Abstract: Embodiments disclosed include methods and apparatus for detecting a reputation of infrastructure associated with potentially malicious content. In some embodiments, an apparatus includes a memory and a processor. The processor is configured to identify an Internet Protocol (IP) address associated with potentially malicious content and define each row of a matrix by applying a different subnet mask from a plurality of subnet masks to a binary representation of the IP address to define that row of the matrix. The processor is further configured to provide the matrix as an input to a machine learning model, and receive, from the machine learning model, a score associated with a maliciousness of the IP address.

Abstract: A housing that is intended to spin on a fishing lure shank and designed to hold blades. The housing can be reusable by incorporating mechanical means that enable the user to swap out blades of different styles, colors, or shapes. There is a hollow tube that runs the long distance of the housing that could be of different cross-sectional shape than the fishing lure shank it is spinning on.

Abstract: A compute instance may be configured to extract a feature of a data instance accessed by the compute instance, generate an anonymized feature value for the feature of the data instance, include the anonymized feature value in a feature vector corresponding to the data instance, and transmit the feature vector to a server-based computing system.

Abstract: A system and method for rigging and lifting a joined, multi-section assembly of corrugated decking sections for attachment to a Cold-Form Steel. The system and method provide for the utilization of an adaptable rig that securely attaches to a multi-section assembly of corrugated decking sections which have be pre-joined by workers in a safe, preferably terrestrial, workspace. The adaptability of the rig enables it to provide attachment points for lifting and supporting the multi-sectioned decking assemblies of disparate sizes and materials. The location and number of these attachment points being a function of the size, gauge, area and aggregate weight of a given pre-joined multi-section assembly. The system and method are can be utilized with pre-joined multi-section assemblies consisting of individual decking sections fabricated from a variety of materials, including ferrous metals, non-ferrous metals, as well as composite materials.

Abstract: In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second subscore and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.

Abstract: A microelectronic chip device includes a semiconductor substrate and multiple on-chip strain sensors (OCSSs) constructed on the substrate at various locations of the substrate. The OCSSs may each include multiple piezoresistive devices configured to sense a strain at a location of the various locations and produce a strain signal representing the strain at that location. A strain measurement circuit may also be constructed on the semiconductor substrate and configured to measure strain parameters from the strain signals produced by the OCSSs. The strain parameters represent the strains at the various location. Values of the strain parameters can be used for analysis of mechanical stress on the chip device.

Abstract: Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.

Abstract: Systems and methods for using single or multi-chain deposit tokens are disclosed. According to an embodiment, a method for deposit tokenization may include: (1) receiving, by a deposit tokenization service for a token issuer and from an authorized party, an instruction for tokenizing an amount of non-tokenized funds in a deposit account; (2) verifying, by the deposit tokenization service, an identity of the authorized party using a verifiable credential; (3) screening, by the deposit tokenization service and using an information oracle, the deposit account and/or the verifiable credential; (4) debiting, by the deposit tokenization service, the deposit account for the amount and crediting the amount to an omnibus account; (5) tokenizing, by the deposit tokenization service, the amount of the non-tokenized funds on a blockchain network as deposit tokens; and (6) crediting, by the deposit tokenization service, a wallet address on the blockchain network with the deposit tokens.

Abstract: An apparatus for detecting malicious files includes a memory and a processor communicatively coupled to the memory. The processor receives multiple potentially malicious files. A first potentially malicious file has a first file format, and a second potentially malicious file has a second file format different than the first file format. The processor extracts a first set of strings from the first potentially malicious file, and extracts a second set of strings from the second potentially malicious file. First and second feature vectors are defined based on lengths of each string from the associated set of strings. The processor provides the first feature vector as an input to a machine learning model to produce a maliciousness classification of the first potentially malicious file, and provides the second feature vector as an input to the machine learning model to produce a maliciousness classification of the second potentially malicious file.

Abstract: In some embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a structured file for which a machine learning model has made a malicious content classification. The code further includes code to remove a portion of the structured file to define a modified structured file that follows a format associated with a type of the structured file. The code further includes code to extract a set of features from the modified structured file. The code further includes code to provide the set of features as an input to the machine learning model to produce an output. The code further includes code to identify an impact of the portion of the structured file on the malicious content classification of the structured file based on the output.

Abstract: This invention relates to a synthetic method for the preparation of Compound 1 and precursors thereof. Compound 1 is prepared via reaction of isoxazole 2 with phenylether (R)-3-ONa.

Abstract: An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.

Abstract: A method of assembling a pallet using a machine includes folding a first portion of a pallet support column to define a vertical support structure. The method further includes folding a second portion of the pallet support column to engage the vertical support structure. The method further includes gluing at least a portion of the second portion to the first portion. The method further includes coupling the pallet support column to at least one of a bottom member or a top member of the pallet.

Abstract: A foldably constructed pallet includes a top member forming a top surface of the foldably constructed pallet and a bottom member forming a bottom surface of the foldably constructed pallet. The bottom member includes, extending from the bottom surface, a plurality of outer sidewalls, and a plurality of internal sidewalls. An outer sidewall of the plurality of outer sidewalls and an internal sidewall of the plurality of internal sidewalls cooperate to define a cradle configured to hold a structural insert such that folded panels of the structural insert may be engaged with each of the outer sidewall and the internal sidewall. The top member may be connected to the bottom member and covering the cradle.

Abstract: In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second sub score and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.

Abstract: Systems and methods are disclosed for monitoring models for bias. In one implementation, a system for automatically assessing a deployed model for selection of a cohort may include a processing device programmed to: apply the deployed model to data representing a first plurality of individuals, the data including at least one characteristic of the first plurality of individuals; based on the application, select a subset of the first plurality of individuals as a cohort; receive data representing a second plurality of individuals labeled as within the cohort, the data including the at least one characteristic of the second plurality of individuals; compare the selected subset and the second plurality of individuals along the at least one characteristic; and determine whether the comparison results in a difference between the selected subset and the second plurality of individuals greater than a threshold.

Abstract: A potentially malicious command including a plurality of features is received. Additionally, a plurality of nodes included in a decision tree are traversed, based on the plurality of features, to identify a leaf node included in the plurality of nodes. The leaf node is associated with (1) a first set of similar commands, each similar command from the first set of similar commands including the plurality of features, and (2) a second set of similar commands from the first set of similar commands and that were previously detected. Additionally, a probability that the potentially malicious command will be escalated as potentially malicious is determined based on the first set of similar commands and the second set of similar commands. Additionally, a first indication quantifying the first set of similar commands, a second indication quantifying the second set of similar commands, and the probability are caused to be displayed.

Abstract: Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.

Abstract: Due to the threat of virus attacks and ransom ware, an apparatus and methods for protecting backup storage devices from malicious software virus attacks is explored. An independent backup storage system is connected to a primary storage server over an undiscoverable communications line. The backup storage system is a read-only backup storage system most of the time buffering the backup storage system from a virus or attack on the primary storage server. The backup storage system changes from a read-only backup storage system to a read/write backup storage system only during a backup window of time where data is backed up to the backup storage system. A snapshot of the backup data is maintained in the backup storage system and can be made available at numerous points of time in the past if the data primary storage server becomes corrupted.

Abstract: An apparatus for detecting malicious files includes a memory and a processor communicatively coupled to the memory. The processor receives multiple potentially malicious files. A first potentially malicious file has a first file format, and a second potentially malicious file has a second file format different than the first file format. The processor extracts a first set of strings from the first potentially malicious file, and extracts a second set of strings from the second potentially malicious file. First and second feature vectors are defined based on lengths of each string from the associated set of strings. The processor provides the first feature vector as an input to a machine learning model to produce a maliciousness classification of the first potentially malicious file, and provides the second feature vector as an input to the machine learning model to produce a maliciousness classification of the second potentially malicious file.