Abstract: Disclosed herein are systems and method for connectivity-aware scheduling. A method may receive connection information that indicates interconnections between a plurality of nodes in a cluster, and may generate a connectivity matrix based on the connection information. The method may identify a first computing unit and a second computing unit associated with an application, schedule the first computing unit onto a first node of the cluster, and subsequent to scheduling the first computing unit onto the first node, schedule the second computing unit onto a second node of the cluster instead of a third node of the cluster in response to determining that the first computing unit and the second computing unit belong to a same application and that the first node is connected to the second node according to the connectivity matrix.

Abstract: Disclosed herein are systems and method for extrapolating from crawled data to generate classifications. In one aspect, a method may receive an evaluation request with an input list comprising at least one entity and a respective website identifier of the at least one entity, identify, from a plurality of categorization labels, a subset of categorization labels that correspond to a requesting entity that generated the evaluation request, crawl information using the website identifier, generate at least one text body from the crawled information, apply a machine learning algorithm on the at least one text body, wherein the machine learning algorithm is configured to generate an output vector indicating categorization labels from the subset of categorization labels that the at least one entity corresponds to, based on terms in the at least one text body; and transmit the output vector to a computing device of the requesting entity.

Abstract: Disclosed herein are systems and method for generating file systems of data sources incompatible with anti-virus scanners. In one exemplary aspect, the method includes: receiving, from an AV scanner, a request to scan a data source for malicious activity, wherein the data source includes a plurality of files, and wherein the AV scanner has a plurality of compatible file types that the AV scanner is capable of scanning; determining that the plurality of files are inaccessible to the AV scanner; generating a file system corresponding to the data source by parsing contents of the data source; generating a virtual volume including a plurality of sparse files corresponding to the plurality of files in the data source; populating at least one sparse file in the virtual volume with respective parsed content of a corresponding file in the data source; and instructing the AV scanner to scan the virtual volume.

Abstract: Systems and methods for conducting a synchronized student-lecturer session on an e-learning server in a computer network. The method includes obtaining a lesson material template from the lecturer computing system; logging a lecturer and one or more students in the e-learning application by verifying respective credentials to access the lesson material template, presenting the lesson material template on a session screen of the e-learning application corresponding to the lecturer and the one or more student users; generating a lecturer session; generating a student session log; tracking the lecturer session log, wherein the lecturer activity as recorded in the lecturer session log is monitored by one or more student users, and tracking the student session log, wherein the student activity as recorded in the student session log is monitored by the lecturer user.

Abstract: Systems and methods for creating a training script on a virtual laboratory instance running in an e-learning infrastructure. The method comprises deploying a virtual laboratory instance from a template on e-learning infrastructure. The virtual laboratory instance includes preconfigured training software and an action interception tool. The method further comprises intercepting system events at the action interception tool corresponding to training software operation, pre-processing intercepted events by filtering and grouping events into an action-centric dataset, generating a training script using a machine learning model, based on an action-centric dataset, in a human-readable format, and storing the training script in a structured text-based format file.

Abstract: Systems and methods for conducting an active assessment of students based on the training material uploaded by a lecturer. A training database obtains the training material which is classified based on a content type by the data classifier. According to the content type, a content to text constructor applies different techniques to convert the content into text. The text is analyzed by the text analyzer. The text analyzer includes a semantic analyzer to perform semantic analysis on the text, a question generator to generate a set of questions based on the semantic analysis and select most relevant questions from the set, and an answer generator module to generate at least one correct answer and multiple incorrect answers for each question. The test script is generated including the questions and answers. Based on the selection of the answer, an assessment of the student is performed.

Abstract: Systems and methods for detecting a phishing attack in an email message. The method includes intercepting and evaluating an email, and generating a reputation score based on the evaluation of the email message. If the malicious component is detected, the email is blocked, if not, a trust score is generated. A determination is made whether the email is generated by AI, based on LLM. A certainty score is generated which is indicative of intentions and context of the email message being malicious. When the certainty score is higher than a threshold, a combined score by combining the reputation score, the trust score, and the certainty score. When the combined reputation score is higher than a threshold, the email is blocked, and if lower, a summary is generated by a summary AI engine. Based on a comparison with known malware summaries, the email is flagged or blocked.

Abstract: Systems and methods for simplified software backup. Generative artificial intelligence (AI) based on a large language model (LLM) is utilized to determine a backup restore operation for a backup request for a target system using a metadata tracked during a previous backup of the target system, and execute the backup restore operation to satisfy the backup request.

Abstract: Systems and methods for firmware protection of industrial control systems. A kernel-level agent operating at a kernel mode intercepts a request to the resource, collects data associated with the intercepted request, and sends the collected data to a security service. A security service receives the collected data, analyzes the collected data to determine a verdict, and sends the verdict to the kernel-level agent. The kernel-level agent then executes a security action for the resource based on the verdict.

Abstract: Systems and methods for the protection of information systems utilize business impact analysis (BIA) data to assess the risk of security mitigation operations. A detected security incident is enriched using BIA data. A proposed mitigation action and a risk of implementing the proposed mitigation action are determined using the enriched data so that an administrator user can understand the impact or risk to the business for the proposed mitigation action.

Abstract: Systems and methods for detecting a malware injection interested processes. The method includes identifying one or more trusted processes, monitoring at least one thread associated with the trusted processes using at least one control point, detecting activity at the at least one thread based on the at least one control point and determining a timestamp of the detected activity, receiving from the trusted processes at least one execution stack corresponding to the timestamp and indicating the at least one control point used to monitor the at least one thread, applying a first malware detector to the at least one execution stack to generate a first verdict, collecting the first verdict and auxiliary information corresponding to the trusted processes at the given timestamp, and applying a second malware detector to the first verdict and the auxiliary information to generate a second verdict.

Abstract: Systems and methods for secure deduplication of encrypted content. A system generally includes a client, a key server, a fingerprint index, and a storage service. The client can perform chunking of a file, hashing of chunks to generate tags, and encryption of chunks using a salted key from the key server. The fingerprint index checks for duplicate ciphertexts using the tags. The storage service saves non-duplicate ciphertext.

Abstract: A system and method for validating the consistency of a computer backup mounts the backup as a virtual machine on a hypervisor host. A screenshot of the virtual machine's boot screen is recorded and sent to a machine-learning service for verification of boot status. The resulting boot status as successful or failed is recorded in metadata associated with that computer backup.

Abstract: Disclosed herein are systems and methods for generating an intuitive network management user interface (UI). A method includes: receiving network information of at least one client network and at least one cloud network, wherein the network information includes a network identifier of each device within a given network, a status of the given network, network policies associated with the given network; determining a plurality of connections between the at least one client network and the at least one cloud network based on the network information; generating, for display, the network management UI including a three-column graphical structure wherein a first column includes first visualized information of the at least one client network, a second column includes second visualized information of the plurality of connections, and a third column includes third visualized information of the at least one cloud network.

Abstract: A system for automatic recognition of security incidents includes a processor coupled to a memory storing instructions, the processor being configured to implement the instructions for an automatic incident generator (AIG) with at least one type of events related to the system, and access to a repository of information about previously recorded incidents with the events related to these previously recorded incidents, to monitor a plurality of events, identify sequences of events including suspected signatures that are capable of constituting an incident, calculate a degree of variance (DoV) of the suspected signatures and at least one signature related to a previously recorded incident, compare the DoV to at least one threshold and, if the DoV is less (or less or equal) to the threshold, identify the incident and optionally initiate the workflow related to the identified incident.

Abstract: Disclosed herein are systems and method for determining environment dimensions based on environment pose. In one aspect, the method may include training, with a dataset including a plurality of images featuring an environment and labelled landmarks in the environment, a neural network to identify a pose of an environment. The method may comprise receiving an input image depicting the environment, generating an input tensor based on the input image, and inputting the input tensor into the neural network, which may be configured to generate an output tensor including a position of each identified landmark, a confidence level associated with each position, and a pose confidence score. The method may include calculating a homography matrix between each position in the output tensor along a camera plane and a corresponding position in an environment plane in order to output an image that visually connects each landmark along the environment plane.

Abstract: Systems and methods for augmenting a training dataset. The method includes gaining access to at least one insufficient training dataset for training a neural network NN. A generative convolutional neural network GCNN is trained using the training set or a subset thereof. At least one additional item is generated by the GCNN trained on the existing training set, and the generated item is added to the original training set.

Abstract: Systems and methods for automatic generation of highlights of video. The system includes a video processor to select one type of the video to be analyzed and split video clips from the video. The video processor recognizes positive clips, negative clips, and auxiliary clips. A spatio-temporal encoder is configured to select, from the recognized clips, a main positive clip, a main negative clip, and auxiliary positive and negative clips, and generate a three-dimensional (3D) embedding vector of each clip. The selected clips are processed by a ranking network having a self-attention layer. The self-attention layer, using a query head, a key head and the value head produces self-attention resultant vector on which an activation function is performed. A rank value is thus obtained for the selected clip. Based on the rank value, video highlights are generated.

Abstract: Systems and methods for automatically identifying outliers in Machine Learning training datasets. The method includes gaining access to a training set for the neural network NN. For each element of the training dataset, an embedding vector is generated, which is a numeric representation of the corresponding element. A centroid of all the embedding vectors of all the elements of the training set is computed equal to an average of all the embedding vectors of all the elements of the training set. A dissimilarity score is generated for each element of the training set by calculating a distance between the embedding vector corresponding to the element and the centroid. The method further includes identifying the elements from the training set with embedding vectors having the dissimilarity score higher than or equal to a predetermined threshold value.

Abstract: Systems and methods for automatically enhancing the quality of images in the training set of a neural network NN. A method includes gaining access to a training set including a plurality of images. Using at least one image quality assessment method, at least one image is identified from a plurality of images in the training set, which matches a low-quality criterion as at least one low-quality image. At least one image enhancement method is used for enhancing the at least one low-quality image to obtain at least one enhanced image. The at least one low-quality image is replaced with the corresponding at least one enhanced image in the training set.