An artefact is received. Features are extracted from this artefact which are, in turn, used to populate a vector. The vector is then input into a classification model to generate a score. The score is then modified using a step function so that the true score is not obfuscated. Thereafter, the modified score can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.
Systems, methods, and devices are described herein for detecting abnormalities within a system based on signal fingerprinting. A plurality of electrical signals are concurrently received from a transceiver over a time period. The time period is partitioned into a plurality of sampling windows. An electrical signal of the plurality of electrical signals is sequentially selected. For the sequentially selected electrical signal, a temporal snapshot of said electrical signal is iteratively captured over a sampling window of the plurality of sampling windows. This iterative capturing is repeated for remaining sampling windows of the plurality of sampling windows. Each captured temporal snapshot is temporally concatenated over the time period according to its respective temporal position of the time period to generate the signal fingerprint.
An artefact is received and parsed into a plurality of observations. A first subset of the observations are inputted into a machine learning model trained using historical data to classify the artefact. In addition, a second subset of the observations are inputted into a xenospace centroid configured to classify the artefact. Thereafter, the artefact is classified based on a combination of an output of the machine learning model and an output of xenospace centroid. Related apparatus, systems, techniques and articles are also described.
Data is received as part of an authentication procedure to identify a user. Such data characterizes a user-generated biometric sequence that is generated by the user interacting with at least one input device according to a desired biometric sequence. Thereafter, using the received data and at least one machine learning model trained using empirically derived historical data generated by a plurality of user-generated biometric sequences (e.g., historical user-generated biometric sequences according to the desired biometric sequence, etc.), the user is authenticated if an output of the at least one machine learning model is above a threshold. Data can be provided that characterizes the authenticating. Related apparatus, systems, techniques and articles are also described.
Methods are described herein for communications bus data transmission using relative ground shifting. A plurality of voltage lines of at least one electronic control unit (ECU) are monitored. The at least one ECU electrically coupled to a communications bus. A voltage differential across at least two of the plurality of voltage lines of the at least one ECU is measured. A pulse or data stream is injected into the communications bus via one or two voltage lines based on the measured voltage differential having an amplitude lower than a predetermined voltage threshold.
Each of a plurality of endpoint computer systems monitors data relating to a plurality of events occurring within an operating environment of the corresponding endpoint computer system. The monitoring can include receiving and/or inferring the data using one or more sensors executing on the endpoint computer systems Thereafter, for each endpoint computer system, artifacts used in connection with the events are stored in a vault maintained on such endpoint computer system. A query is later received by at least a subset of the plurality of endpoint computer systems from a server. Such endpoint computer systems, in response, identify and retrieve artifacts within the corresponding vaults response to the query. Results responsive to the query including or characterizing the identified artifacts is then provided by the endpoint computer systems receiving the query to the server.
An endpoint computer system monitors data relating to a plurality of events occurring within an operating environment of the endpoint computer system. The monitoring can include receiving and/or inferring the data using one or more sensors executing on the endpoint computer system. The endpoint computer system can store artifacts used in connection with the plurality of events in a vault maintained on such endpoint computer system. The endpoint computer system, in response to a trigger, identifies and retrieves metadata characterizing artifacts associated with the trigger from the vault. Such identified and retrieved metadata is then provided by the endpoint computer system to a remote server.
Systems are provided herein for a hardware protection framework. A security module monitors a plurality of voltage lines of at least one electronic control unit (ECU) electrically coupled to a communications bus. A voltage differential across at least two of the plurality of voltage lines of the at least one ECU is measured. The voltage differential is compared to a plurality of predetermined signal fingerprints associated with the at least one ECU. A variance in the compared voltage differential is identified relative to one or more of the plurality of predetermined signal fingerprints. Data characterizing the identified variance is provided. In some aspects, a pulse or a data stream is injected based on the voltage differential having an amplitude lower than a predetermined voltage threshold.
Methods are provided herein for communications bus signal fingerprinting. A security module monitors a plurality of voltage lines of at least one electronic control unit (ECU) electrically coupled to a communications bus. A voltage differential across at least two of the plurality of voltage lines of the at least one ECU is measured. The voltage differential is compared to a plurality of predetermined signal fingerprints associated with the at least one ECU. A variance in the compared voltage differential is identified relative to one or more of the plurality of predetermined signal fingerprints. Data characterizing the identified variance is provided.
Contextual information associated with a file is provided to at least enable a classification of the file when a malware classifier is unable to classify the file. In response to the providing of the contextual information, the classification of the file is received. Based at least on the received classification of the file, the malware classifier is updated to enable the malware classifier to classify the file.
A plurality of events associated with each of a plurality of computing nodes that form part of a network topology are monitored. The network topology includes antivirus tools to detect malicious software prior to it accessing one of the computing nodes. Thereafter, it is determined that, using at least one machine learning model, at least one of the events is indicative of malicious activity that has circumvented or bypassed the antivirus tools. Data is then provided that characterizes the determination. Related apparatus, systems, techniques and articles are also described.
Systems are provided to classify an instruction sequence with a machine learning model. An instruction sequence is processed with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and to determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens. The classification of the instruction sequence can then be provided as an output. Related methods and articles of manufacture, including computer program products, are also provided.
Systems and methods are provided for computer user authentication using machine learning. Authentication for a user is initiated based on an identification confidence score of the user. The identification confidence score is based on one or more characteristics of the user. Using a machine learning model for the user, user activity of the user is monitored for anomalous activity to generate first data. Based on the monitoring, differences between the first data and historical utilization data for the user determine whether the user's utilization of the one or more resources is anomalous. When the user's utilization of the one or more resource is anomalous, the user's access to the one or more resource is removed.
Systems method are provided for training and utilizing a machine learning model to detect malicious container files. A container file is processed with a trained machine learning model that is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious. Based on such a determination, an indication can be provided indicating whether the container file includes one or more files rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also provided.
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.
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.
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/54 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by adding security routines or objects to programs
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.
A mismatch between model-based classifications produced by a first version of a machine learning threat discernment model and a second version of a machine learning threat discernment model for a file is detected. The mismatch is analyzed to determine appropriate handling for the file, and taking an action based on the analyzing. The analyzing includes comparing a human-generated classification status for a file, a first model version status that reflects classification by the first version of the machine learning threat discernment model, and a second model version status that reflects classification by the second version of the machine learning threat discernment model. The analyzing can also include allowing the human-generated classification status to dominate when it is available.
H04L 29/06 - Communication control; Communication processing characterised by a protocol
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/51 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems at application loading time, e.g. accepting, rejecting, starting or inhibiting executable software based on integrity or source reliability
19.
MACHINE LEARNING MODEL FOR MALWARE DYNAMIC ANALYSIS
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.
In one aspect, a computer-implemented method is disclosed. The computer-implemented method may include determining a sketch matrix that approximates a matrix representative of a reference dataset. The reference dataset may include at least one computer program having a predetermined classification. A reduced dimension representation of the reference dataset may be generated based at least on the sketch matrix. The reduced dimension representation may have fewer features than the reference dataset. A target computer program may be classified based on the reduced dimension representation. The target computer program may be classified to determine whether the target computer program is malicious. Related systems and articles of manufacture, including computer program products, are also disclosed.
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.
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
H04L 29/06 - Communication control; Communication processing characterised by a protocol
22.
RETENTION AND ACCESSIBILITY OF DATA CHARACTERIZING EVENTS ON AN ENDPOINT COMPUTER
An endpoint computer system can harvest data relating to a plurality of events occurring within an operating environment of the endpoint computer system and can add the harvested data to a local data store maintained on the endpoint computer system. A query response can be generated, for example by identifying and retrieving responsive data from the local data store. The responsive data are related to an artifact on the endpoint computer system and/or to an event of the plurality of events. In some examples, the local data store can be an audit log and/or can include one or more tamper resistant features. Systems, methods, and computer program products are described.
In one aspect there is provided a method. The method may include: determining that an executable implements a sub-execution environment, the sub-execution environment being configured to receive an input, and the input triggering at least one event at the sub-execution environment; intercepting the event at the sub-execution environment; and applying a security policy to the intercepted event, the applying of the policy comprises blocking the event, when the event is determined to be a prohibited event. Systems and articles of manufacture, including computer program products, are also provided.
G06F 21/50 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
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.
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.
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.
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.
G06F 21/51 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems at application loading time, e.g. accepting, rejecting, starting or inhibiting executable software based on integrity or source reliability
28.
GENERATION OF API CALL GRAPHS FROM STATIC DISASSEMBLY
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.
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.
A sample of data is placed within a directed graph that comprises a plurality of hierarchical nodes that form a queue of work items for a particular worker class that are used to process the sample of data. Subsequently, work items are scheduled within the queue for each of a plurality of workers by traversing the nodes of the directed graph. The work items are then served to the workers according to the queue. Results can later be received from the workers for the work items (the nodes of the directed graph are traversed based on the received results). In addition, in some variations, the results can be classified so that one or models can be generated. Related systems, methods, and computer program products are also described.
patents
