Uab 360 It

360 It, Uab

Dainius Razinskas

Mantas Briliauskas

Vykintas Maknickas

Mohamed Adly Amer Elgaafary

Browser-side execution detection and prevention may include identifying, by a client device, a change to an anchor tag in a document object model of a web page; in response to identifying the change to the anchor tag, transmitting, by the client device to a link checking server, a link evaluation request for the anchor tag; receiving, by the client device and from the link checking server, a response indicating a failure of at least one test performed by the link checking server with respect to the anchor tag; and, in response to the response indicating the failure, altering a display of the anchor tag, to obtain an altered anchor tag, in the web page, the altered anchor tag including a visual indicator of the failure.

Browser-side execution detection and prevention

Systems and methods for malware detection are provided herein. In some embodiments, a system having one or more processors is configured to: perform, on a plurality of user devices, at least one of a static analysis or a behavioral analysis of a file downloaded to a user device; receive a plurality of features extracted from the downloaded file; train at least one machine learning model, on a central server in communication with the plurality of user device, based on the plurality of features; distribute the at least one trained machine learning model to the plurality of user devices; and update at least one of a machine learning model used for the static analysis or behavioral analysis with the distributed at least one trained machine learning model.

System and method for information gain for malware detection

A malware detection method that uses federated learning includes transmitting file characterization information for one or more local files, receiving a first malware detection model and labeled training data that is generated by a remote device, training the first malware detection model using the labeled training data set, transmitting parameters of the trained first malware detection model to the remote device, and receiving, from the remote device, a second malware detection model, wherein the second malware detection model is trained by federated learning using the parameters of the trained first malware detection model and additional parameters provided by one or more additional remote devices.

Malware detection using federated learning

A system for detecting malware includes a server and processor that executes instructions to receive, from the server, a first malware detection model and a database of known malicious files; label each file of a training data set as either malicious or clean by comparing each file of the training data set to the database, where if a match is not found to in the database, the model is used to predict maliciousness; train the first malware detection model using the labeled training data set; transmit parameters of the trained first malware detection model to the server; and receive, from the server, a second malware detection model, wherein the second malware detection model is trained by federated learning using the parameters of the trained first malware detection model and additional parameters provided by one or more remote devices.

Systems and methods for computer security are provided by a processor programmed to: receive an Internet file and produce a cryptographic hash of the Internet file; compare the cryptographic hash to external malware databases and external antiviral databases for a malicious file match to determine the Internet file's status that is based upon a weighted consensus algorithm derived from the external malware databases and the external antiviral databases; check if the Internet file's status determination matches the internal malicious software database Internet file's status and update the internal malicious software database based upon the Internet file's status determination if a threshold for the weighted consensus algorithm is exceeded; and train a machine learning algorithm using the Internet file's status determination to create a labelled data set based upon the Internet file's status determination, and provide a report via the input/output device based upon the Internet file's status determination.

System and method for training of antimalware machine learning models

A method of generating a predictive model for malware detection using federated learning includes transmitting, to each of a plurality of remote devices, a copy of the predictive model, where the predictive model is configured to predict whether a file is malicious; receiving, from each of the plurality of remote devices, model parameters determined by independently training the copy of the predictive model on each of the plurality of remote devices using local files stored on respective ones of the plurality of remote devices; generating a federated model by training the predictive model based on the model parameters received from each of the plurality of remote devices; and transmitting the federated model to each of the plurality of remote devices.

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Vilnius, Lithuania

Dainius Razinskas