Abstract: A technique for providing cyber resilience by integrating autonomous adversary and defender agents, deep learning, and graph thinking. An automated competitive environment of autonomous adversary and defender agents is provided such that the adversary agent can emulate the adversary activities, patterns, and intentions using all available cybersecurity measurements and observations, and, the defender agent can generate and suggest the best possible appropriate actions to mitigate or prevent adversary activities while recovering or protecting assets. An automated cyber resilience system with autonomous agents is provided using machine learning and security analytics to first predict the current and future adversary activities and then provide an automated critical asset protection and recovery by enabling agents to take appropriate reactive and pro-active actions at each time step to prevent, recover, or mitigate adversary activities over enterprise and tactical networks.

Abstract: A technique for providing cyber resilience by integrating autonomous adversary and defender agents, deep learning, and graph thinking. An automated competitive environment of autonomous adversary and defender agents is provided such that the adversary agent can emulate the adversary activities, patterns, and intentions using all available cybersecurity measurements and observations, and, the defender agent can generate and suggest the best possible appropriate actions to mitigate or prevent adversary activities while recovering or protecting assets. An automated cyber resilience system with autonomous agents is provided using machine learning and security analytics to first predict the current and future adversary activities and then provide an automated critical asset protection and recovery by enabling agents to take appropriate reactive and pro-active actions at each time step to prevent, recover, or mitigate adversary activities over enterprise and tactical networks.

Abstract: Provided are processes of monitoring or modifying a network of electronically connected assets that dynamically builds relationships and dependencies among detected vulnerabilities in one or more of the assets and sensor measurements so that risk assessment can be achieved more accurately and in real-time. A process includes: identifying a plurality of vulnerabilities on a network of electronically interconnected devices representing one or more critical assets; determining dependencies between each vulnerability in the plurality of vulnerabilities; creating a hidden Markov model representing an attack state of each vulnerability of the plurality of vulnerabilities; determining the exploit likelihood of each of the attack states at a first time; determining the most probable sequences or paths of the attack states; and identifying dynamically the risk of one or more of the critical assets based on the sequences or paths of attack states.

Abstract: A method and apparatus is provided for determining aggregated value of risk and resilience metrics of critical nodes in a network of computer nodes, comprising determining a status of each node in a plurality of nodes, computing one or more effectiveness attributes for each node in the plurality of nodes, ranking the plurality of nodes based upon at least the one or more effectiveness attributes of each node, determining one or more nodes as critical nodes based on the ranking and computing a criticality surface of the one or more critical nodes as the aggregated value of risk and resilience metrics, wherein the criticality surface is an aggregation of the one or more effectiveness attribute for each of the one or more critical nodes.

Abstract: Provided are processes of monitoring or modifying a network of electronically connected assets that dynamically builds relationships and dependencies among detected vulnerabilities in one or more of the assets and sensor measurements so that risk assessment can be achieved more accurately and in real-time. A process includes: identifying a plurality of vulnerabilities on a network of electronically interconnected devices representing one or more critical assets; determining dependencies between each vulnerability in the plurality of vulnerabilities; creating a hidden Markov model representing an attack state of each vulnerability of the plurality of vulnerabilities; determining the exploit likelihood of each of the attack states at a first time; determining the most probable sequences or paths of the attack states; and identifying dynamically the risk of one or more of the critical assets based on the sequences or paths of attack states.

Abstract: A method and apparatus is provided for determining aggregated value of risk and resilience metrics of critical nodes in a network of computer nodes, comprising determining a status of each node in a plurality of nodes, computing one or more effectiveness attributes for each node in the plurality of nodes, ranking the plurality of nodes based upon at least the one or more effectiveness attributes of each node, determining one or more nodes as critical nodes based on the ranking and computing a criticality surface of the one or more critical nodes as the aggregated value of risk and resilience metrics, wherein the criticality surface is an aggregation of the one or more effectiveness attribute for each of the one or more critical nodes.

Abstract: Methods and apparatus are described for transmitting data using a wideband code division multiple access (WCDMA) transmission scheme that utilizes OVSF codes that are assigned in ways that do not result in underutilization of the network due to code blocking. One embodiment of the invention includes a WCDMA transmitter configured to spread data using an orthogonal variable spreading factor (OVSF) code and to periodically transmit the spread data for a predetermined time until all of the data has been transmitted. In some cases, the amount of time in each period that is assigned for transmission of the spread data is determined based on the quality of service requirements of the data.