Event-Driven Temporal Graph Networks for Asynchronous Multi-Agent Cyber Defense in NetForge_RL

1. 1
   Understand the Sim2Real Gap

   Grasp why traditional cyber simulations fail to accurately represent real-world Security Operations Centers (SOCs) for Multi-Agent Reinforcement Learning (MARL). Focus on limitations like abstract network physics, synchronous operations, and idealized state information that prevent effective policy deployment.
2. 2
   Set Up NetForge_RL Environment

   Configure your NetForge_RL simulation environment to support event-driven dynamics and dynamic graph manipulation. This may require extending NetForge_RL's native capabilities or integrating custom modules for fine-grained temporal events.
3. 3
   Define Event-Driven Temporal Graph Network (TGN) Architecture

   Design and implement a TGN model capable of processing dynamic graph updates and temporal features. Utilize deep learning frameworks like PyTorch or TensorFlow to build components that capture time-varying relationships and event sequences.
4. 4
   Integrate TGN for Dynamic Simulation Updates

   Connect your TGN model to NetForge_RL to allow the simulation to feed real-time event data into the TGN and for the TGN to inform agent decision-making. Ensure the integration supports asynchronous updates reflecting real-world cyber defense scenarios.
5. 5
   Train Asynchronous MARL Agents

   Develop and train MARL policies within the TGN-enhanced NetForge_RL environment. Focus on agents that can operate asynchronously, reacting to events and making decisions in a dynamic, non-sequential manner, mirroring actual SOC operations.
6. 6
   Evaluate Realism and Performance

   Assess the trained MARL agents' performance and the simulation's fidelity. Compare results against traditional synchronous simulations to quantify the benefits of event-driven temporal graph networks in achieving more realistic and deployable cyber defense strategies.

import torch
import torch.nn as nn
import torch.nn.functional as F

class TemporalGraphAttentionLayer(nn.Module):
    def __init__(self, in_features, out_features, dropout=0.6, alpha=0.2, concat=True):
        super(TemporalGraphAttentionLayer, self).__init__()
        self.dropout = dropout
        self.in_features = in_features
        self.out_features = out_features
        self.alpha = alpha
        self.concat = concat

        self.W = nn.Parameter(torch.empty(size=(in_features, out_features)))
        nn.init.xavier_uniform_(self.W.data, gain=1.414)
        self.a = nn.Parameter(torch.empty(size=(2*out_features, 1)))
        nn.init.xavier_uniform_(self.a.data, gain=1.414)

        self.leakyrelu = nn.LeakyReLU(self.alpha)

    def forward(self, h, adj):
        Wh = torch.matmul(h, self.W)
        
        e = self._prepare_attentional_mechanism_input(Wh)

        zero_vec = -9e15*torch.ones_like(e)
        attention = torch.where(adj > 0, e, zero_vec)
        attention = F.softmax(attention, dim=1)
        attention = F.dropout(attention, self.dropout, training=self.training)
        h_prime = torch.matmul(attention, Wh)

        if self.concat:
            return F.elu(h_prime)
        else:
            return h_prime

    def _prepare_attentional_mechanism_input(self, Wh):
        Wh1 = torch.matmul(Wh, self.a[:self.out_features, :])
        Wh2 = torch.matmul(Wh, self.a[self.out_features:, :])
        e = Wh1 + Wh2.transpose(0, 1)
        return self.leakyrelu(e)

# Example Usage:
N = 10 # Number of nodes
F_in = 16 # Input features per node
F_out = 32 # Output features per node

# Dummy input features and adjacency matrix (representing a graph at a specific time step)
features = torch.randn(N, F_in)
adj_matrix = torch.randint(0, 2, (N, N)).float() # Binary adjacency matrix
adj_matrix = adj_matrix - torch.diag_embed(torch.diag(adj_matrix)) # Remove self-loops

# Instantiate and apply the Temporal Graph Attention Layer
gat_layer = TemporalGraphAttentionLayer(F_in, F_out)
output = gat_layer(features, adj_matrix)

print(f"Input features shape: {features.shape}")
print(f"Output features shape: {output.shape}") # Expected: (N, F_out)