Abstract: According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that, when executed by the processor, may cause the processor to determine a plurality of queues for storing event data. The processor may partition the event data into a plurality of logical partitions and group the logical partitions into a plurality of logical partition ranges. Each logical partition range may be assigned to a respective queue of the plurality of queues. The processor may determine a first logical partition range mapped to a first queue, compute a first set of metrics for the first queue, and determine a first load of the first queue based on the first set of metrics. The processor may then adjust the first logical partition range by modifying a number of logical partitions mapped to the first queue based on the first load as determined.

Abstract: A method, system, and computer program product for anomaly detection using embedding space representation of system states. An anomaly detection model is trained using an anomaly detection algorithm and a plurality of reference vectors obtained using an embedding space representation process configured for mapping to a single point vector in an embedding space each of a plurality of system state snapshots comprised in a training dataset and each capturing during a defined time window a plurality of features of each process operating in the system. Responsive to receiving a testing dataset comprising one or more system state snapshots, one or more vectors in the embedding space are obtained using the embedding space representation process for mapping each system state snapshot in the testing dataset, and the anomaly detection model is used to determine whether a vector of the one or more vectors being indicative of a cyber-attack on the system.

Abstract: This disclosure provides a filtering mechanism to manage anomalous security data items. An anomalous security data item is provided to an analysis engine (such as a Large Language Model (LLM) or another form of generative language model) for interpretation. By curating a selection of one or more relevant non-anomalous security data items to provide with the anomalous data item, the filtering mechanism enables the analysis engine to perform with increased accuracy, without requiring the analyst engine to process large numbers of data items to ascertain their relevance to the anomalous security data item.

Abstract: A computer-implemented method, computer device and computer program are provided for training a Machine Learning model based on a plurality of signal records, each signal record comprising an entity identifier, a signal identifier and a timestamp. Signal records related to an entity can be input into the trained Machine Learning model. The trained Machine Learning model can be used to determine if an entity is malicious.

Abstract: Techniques are described herein that are capable of using weighted peer groups to selectively trigger a security alert. A determination is made that an entity performs an operation. The entity has peers that are categorized among peer groups. For each peer group, an extent to which the peers in the peer group perform the operation is determined. Weights are assigned to the respective peer groups. For each peer group, the extent to which the peers in the peer group perform the operation and the weight that is assigned to the peer group are combined to provide a respective weighted group value. A risk score, which is based at least in part on the weighted group values of the peer groups, is assigned to the operation. The security alert regarding the operation is selectively triggered based at least in part on the risk score.

Abstract: According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that, when executed by the processor, may cause the processor to receive event data to be placed in a plurality of queues. The processor may determine logical partitions for the received event data and may store the event data in the plurality of queues based on the determined logical partitions. A first queue of the plurality of queues may be mapped to a first logical partition range and a second queue of the plurality of queues may be mapped to a second logical partition range. Based on respective loads at the plurality of queues, the processor may update a mapping of the determined logical partitions to the plurality of queues by transferring logical partitions between the first queue and the second queue.

Abstract: A computer-implemented method, computer device and computer program are provided for training a Machine Learning model based on a plurality of signal records, each signal record comprising an entity identifier, a signal identifier and a timestamp. Signal records related to an entity can be input into the trained Machine Learning model. The trained Machine Learning model can be used to determine if an entity is malicious.

Abstract: Compositions are provided which include at last one Dead Sea extract and at least one polysaccharide, wherein the repeating unit of said polysaccharide is a tetrasaccharide consisting of two D-glucose residues, one L-fucose residue and one D-glucuronic acid residue. Formulations include the compositions which are used as skin protectants against pollution.

Abstract: Techniques for detecting anomalies or cyber attacks on a vehicle. A computer-implemented method for anomaly or attack detection includes determining, using a first model, a first predicted value of a first variable message associated with a vehicle, determining, using a second model, a second predicted value of the first variable message associated with the vehicle, determining, based on a difference between an actual value of the first variable message and the first predicted value of the first variable message and on a difference between the actual value of the first variable message and the second predicted value of the first variable message, a vector, and determining, using a third model, an output value based on the vector, the output value corresponding to at least one of a likelihood that an anomaly or an attack is occurring or a type of the anomaly or the attack.

Abstract: A method, system, and computer program product for anomaly detection using embedding space representation of system states. An anomaly detection model is trained using an anomaly detection algorithm and a plurality of reference vectors obtained using an embedding space representation process configured for mapping to a single point vector in an embedding space each of a plurality of system state snapshots comprised in a training dataset and each capturing during a defined time window a plurality of features of each process operating in the system. Responsive to receiving a testing dataset comprising one or more system state snapshots, one or more vectors in the embedding space are obtained using the embedding space representation process for mapping each system state snapshot in the testing dataset, and the anomaly detection model is used to determine whether a vector of the one or more vectors being indicative of a cyber-attack on the system.

Abstract: According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that, when executed by the processor, may cause the processor to receive event data to be placed in a plurality of queues. The processor may determine logical partitions for the received event data and may store the event data in the plurality of queues based on the determined logical partitions. A first queue of the plurality of queues may be mapped to a first logical partition range and a second queue of the plurality of queues may be mapped to a second logical partition range. Based on respective loads at the plurality of queues, the processor may update a mapping of the determined logical partitions to the plurality of queues by transferring logical partitions between the first queue and the second queue.

Abstract: The present disclosure provides compositions including at least one Dead Sea extract, ?-carotene and Niacinamide. The present disclosure further provides formulations including the compositions, their uses and methods utilizing same.

Abstract: An access system for a vehicle may include at least one antenna configured to receive access signals for authorization to gain access to the vehicle, and a controller configured to receive motion data from a key fob associated with the vehicle, the motion data indicative of a route of a user associated with the key fob, classify the motion data as one of an open route and a closed route, and restrict access to the vehicle in response to the motion data being classified as an open route.

Abstract: This present invention discloses compositions comprising Dead Sea extract in combination with one or more plant extracts, uses thereof and methods of preventing and/or reducing and/or inhibiting the glycation of one or more biomolecules and/or preventing and/or reducing and/or inhibiting the formation of Advanced Glycation End Products.

Abstract: Techniques for detecting anomalies or cyber attacks on a vehicle. A computer-implemented method for anomaly or attack detection includes determining, using a first model, a first predicted value of a first variable message associated with a vehicle, determining, using a second model, a second predicted value of the first variable message associated with the vehicle, determining, based on a difference between an actual value of the first variable message and the first predicted value of the first variable message and on a difference between the actual value of the first variable message and the second predicted value of the first variable message, a vector, and determining, using a third model, an output value based on the vector, the output value corresponding to at least one of a likelihood that an anomaly or an attack is occurring or a type of the anomaly or the attack.

Abstract: This present invention discloses compositions comprising Dead Sea extract in combination with one or more plant extracts, uses thereof and methods of preventing and/or reducing and/or inhibiting the glycation of one or more biomolecules and/or preventing and/or reducing and/or inhibiting the formation of Advanced Glycation End Products.

Abstract: Provided are nonwoven fabric materials including Dead Sea minerals which exhibit useful aesthetic and physical performance during the time of utilization in a consumer product. Further provided are processes for the preparation of the material and to uses thereof.

Abstract: Techniques are described herein that are capable of using weighted peer groups to selectively trigger a security alert. A determination is made that an entity performs an operation. The entity has peers that are categorized among peer groups. For each peer group, an extent to which the peers in the peer group perform the operation is determined. Weights are assigned to the respective peer groups. For each peer group, the extent to which the peers in the peer group perform the operation and the weight that is assigned to the peer group are combined to provide a respective weighted group value. A risk score, which is based at least in part on the weighted group values of the peer groups, is assigned to the operation. The security alert regarding the operation is selectively triggered based at least in part on the risk score.

Abstract: Provided are compositions including at least one Dead Sea extract and at least one extract of the Apple of Sodom (Calotropis procera). Further provided are formulations including the compositions and their uses.

Abstract: Provided are nanoparticles including at least one Dead Sea Extract, as an active ingredient, and at least one amphipathic material. Further provided are skin-care compositions and/or pharmaceutical compositions including the nanoparticles. Cosmetic and/or therapeutic methods utilizing the nanoparticles are disclosed as well.