Uab 360 It

Aleksandr Sevcenko

Mantas Briliauskas

Mohamed Adly Amer Elgaafary

Vykintas Maknickas

Justas Rafanavicius

Ernestas Kulik

A method including analyzing affected data known to include harmful content, and clean data known to be free of the harmful content; determining, based on analyzing the affected data and the clean data, harmful traits that appear in the affected data with a frequency that satisfies a threshold frequency, and clean traits that appear in the clean data with the frequency that satisfies the threshold frequency; mixing the harmful traits and the clean traits to determine a mixed set; analyzing the affected data based on utilizing the mixed set to determine a harmful pattern that indicates characteristics associated with the harmful traits and the clean traits; and transmitting pattern information indicating the harmful pattern to enable the user device to determine whether given data includes the harmful content is disclosed. Various other aspects are contemplated.

Dynamic analysis for detecting harmful content

A method including determining, by an infrastructure device, a mixed set of harmful traits and clean traits, the harmful traits being associated with affected data known to include harmful content and the clean traits being associated with clean data known to be free of the harmful content; determining, by the infrastructure device, harmful patterns indicating characteristics of the harmful traits based on comparing the affected data with the mixed set, wherein a harmful pattern indicates a particular combination of one or more of the harmful traits; transmitting, by the infrastructure device to a user device, the harmful patterns; determining, by the user device, a determined pattern based at least in part on traits included in given data; and determining, by the user device, whether the given data includes the malicious content based on comparing the determined pattern with the harmful patterns is disclosed. Various other aspects are contemplated.

Optimized analysis for detecting harmful content

A method including receiving, by a security device from a network device, an initial security instruction set including a plurality of initial security instructions associated with operation of the security device; receiving, by the security device from the network device, an event signal associated with the security device carrying out a network-facing operation; transmitting, by the security device to the network device based on receiving the event signal, a security instruction associated with the security device carrying out the network-facing operation, the security instruction being from among the plurality of initial security instructions; receiving, by the security device from the network device based on transmitting the security instruction, communication information to enable the security device to carry out the network-facing operation; and carrying out, by the security device, the network-facing operation based on utilizing the communication information is disclosed. Various other aspects are contemplated.

Securing network devices against network vulnerabilities

An exemplary system and method are disclosed for detecting malware via an antimalware application employing adversarial machine learning such as generative adversarial machine learning and the training and/or configuring of such systems. The exemplary system and method are configured with two or more generative adversarial networks (GANs), including (i) a first generative adversarial network (GAN) that can be configured using a library of malware code or non-malware code and (ii) a second generative adversarial network (GAN) that operates in conjunction with the first generative adversarial network (GAN) in which the second generative adversarial network is configured using a library of non-malware code.

System and method for antimalware application adversarial machine learning

A method and apparatus for providing IP address filtering. The method identifies one or more suspicious Uniform Resource Locators (URLs) and resolves the one or more suspicious URLs to one or more suspicious IP addresses. A suspicious IP address list is created containing the one or more suspicious IP addresses. The suspicious IP address list may be used to facilitate a security response to filter one or more of the IP addresses in the suspicious IP address list.

Method and apparatus for providing IP address filtering

A method for controlling access to process data includes encrypting process data of a process; receiving a request to access the process data; requesting a security code to access the encrypted process data; receiving the security code; authenticating the received security code; and granting access to the encrypted process data if the received security code is successfully authenticated and denying access to the encrypted process data if the received security code is not successfully authenticated.

System and method for protecting browser data

In an embodiment, systems and methods for detecting malware are provided. A server trains a static malware model and a dynamic malware model to detect malware in files. The models are distributed to a plurality of user devices for use by antimalware software executing on the user devices. When a user device receives a file, the static malware model is used to determine whether the file contains malware. If the static malware model is unable to make the determination, when the file is later executed, the dynamic malware model is used to determine whether the file contains malware. The file along with the determination made by the dynamic malware model are then provided to the server. The server then retrains the static malware model using the received files and the received determinations. The server then distributes the updated static malware model to each of the devices.

Systems and methods for detecting malware using static and dynamic malware models

Systems and methods for malware filtering are provided herein. In some embodiments, a system having one or more processors is configured to: retrieve a file downloaded to a user device; break the downloaded file into a plurality of chunks; scan the plurality of chunks to identify potentially malicious chunks; predict whether the downloaded file is malicious based on the scan of the plurality of chunks; and determine whether the downloaded file is malicious based on the prediction.

System and method for differential malware scanner

A method including configuring, by an infrastructure device, a user device to receive harmful patterns indicating characteristics of harmful traits included in affected data known to include malicious content and clean patterns indicating characteristics of clean traits included in clean data known to be free of the malicious content; configuring the user device to receive a first portion of given data; configuring the user device to determine a pattern associated with traits included in the first portion of the given data; configuring the user device to determine whether the first portion of the given data includes the malicious content based on comparing the determined pattern with the harmful patterns and the clean patterns; and configuring the user device to selectively receive a second portion of the given data based determining whether the first portion of the given data includes the malicious content is disclosed. Various other aspects are contemplated.

A method including receiving, by a user device, harmful patterns indicating characteristics of harmful traits included in affected data known to include malicious content and clean patterns indicating characteristics of clean traits included in clean data known to be free of the malicious content; determining, by the user device, a pattern associated with traits included in given data; and determining, by the user device, whether the given data includes the malicious content based at least in part on comparing the determined pattern with the harmful patterns and the clean patterns. Various other aspects are contemplated.

Growing community of inventors

Vilnius, Lithuania

Aleksandr Sevcenko