Acronis International Gmbh

Constructor Technology Ag

Constructor Education and Research Genossenschaft

Chainstack Pte. Ltd.

Serg Bell

Stanislav Protasov

Vladimir Strogov

Nickolay Berko

Aliaksei Dodz

Candid Wuest

Sergey Ulasen

Philipp Gysel

Nikolay Dobrovolskiy

Ilya Shimchik

Alexander Tormasov

Laurent Dedenis

Ivan Klimov

Artem Shapiro

Vasyl Shandyba

Alexander Andreev

Maksim Filipenko

Ruslan Mustafin

Aleksandr Buival

Andrey Kulaga

Ilya Baimetov

Hiten Gajjar

Prabhuraj Reddy

Kurt Natvig

Evgeny A Aseev

Nikolay Grebennikov

Andrey Adaschik

Alexander Snorkin

Ivan Kirillov

Anton Kivich

Denis Parkhomenko

Andrei Boiarov

Denis Kopyrin

Kseniia Alekseitseva

Evgenii Kabantsev

Ivan Krestinin

Anton Enakiev

Mikhail Rybakov

Aleksandr Brilliantov

Vasily Semyonov

Irina Lukasheva

German Bartenev

Vladimir Miroshkin

Brad Smith

Maxim Cherey

Dmitry Chepurovskiy

Dmitry Ledenyov

Yuri Per

Anton Zaslavskii

Andrey Kuleshov

Marcel De Korte

Stanislav Protsaov

Oleg Ishanov

Alexey Rybak

Evgeny Aseev

Alexey Kostyushko

Igor Milosevic

Dmitriy Obukhov

Artem Ushanov

Ilia Iuskevich

Dinil Divakaran

A system and method for regulating a motorsport racing event are disclosed. The system comprises sensor modules for sensing a plurality of race parameters to generate a plurality of input signals. A data analysis module receives the input signals for analysis. A decision-making module receives the analyzed signal and computes a recommendation or a decision corresponding to the level of violation. A penalty and recommendation module receives information associated with the recommendation or the decision and presents the recommendation or the decision.

Automatic automotive race management

Systems and methods for protecting computing systems against ransomware attacks using AI-generated virtual file honeypots. Generative AI comprising a large language model generates virtual file honeypots automatically in response to attack vectors associated with suspect actors and ransomware families.

AI-generated virtual file honeypots for computing systems behavior-based protection against ransomware attacks

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.

Firmware protection for industrial control systems

Systems and methods for verifying a production system automatically by testing a mirror copy of the production system on a testing computer. The system includes a mirror update transporter to deliver a mirror update from the production system to the mirror system, a mounting module to apply the mirror update to the mirror system, a testing computer on which the mirror system is running, a testing module to automatically execute a set of tests on the mirror system, and a communication module to communicate the results of the tests.

System and method for evaluating active backups using penetration testing

The present disclosure relates to a system to evaluate an avatar generated by an avatar generator. The system comprises an evaluation module including an audio evaluation module for evaluating audio features and a video evaluation module for evaluating video features. Evaluation of the avatar includes extracting audio and video features from the avatar and applying a set of evaluation metrics for generating audio and video evaluation scores. The scores are combined to generate a final score. For avatar generator evaluation, audio clip and video clip are provided to the audio evaluation module and video evaluation module, respectively. A set of evaluation metrics is applied for evaluation. Each metric can generate a score. All scores are combined to generate a final evaluation score.

System and method for an audio-visual avatar evaluation

A system to optimize required resources at an endpoint needed to monitor a user behavior for abnormalities with the endpoint includes a processor processing a plurality of agents running at the endpoint to intercept network traffic metrics, intercept device access metrics, intercept app-specific user-mode metrics, parse intercepted data, and submit the intercepted data to a backend component at a server to collect the intercepted data from the endpoint, predict deviation from a normal profile, in which the backend component assesses available characteristics of a particular endpoint, calculates an endpoint user profile, calculates a degree of variance (DoV) between the user profile and the normal profile, compares the calculated DoV to a predetermined Variance Threshold (VT), and predicts, based on machine learning algorithms, a movement of a trend of the DoV within the VT, creates an adjusted metrics list, and distributes adjusted metrics to a related endpoint.

User behavior anomaly detection-sensors

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.

System and method for automatic video summarization

Systems and methods for embedding into a storage stack of a UNIX based computing device. A method includes building a file storage map for a file stored in computing device data storage, intercepting a system call associated with the file, intercepting a storage stack operation, and analyzing the system call and the storage stack operation using the file storage map to determine a change to a data block of the file. The method further includes building a shadow copy of the file on backup storage. The method further includes detecting a malicious operation based on the change to the data block.

Shadow backup and anti-malware protection for UNIX-based machines

Systems and methods for ransomware protection in advanced injection-based attacks. Events from a driver are analyzed to identify a potentially malicious actor. A confidence level is calculated for the potentially malicious actor identification to weigh the probability that the actor is malware or a source of malware. Behavior associated with the potentially malicious actor can be collected, if it is likely that the potentially malicious actor is associated with malware, such as if the confidence level is over a predetermined threshold. Subsequently, one or more virtual honeypots are generated for the suspicious processes and the response to the virtual honeypots is included in anti-ransomware heuristic analysis.

Virtual file honey pots for computing systems behavior-based protection against ransomware attacks

Systems and methods for ransomware protection in advanced injection-based attacks. The call stack(s) of injected threads are analyzed and a preliminary verdict of benign or malicious can be determined. Additional sensors collect data to associate injected threads with other activities or actors to further estimate the injected thread being benign or malicious. If the threat level is high enough, such as over a given threshold, the preliminary verdict can be determined to be malicious. Subsequently, one or more virtual honeypots are generated for the suspicious threads and the injected thread's response to the virtual honeypots included in anti-ransomware heuristic analysis.

Ransomware protection in advanced injection-based attacks

Disclosed herein are systems and method for detecting malware in obfuscated scripts. A method may include receiving the obfuscated script on a computing device written in a first coding language, wherein a malware scanner on the computing device is incompatible with the first coding language, identifying the first coding language based on detected keywords and operators in the obfuscated script, converting each line of the obfuscated script in the first coding language into a respective modified abstract syntax tree (mAST), receiving artifacts of the obfuscated script by executing at least one mAST using a universal emulator, scanning the artifacts for malware using the malware scanner; and in response to detecting the malware in the obfuscated script based on the scanning, performing a remediation action on the obfuscated script.

Systems and methods for detecting malware in obfuscated scripts

In one aspect, a system may receive, via a user graphical user interface, a request to perform a data migration from a source data center to a destination data center. A system may in response to receiving the request, perform the data migration by: (1) creating a copy of a tenants structure that lists information about each tenant of the source data center, wherein a respective tenant is a representation of a user on a platform level, (2) creating and activating, on the destination data center, a plurality of new user accounts that match a main login body and suffix of a plurality of existing user accounts on the source data center, (3) re-registering, at the destination data center, agents from the source data center; and (4) migrating backup data from the source data center to the destination data center.

Systems and methods for performing data migration between data centers

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.

Systems and methods of using business impact analysis data to assess the risk of security mitigation steps

Disclosed herein are systems and method for detecting vulnerabilities in devices within a network. In one exemplary aspect, a method includes: intercepting a plurality of packets in a network with a plurality of devices; identifying a subset of packets corresponding to a first device of the network; extracting and embedding a plurality of features representing device behavior from the subset of packets into an input vector; executing a machine learning algorithm that receives the input vector and outputs a device type of the first device, wherein the machine learning algorithm is trained using a training dataset including network traffic information labelled with device types and a list of attributes of each respective device type; performing, for the first device, a security scan specific to the device type; and in response to detecting a vulnerability based on the security scan, generating a vulnerability assessment report for the first device.

Systems and methods for detecting vulnerabilities in internet-of-things (IOT) devices

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.

Detection of malicious phishing emails

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Singapore, Singapore

Serg Bell