Abstract: Determining, by a machine learning model in an isolated operating environment, whether a file is safe for processing by a primary operating environment. The file is provided, when the determining indicates the file is safe for processing, to the primary operating environment for processing by the primary operating environment. When the determining indicates the file is unsafe for processing, the file is prevented from being processed by the primary operating environment. The isolated operating environment can be maintained on an isolated computing system remote from a primary computing system maintaining the primary operating system. The isolating computing system and the primary operating system can communicate over a cloud network.

Abstract: Data is received that includes at least a portion of a program. Thereafter, entry point locations and execution-relevant metadata of the program are identified and retrieved. Regions of code within the program are then identified using static disassembly and based on the identified entry point locations and metadata. In addition, entry points are determined for each of a plurality of functions. Thereafter, a set of possible call sequences are generated for each function based on the identified regions of code and the determined entry points for each of the plurality of functions. Related apparatus, systems, techniques and articles are also described.

Abstract: Executable memory space is protected by receiving, from a process, a request to configure a portion of memory with a memory protection attribute that allows the process to perform at least one memory operation on the portion of the memory. Thereafter, the request is responded to with a grant, configuring the portion of memory with a different memory protection attribute than the requested memory protection attribute. The different memory protection attribute restricting the at least one memory operation from being performed by the process on the portion of the memory. In addition, it is detected when the process attempts, in accordance with the grant, the at least one memory operation at the configured portion of memory. Related systems and articles of manufacture, including computer program products, are also disclosed.

Abstract: Under one aspect, a computer-implemented method includes receiving a query at a query interface about whether a computer file comprises malicious code. It is determined, using at least one machine learning sub model corresponding to a type of the computer file, whether the computer file comprises malicious code. Data characterizing the determination are provided to the query interface. Generating the sub model includes receiving computer files at a collection interface. Multiple sub populations of the computer files are generated based on respective types of the computer files, and random training and testing sets are generated from each of the sub populations. At least one sub model for each random training set is generated.

Abstract: Transaction terminal malicious software is detected by monitoring calls of a first process to identify attempts by the first process to read memory used by a second process. The first and second processes are different from each other and are executed by at least one data processor forming part of a transaction terminal system having at least one transaction terminal. Thereafter, it is determined that the memory used by the second process comprises patterns indicative of sensitive financial or identification information. In response, at least one corrective action is initiated to prevent use of the financial or identification information. Related apparatus, systems, techniques and articles are also described.

Abstract: A nested file having a primary file and at least one secondary file embedded therein is parsed using at least one parser of a cell. The cell assigns a maliciousness score to each of the parsed primary file and each of the parsed at least one secondary file. Thereafter, the cell generates an overall maliciousness score for the nested file that indicates a level of confidence that the nested file contains malicious content. The overall maliciousness score is provided to a data consumer indicating whether to proceed with consuming the data contained within the nested file.

Abstract: An agent inserts one or more hooks into a sub-execution runtime environment that is configured to include a script and/or targeted to include the script. The agent including the one or more hooks monitors a behavior of the sub-execution runtime environment and/or the script. The agent subsequently obtains context information regarding the sub-execution runtime environment and/or the script so that it can control the runtime of at least the sub-execution runtime environment. Related systems, methods, and articles of manufacture are also disclosed.

Abstract: Determining, by a machine learning model in an isolated operating environment, whether a file is safe for processing by a primary operating environment. The file is provided, when the determining indicates the file is safe for processing, to the primary operating environment for processing by the primary operating environment. When the determining indicates the file is unsafe for processing, the file is prevented from being processed by the primary operating environment. The isolated operating environment can be maintained on an isolated computing system remote from a primary computing system maintaining the primary operating system. The isolating computing system and the primary operating system can communicate over a cloud network.

Abstract: Transaction terminal malicious software is detected by monitoring calls of a first process to identify attempts by the first process to read memory used by a second process. The first and second processes are different from each other and are executed by at least one data processor forming part of a transaction terminal system having at least one transaction terminal. Thereafter, it is determined that the memory used by the second process comprises patterns indicative of sensitive financial or identification information. In response, at least one corrective action is initiated to prevent use of the financial or identification information. Related apparatus, systems, techniques and articles are also described.

Abstract: As part of an analysis of the likelihood that a given input (e.g. a file, etc.) includes malicious code, a convolutional neural network can be used to review a sequence of chunks into which an input is divided to assess how best to navigate through the input and to classify parts of the input in a most optimal manner. At least some of the sequence of chunks can be further examined using a recurrent neural network in series with the convolutional neural network to determine how to progress through the sequence of chunks. A state of the at least some of the chunks examined using the recurrent neural network summarized to form an output indicative of the likelihood that the input includes malicious code. Methods, systems, and articles of manufacture are also described.

Abstract: As part of an analysis of the likelihood that a given input (e.g. a file, etc.) includes malicious code, a convolutional neural network can be used to review a sequence of chunks into which an input is divided to assess how best to navigate through the input and to classify parts of the input in a most optimal manner. At least some of the sequence of chunks can be further examined using a recurrent neural network in series with the convolutional neural network to determine how to progress through the sequence of chunks. A state of the at least some of the chunks examined using the recurrent neural network summarized to form an output indicative of the likelihood that the input includes malicious code. Methods, systems, and articles of manufacture are also described.

Abstract: In one respect, there is provided a system for training a neural network adapted for classifying one or more scripts. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one memory provides operations including: receiving a disassembled binary file that includes a plurality of instructions; processing the disassembled binary file with a convolutional neural network configured to detect a presence of one or more sequences of instructions amongst the plurality of instructions and determine a classification for the disassembled binary file based at least in part on the presence of the one or more sequences of instructions; and providing, as an output, the classification of the disassembled binary file. Related computer-implemented methods are also disclosed.

Abstract: A plurality of data files is received. Thereafter, each file is represented as an entropy time series that reflects an amount of entropy across locations in code for such file. A wavelet transform is applied, for each file, to the corresponding entropy time series to generate an energy spectrum characterizing, for the file, an amount of entropic energy at multiple scales of code resolution. It can then be determined, for each file, whether or not the file is likely to be malicious based on the energy spectrum. Related apparatus, systems, techniques and articles are also described.

Abstract: Using a recurrent neural network (RNN) that has been trained to a satisfactory level of performance, highly discriminative features can be extracted by running a sample through the RNN, and then extracting a final hidden state hi, where i is the number of instructions of the sample. This resulting feature vector may then be concatenated with the other hand-engineered features, and a larger classifier may then be trained on hand-engineered as well as automatically determined features. Related apparatus, systems, techniques and articles are also described.

Abstract: Using a recurrent neural network (RNN) that has been trained to a satisfactory level of performance, highly discriminative features can be extracted by running a sample through the RNN, and then extracting a final hidden state hi, where i is the number of instructions of the sample. This resulting feature vector may then be concatenated with the other hand-engineered features, and a larger classifier may then be trained on hand-engineered as well as automatically determined features. Related apparatus, systems, techniques and articles are also described.

Abstract: The present disclosure involves systems and computer-implemented methods for installing software hooks. One process includes identifying a target method and a hook code, where the hook code is to execute instead of at least a portion of the target method, and wherein the target method and the hook code are executed within a managed code environment. A compiled version of the target method and a compiled version of the hook code are located in memory, where the compiled versions of the target method and the hook code are compiled in native code. Then, the compiled version of the target method is modified to direct execution of at least a portion of the compiled version of the target method to the compiled version of the hook code. The non-compiled version of the target method may be originally stored as bytecode. The managed code environment may comprise a managed .NET environment.

Abstract: Using a recurrent neural network (RNN) that has been trained to a satisfactory level of performance, highly discriminative features can be extracted by running a sample through the RNN, and then extracting a final hidden state hi, where i is the number of instructions of the sample. This resulting feature vector may then be concatenated with the other hand-engineered features, and a larger classifier may then be trained on hand-engineered as well as automatically determined features. Related apparatus, systems, techniques and articles are also described.

Abstract: A plurality of data files is received. Thereafter, each file is represented as an entropy time series that reflects an amount of entropy across locations in code for such file. A wavelet transform is applied, for each file, to the corresponding entropy time series to generate an energy spectrum characterizing, for the file, an amount of entropic energy at multiple scales of code resolution. It can then be determined, for each file, whether or not the file is likely to be malicious based on the energy spectrum. Related apparatus, systems, techniques and articles are also described.

Abstract: A plurality of data files is received. Thereafter, each file is represented as an entropy time series that reflects an amount of entropy across locations in code for such file. A wavelet transform is applied, for each file, to the corresponding entropy time series to generate an energy spectrum characterizing, for the file, an amount of entropic energy at multiple scales of code resolution. It can then be determined, for each file, whether or not the file is likely to be malicious based on the energy spectrum. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that includes at least a portion of a program. Thereafter, entry point locations and execution-relevant metadata of the program are identified and retrieved. Regions of code within the program are then identified using static disassembly and based on the identified entry point locations and metadata. In addition, entry points are determined for each of a plurality of functions. Thereafter, a set of possible call sequences are generated for each function based on the identified regions of code and the determined entry points for each of the plurality of functions. Related apparatus, systems, techniques and articles are also described.