Abstract: Described are techniques to enable computers to efficiently determine if they should run a program based on an immediate (i.e., real-time, etc.) analysis of the program. Such an approach leverages highly trained ensemble machine learning algorithms to create a real-time discernment on a combination of static and dynamic features collected from the program, the computer's current environment, and external factors. Related apparatus, systems, techniques and articles 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: 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: Described are techniques to enable computers to efficiently determine if they should run a program based on an immediate (i.e., real-time, etc.) analysis of the program. Such an approach leverages highly trained ensemble machine learning algorithms to create a real-time discernment on a combination of static and dynamic features collected from the program, the computer's current environment, and external factors. Related apparatus, systems, techniques and articles are also described.

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: 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: In some implementations there may be provided a system. The system may include a processor and a memory. The memory may include program code which causes operations when executed by the processor. The operations may include analyzing a series of events contained in received data. The series of events may include events that occur during the execution of a data object. The series of events may be analyzed to at least extract, from the series of events, subsequences of events. A machine learning model may determine a classification for the received data. The machine learning model may classify the received data based at least on whether the subsequences of events are malicious. The classification indicative of whether the received data is malicious may be provided. Related methods and articles of manufacture, including computer program products, 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: Data is received or accessed that includes a structured file encapsulating data required by an execution environment to manage executable code wrapped within the structured file. Thereafter, code and data regions are iteratively identified in the structured file. Such identification is analyzed so that at least one feature can be extracted from the structured file. Related apparatus, systems, techniques and articles are also described.

Abstract: Identifying shellcode in a sequence of instructions by identifying a first instruction, the first instruction identifying a first bound of a sequence of instructions, identifying a second instruction, the second instruction identifying a second bound of the sequence of instructions, and generating a distribution for the sequence of instructions, bounded by the first instruction and the second instructions, the distribution indicative of whether the sequence of instructions is likely to include shellcode.

Abstract: Data is received or accessed that includes a structured file encapsulating data required by an execution environment to manage executable code wrapped within the structured file. Thereafter, code and data regions are iteratively identified in the structured file. Such identification is analyzed so that at least one feature can be extracted from the structured file. 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: 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 hh 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: Systems, methods, and articles of manufacture, including computer program products, are provided for classification systems and methods using modeling. In some example embodiments, there is provided a system that includes at least one processor and at least one memory including program code which when executed by the at least one memory provides operations. The operations can include generating a representation of a sequence of sections of a file and/or determining, from a model including conditional probabilities, a probability for each transition between at least two sequential sections in the representation. The operations can further include classifying the file based on the probabilities for each transition.

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: In one respect, there is provided a system for training a neural network adapted for classifying one or more instruction sequences. 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 processor provides operations including: training, based at least on training data, a machine learning model to detect one or more predetermined interdependencies amongst a plurality of tokens in the training data; and providing the trained machine learning model to enable classification of one or more instruction sequences. Related methods and articles of manufacture, including computer program products, are also provided.

Abstract: In one respect, there is provided a system for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

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.