Saudi Arabian Oil Company

Irslan Siddiq

Kashif Khawaja

Saeed A Alsaeed

A method of determining whether an end user device coupled to a voice over Internet Protocol (VOIP) network is online or offline assembling source data from a variety of network monitoring devices and services including at least one selected from the group consisting of: of asset inventories, VoIP Call Control Servers, routers, firewalls, and intrusion protection systems (IPSs) concerning an online and offline status of a plurality of end user device in the network. The method also comprises training a machine learning algorithm using the source data to determine whether the end user devices coupled to the VoIP network are online or offline, and after training, executing the machine learning algorithm to determine whether a specific end user device currently coupled to the VoIP network is online or offline.

Tracking in-service IP phones using machine learning

A system, a method, and a computer program for analyzing network security data from diverse data sources to predict and remediate a vulnerability at a node in a computer network, comprising receiving network security data from a plurality of data sources, extracting feature vectors from the received network security data, applying a machine learning model to the extracted feature vectors to generate a weighted vulnerability value, predicting a computing resource vulnerability at a node in the computer network based on the weighted vulnerability value, and transmitting the predicted computing resource vulnerability to a computing device which is configured to remediate the predicted computing resource vulnerability.

Network security system and method for preemptively identifying or remediating security vulnerabilities

A method and system for remediating a vulnerability in a first computing resource asset in a computer network, including receiving vulnerability scanning results data from each respective one of a plurality of diverse vulnerability scanners, storing the vulnerability scanning results data as a collection of vulnerability scanning results data, normalizing and extracting common features from the normalized data, vectorizing the common features to determine feature vectors, applying a false positive predictor model to predict a false positive, separating vulnerability scanning results data that corresponds to one or more predicted false positives from a remainder of the collection of vulnerability scanning result data, and sending the remainder of the collection of vulnerability scanning results data to a second computer resource asset.

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Dhahran, Saudi Arabia

Irslan Siddiq