Uab 360 It

Cujo LLC

360 It, Uab

Jovaldas Januskevicius

Karolis Pabijanskas

Mindaugas Valkaitis

Kazimieras Celiesius

Leonardas Marozas

Filip Savin

Victor Kuarsingh

Evaldas Kazlauskis

Justinas Bisikirskas

A method including configuring a VPN server to predict or determine potential overloading of the VPN server based on predicting or determining a breach of a critical threshold; configuring the VPN server to verify, based on predicting or determining the potential overloading, an identity of a secondary server with which the VPN server is authorized to establish a secure connection; configuring the VPN server to establish, based on verifying the identity of the secondary server, a secure connection with the secondary server to enable communication of encrypted information between the VPN server and the secondary server; and configuring the VPN server to transmit, to the secondary server, an encrypted message identifying a host device and data of interest to be retrieved from the host device to enable the secondary server to request the data of interest from the host device is disclosed. Various other aspects are contemplated.

Dynamic management of servers based on environmental events

A method including configuring a VPN server to utilize, during an established VPN connection between the VPN server and a user device, a first exit IP address to transmit a first query to a host device for retrieving data of interest; configuring the VPN server to predict or determine potential overloading of the VPN server based on a breach of a critical threshold; configuring the VPN server to establish, based on predicting or determining the breach, a secure connection with a secondary server; and configuring the VPN server to transmit, to the secondary server over the secure connection, an encrypted message identifying the host device and the data of interest to be retrieved from the host device to enable the secondary server to request the data of interest based on utilizing a second exit IP address is disclosed. Various other aspects are contemplated.

A method including predicting or determining, by a VPN server, potential overloading of the VPN server based on predicting or determining a breach of a critical threshold associated with the VPN server; verifying, by the VPN server based on predicting or determining the potential overloading, an identity of a secondary server with which the VPN server is authorized to establish a secure connection; establishing, by the VPN server based on verifying the identity of the secondary server, a secure connection with the secondary server to enable communication of encrypted information between the VPN server and the secondary server; and transmitting, by the VPN server to the secondary server, an encrypted message identifying a host device and data of interest to be retrieved from the host device to enable the secondary server to request the data of interest from the host device is disclosed. Various other aspects are contemplated.

A method including utilizing, by a VPN server, a first exit IP address to transmit a first query to a host device for retrieving data of interest requested by the user device; predicting or determining, by the VPN server, potential overloading of the VPN server; establishing, by the VPN server based on predicting or determining a breach, a secure connection with a secondary server to enable communication of encrypted information between the VPN server and the secondary server; and transmitting, by the VPN server to the secondary server over the secure connection, an encrypted message identifying the host device and the data of interest to enable the secondary server to transmit a second query to request the data of interest based at least in part on utilizing a second exit IP address, different from the first exit IP address is disclosed. Various other aspects are contemplated.

The present embodiment relates to method and system for dynamically identifying the optimal servers from among a plurality of VPN servers. The method and system to score or rank the plurality of VPN servers through mathematical operations to produce a scored list of servers. The servers are dynamically scored based on several server conditions including but not limited to server location, server hub score, server creation time, server load, captcha rates and other like information. The method and system further calculate server penalty scores for a plurality of VPN servers and dynamically identifies optimal servers based on the least server penalty score. Further, the method and system provide means for the VPN service provider to direct their users to connect with the optimal servers consistently.

Dynamic system and method for identifying optimal servers in a virtual private network

Maintaining a database of a plurality of time series data sets, wherein each time series data set is associated to a previously known computer device of a computer network; detecting a connection request from a second computer device of the computer network; collecting one or more new data sets related to the second computer device; comparing the one or more new data sets with one or more time series data sets; calculating one or more value scores related to the plurality of time series data sets based on the comparison; and determining a device association score based on the calculated one or more value scores related to the plurality of time series data sets, wherein the device association score determines an association level between the previously known computer device and the second computer device of the computer network.

Non-intrusive / agentless network device identification

Network device identification. A method includes extracting, from network traffic data of a plurality of user devices in a computer network, one or more data fragments relating to a device model of each user device, associating the one or more data fragments with device identification data assigned to each user device, determining a device model for a specific data fragment based on analyzing one or more data fields associated with the specific data fragment, and generating one or more device model identification rules based on the specific data fragment.

Growing community of inventors

Kaunas, Lithuania

Jovaldas Januskevicius