geneva.actions.strategy

class strategy.Strategy(in_actions, out_actions, environment_id=None)

Bases: object

__init__(in_actions, out_actions, environment_id=None)

Initialize self. See help(type(self)) for accurate signature.

act_on_packet(packet, logger, direction='out')

Runs the strategy on a given scapy packet.

init_from_scratch(num_in_trees, num_out_trees, num_in_actions, num_out_actions, disabled=None)

Initializes this individual by drawing random actions.

initialize(logger, num_in_trees, num_out_trees, num_in_actions, num_out_actions, seed, disabled=None)

Initializes a new strategy object randomly.

mutate(logger)

Top level mutation function for a strategy. Simply mutates the out and in trees.

mutate_dir(trees, direction, logger)

Mutates a list of trees. Requires the direction the tree operates on (in or out).

pretty_print()

pretty_str_forest(forest)

Returns a string representation of a given forest (inbound or outbound)

run_on_packet(packet, logger, direction)

Runs the strategy on a given packet given the forest direction.

str_forest(forest)

Returns a string representation of a given forest (inbound or outbound)

strategy.do_mate(forest1, forest2)

Performs mating between two given forests (lists of trees). With 80% probability, a random tree from each forest are mated, otherwise, a random tree is swapped between them.

strategy.mate(ind1, ind2, indpb)

Executes a uniform crossover that modify in place the two individuals. The attributes are swapped according to the indpb probability.

strategy.swap_one(forest1, forest2)

Swaps a random tree from forest1 and forest2.

It picks a random element within forest1 and a random element within forest2, chooses a random index within each forest, and inserts the random element