Methods, systems, and computer readable medium for verifying interactions with digital components. The method includes receiving input indicating interaction associated with a digital component that is provided by a content provider and presented by a user interface of the user device, determining, by a trusted program of the user device, that the interaction is valid, generating, by the trusted program and based on the determination that the interaction is valid based on a validity evaluation, a digitally signed token that attests the validity of the interaction with the digital component, and providing, by the trusted program and to a third party, the digitally signed token as an indication of the validity of the interaction.

A method for training a machine learning model using information pertaining to characteristics of upload activity performed at one or more client devices includes generating first training input including (i) information identifying first amounts of data uploaded during a specified time interval for one or more of multiple application categories, and (ii) information identifying first locations external to a client device to which the first amounts of data are uploaded. The method includes generating a first target output that indicates whether the first amounts of data uploaded to the first locations correspond to malicious or non-malicious upload activity. The method includes providing the training data to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output.

Aspects of the disclosure are directed to a system for classifying software as malicious or benign based on predicting the effect the software has on the platform before the software is actually deployed. A system as described herein can operate in close to real-time to receive, isolate, and classify software as benign or malicious. Aspects of the disclosure provide for accurate classification of malicious programs or scripts even if ostensibly the program appears benign, and vice versa, based on the effect predicted by a machine learning model trained as described herein. The system can also be implemented to isolate and verify incoming scripts or software to the platform, to provide a predicted classification while not substantially impacting processing pipelines involving platform resources or the user experience with the platform in general.

Apparatus, systems, methods for measuring a side-channel is disclosed. The methods involve obtaining a first measurement of a magnetic field in a first range from the side-channel of the at least one electronic device; generating a version of the side-channel; obtaining a second measurement of the magnetic field in a second range from the version of the side-channel; and generating a composite measurement of the magnetic field from the side-channel of the at least one electronic device based on the first measurement and the second measurement. The first range includes a minimum threshold and at least a portion of the second range is less than the minimum threshold of the first range.

A method, apparatus and computer program product for automated security policy synthesis and use in a container environment. In this approach, a binary analysis of a program associated with a container image is carried out within a binary analysis platform. During the binary analysis, the program is micro-executed directly inside the analysis platform to generate a graph that summarizes the program's expected interactions within the run-time container environment. The expected interactions are identified by analysis of one or more system calls and their arguments found during micro-executing the program. Once the graph is created, a security policy is then automatically synthesized from the graph and instantiated into the container environment. The policy embeds at least one system call argument. During run-time monitoring of an event sequence associated with the program executing in the container environment, an action is taken when the event sequence is determined to violate the security policy.

One disclosed method involves determining, by at least one computing system and based at least in part on input provided to a meeting application, at least a first topic of interest for a first user accessing the meeting application via a first client device, in response to determining the first topic of interest, querying, by the at least one computing system, at least one data source, external to the meeting application, for information corresponding to the first topic of interest, and causing, by the at least one computing system, the first client device to display a representation of the information.

Disclosed herein are systems and method for preventing zero-day attacks. A method may include receiving a first report including information about an execution of a first script of an application that modifies a file on a first computing device, and receiving a second report including an indication that the file includes malicious code. In response to determining that an identifier of the file is present in both the first report and the second report, the method may include generating and transmitting, to the first computing device, a first rule that prevents execution of any script that shares at least one operation of the first script. The method may include, in response to determining that a vulnerability detected by the first rule is not present in a vulnerability database, generating an entry in the vulnerability database for the vulnerability as a zero-day vulnerability and transmitting an alert to the application developer.

Provided are a cyber threat information processing apparatus, a method thereof, and a storage medium storing a cyber threat information processing program. It is possible to provide a cybersecurity threat information processing method including disassembling an input executable file to obtain disassembled code, and reconstructing the disassembled code to obtain reconstructed disassembled code, into a hash function, and converting the hash function into N-gram data (N being a natural number), and performing ensemble machine learning on block-unit code of the converted N-gram data to profile the block-unit code by an identifier of an attack technique performed by the block-unit code and an identifier of an attacker generating the block-unit code. It is possible to detect and address a variant of malware, and identify malware, an attack technique, an attacker, and an attack prediction method within a significantly short time even for a variant of malware.

Detection circuitry for an integrated circuit (IC) includes voltage divider circuitry, comparison circuitry, and calibration circuitry. The voltage divider circuitry receives a power supply signal and output a first reference voltage signal and a supply voltage signal based on the power supply signal. The comparison circuitry compares the first reference voltage signal and the supply voltage signal to generate an output signal. The calibration circuitry alters one or more parameters of the voltage divider circuitry to increase a voltage value of the supply voltage signal based on the comparison of the first reference voltage signal with the supply voltage signal.

A vehicular control apparatus is used in an onboard system provided with a plurality of information processors mutually connected via a communication bus, and includes a storage section for storing information, and an arithmetic section for executing a process based on the information stored in the storage section. The information contains first management information relating to a security abnormality as a communication data abnormality owing to security attack from outside the onboard system, and second management information relating to a safety abnormality as a communication data abnormality owing to an abnormality in the onboard system. The first management information contains first limit condition information indicating a first limit condition for executing a security coping with the security abnormality. The second management information contains second limit condition information indicating a second limit condition for executing a safety coping with the safety abnormality. Upon detection of the communication data abnormality in the onboard system, the arithmetic section determines a coping content to the detected communication data abnormality based on the first management information and the second management information.