Edge-centric resilience with proactive jammer-resilient optimization

1 . A method comprising:

identifying an adversarial jammer is causing an impact on a wireless system;

generating a risk assessment of impact caused by the adversarial jammer to a user, wherein the generating further comprises:

constructing an threat model comprising jammer characteristics, user distribution and mobility, network characteristics, and vulnerability points; and

calculating attack probability using temporal and spatial likelihood, network exposure, and adaptive behaviors of the adversarial jammer and the wireless system;

identifying an action to apply based on the risk assessment; and

performing the identified action.

2 . The method of claim 1 , further comprising:

generating a reinforcement learning, artificial intelligence training environment;

performing reinforcement learning, artificial intelligence training during an exploration phase; and

performing reinforcement learning, artificial intelligence deployment during an exploitation phase.

3 . The method of claim 1 , wherein identifying the adversarial jammer further comprises;

generating a preliminary characterization of the adversarial jammer based on a detected effect on the wireless system caused by the adversarial jammer.

4 . The method of claim 1 , wherein the risk assessment considers an attack probability to a user utilizing the wireless system, adversarial jammer strength, task priority determined through machine learning of prior attacks, and task responsibility determined through machine learning of prior attacks.

5 . The method of claim 1 , wherein the action is identified through a rule-based system or a reinforced learning-based agent.

6 . The method of claim 1 , wherein the action is selected from a group consisting of delaying training, applying countermeasures, pre-emptively secure users, and continue as is.

7 . The method of claim 2 , further comprising:

storing data from the reinforcement learning, artificial intelligence training and reinforcement learning, artificial intelligence deployment in a repository, wherein the data is selected from a group consisting of rules, policies, metadata, and other historical data.

8 . A computer system comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more non-transitory computer-readable storage media to cause the processor set to perform operations comprising: identifying an adversarial jammer is causing an impact on a wireless system; generating a risk assessment of impact caused by the adversarial jammer to a user, wherein the generating further comprises: constructing a threat model comprising jammer characteristics, user distribution and mobility, network characteristics, and vulnerability points; and calculating attack probability using temporal and spatial likelihood, network exposure, and adaptive behaviors of the adversarial jammer and the wireless system; identifying an action to apply based on the risk assessment; and performing the identified action.

9 . The computer system of claim 8 , further comprising:

generating a reinforcement learning, artificial intelligence training environment;

performing reinforcement learning, artificial intelligence training during an exploration phase; and

performing reinforcement learning, artificial intelligence deployment during an exploitation phase.

10 . The computer system of claim 8 , wherein identifying the adversarial jammer further comprises;

generating a preliminary characterization of the adversarial jammer based on a detected effect on the wireless system caused by the adversarial jammer.

11 . The computer system of claim 8 , wherein the risk assessment considers an attack probability to a user utilizing the wireless system, adversarial jammer strength, task priority determined through machine learning of prior attacks, and task responsibility determined through machine learning of prior attacks.

12 . The computer system of claim 8 , wherein the action is identified through a rule-based system or a reinforced learning-based agent.

13 . The computer system of claim 8 , wherein the action is selected from a group consisting of delaying training, applying countermeasures, pre-emptively secure users, and continue as is.

14 . The computer system of claim 9 , further comprising:

storing data from the reinforcement learning, artificial intelligence training and reinforcement learning, artificial intelligence deployment in a repository, wherein the data is selected from a group consisting of rules, policies, metadata, and other historical data.

15 . A computer program product comprising: one or more non-transitory computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations comprising: identifying an adversarial jammer is causing an impact on a wireless system; generating a risk assessment of impact caused by the adversarial jammer to a user, wherein the generating further comprises: constructing a threat model comprising jammer characteristics, user distribution and mobility, network characteristics, and vulnerability points; and calculating attack probability using temporal and spatial likelihood, network exposure, and adaptive behaviors of the adversarial jammer and the wireless system; identifying an action to apply based on the risk assessment; and performing the identified action.

16 . The computer program product of claim 15 , further comprising:

generating a reinforcement learning, artificial intelligence training environment;

performing reinforcement learning, artificial intelligence training during an exploration phase; and

performing reinforcement learning, artificial intelligence deployment during an exploitation phase.

17 . The computer program product of claim 15 , wherein identifying the adversarial jammer further comprises;

generating a preliminary characterization of the adversarial jammer based on a detected effect on the wireless system caused by the adversarial jammer.

18 . The computer program product of claim 15 , wherein the risk assessment considers an attack probability to a user utilizing the wireless system, adversarial jammer strength, task priority determined through machine learning of prior attacks, and task responsibility determined through machine learning of prior attacks.

19 . The computer program product of claim 15 , wherein the action is identified through a rule-based system or a reinforced learning-based agent.

20 . The computer program product of claim 15 , wherein the action is selected from a group consisting of delaying training, applying countermeasures, pre-emptively secure users, and continue as is.