The example apparatus includes an offchain identification protector (OIP) controller to generate a first token based on the first artifact data and the first timestamp, the first token to establish a first entry in a blockchain at a first time, and generate a second token based on the second artifact data, the second timestamp, and the first token, the second token to establish a second entry in the blockchain at a second time after the first time.