## Step 1: Define the symbolic representation of the variables
The variables are defined as follows:
- 'x0': 'Mbps bandwidth allocated to monitoring'
- 'x1': 'automatic alerts'
- 'x2': 'network administrators'
- 'x3': 'deployed decoys'

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is: $5.2x_0 + 5.57x_1 + 9.85x_2 + 4.94x_3$

## Step 3: List all the constraints in symbolic notation
Constraints:
1. $4.81x_0 \leq 50$
2. $3.7x_0 \leq 120$
3. $2.43x_1 \leq 50$
4. $3.39x_1 \leq 120$
5. $3.8x_2 \leq 50$
6. $3.4x_2 \leq 120$
7. $1.36x_3 \leq 50$
8. $4.51x_3 \leq 120$
9. $4.81x_0 + 3.8x_2 \geq 10$
10. $2.43x_1 + 1.36x_3 \geq 4$
11. $4.81x_0 + 2.43x_1 \geq 5$
12. $3.8x_2 + 1.36x_3 \geq 12$
13. $4.81x_0 + 2.43x_1 + 3.8x_2 + 1.36x_3 \geq 12$
14. $3.39x_1 + 4.51x_3 \geq 15$
15. $3.7x_0 + 3.4x_2 \geq 28$
16. $3.4x_2 + 4.51x_3 \geq 11$
17. $3.7x_0 + 4.51x_3 \geq 13$
18. $3.7x_0 + 3.39x_1 \geq 17$
19. $3.7x_0 + 3.4x_2 + 4.51x_3 \geq 18$
20. $3.7x_0 + 3.39x_1 + 3.4x_2 + 4.51x_3 \geq 18$
21. $6x_0 - 2x_3 \geq 0$
22. $4x_1 - x_3 \geq 0$
23. $2.43x_1 + 1.36x_3 \leq 46$
24. $4.81x_0 + 1.36x_3 \leq 42$
25. $3.8x_2 + 1.36x_3 \leq 45$
26. $4.81x_0 + 2.43x_1 + 3.8x_2 \leq 45$
27. $4.81x_0 + 2.43x_1 + 1.36x_3 \leq 44$
28. $3.39x_1 + 3.4x_2 \leq 90$
29. $3.7x_0 + 3.39x_1 \leq 78$
30. $3.4x_2 + 4.51x_3 \leq 90$
31. $3.39x_1 + 4.51x_3 \leq 102$

## Step 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'Mbps bandwidth allocated to monitoring'),
        ('x1', 'automatic alerts'),
        ('x2', 'network administrators'),
        ('x3', 'deployed decoys')
    ],
    'objective_function': '5.2*x0 + 5.57*x1 + 9.85*x2 + 4.94*x3',
    'constraints': [
        '4.81*x0 <= 50',
        '3.7*x0 <= 120',
        '2.43*x1 <= 50',
        '3.39*x1 <= 120',
        '3.8*x2 <= 50',
        '3.4*x2 <= 120',
        '1.36*x3 <= 50',
        '4.51*x3 <= 120',
        '4.81*x0 + 3.8*x2 >= 10',
        '2.43*x1 + 1.36*x3 >= 4',
        '4.81*x0 + 2.43*x1 >= 5',
        '3.8*x2 + 1.36*x3 >= 12',
        '4.81*x0 + 2.43*x1 + 3.8*x2 + 1.36*x3 >= 12',
        '3.39*x1 + 4.51*x3 >= 15',
        '3.7*x0 + 3.4*x2 >= 28',
        '3.4*x2 + 4.51*x3 >= 11',
        '3.7*x0 + 4.51*x3 >= 13',
        '3.7*x0 + 3.39*x1 >= 17',
        '3.7*x0 + 3.4*x2 + 4.51*x3 >= 18',
        '3.7*x0 + 3.39*x1 + 3.4*x2 + 4.51*x3 >= 18',
        '6*x0 - 2*x3 >= 0',
        '4*x1 - x3 >= 0',
        '2.43*x1 + 1.36*x3 <= 46',
        '4.81*x0 + 1.36*x3 <= 42',
        '3.8*x2 + 1.36*x3 <= 45',
        '4.81*x0 + 2.43*x1 + 3.8*x2 <= 45',
        '4.81*x0 + 2.43*x1 + 1.36*x3 <= 44',
        '3.39*x1 + 3.4*x2 <= 90',
        '3.7*x0 + 3.39*x1 <= 78',
        '3.4*x2 + 4.51*x3 <= 90',
        '3.39*x1 + 4.51*x3 <= 102'
    ]
}
```

## Step 5: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name='x0', vtype=gurobi.GRB.INTEGER)  # Mbps bandwidth allocated to monitoring
    x1 = model.addVar(name='x1', vtype=gurobi.GRB.INTEGER)  # automatic alerts
    x2 = model.addVar(name='x2', vtype=gurobi.GRB.INTEGER)  # network administrators
    x3 = model.addVar(name='x3', vtype=gurobi.GRB.INTEGER)  # deployed decoys

    # Objective function
    model.setObjective(5.2*x0 + 5.57*x1 + 9.85*x2 + 4.94*x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(4.81*x0 <= 50)
    model.addConstr(3.7*x0 <= 120)
    model.addConstr(2.43*x1 <= 50)
    model.addConstr(3.39*x1 <= 120)
    model.addConstr(3.8*x2 <= 50)
    model.addConstr(3.4*x2 <= 120)
    model.addConstr(1.36*x3 <= 50)
    model.addConstr(4.51*x3 <= 120)
    model.addConstr(4.81*x0 + 3.8*x2 >= 10)
    model.addConstr(2.43*x1 + 1.36*x3 >= 4)
    model.addConstr(4.81*x0 + 2.43*x1 >= 5)
    model.addConstr(3.8*x2 + 1.36*x3 >= 12)
    model.addConstr(4.81*x0 + 2.43*x1 + 3.8*x2 + 1.36*x3 >= 12)
    model.addConstr(3.39*x1 + 4.51*x3 >= 15)
    model.addConstr(3.7*x0 + 3.4*x2 >= 28)
    model.addConstr(3.4*x2 + 4.51*x3 >= 11)
    model.addConstr(3.7*x0 + 4.51*x3 >= 13)
    model.addConstr(3.7*x0 + 3.39*x1 >= 17)
    model.addConstr(3.7*x0 + 3.4*x2 + 4.51*x3 >= 18)
    model.addConstr(3.7*x0 + 3.39*x1 + 3.4*x2 + 4.51*x3 >= 18)
    model.addConstr(6*x0 - 2*x3 >= 0)
    model.addConstr(4*x1 - x3 >= 0)
    model.addConstr(2.43*x1 + 1.36*x3 <= 46)
    model.addConstr(4.81*x0 + 1.36*x3 <= 42)
    model.addConstr(3.8*x2 + 1.36*x3 <= 45)
    model.addConstr(4.81*x0 + 2.43*x1 + 3.8*x2 <= 45)
    model.addConstr(4.81*x0 + 2.43*x1 + 1.36*x3 <= 44)
    model.addConstr(3.39*x1 + 3.4*x2 <= 90)
    model.addConstr(3.7*x0 + 3.39*x1 <= 78)
    model.addConstr(3.4*x2 + 4.51*x3 <= 90)
    model.addConstr(3.39*x1 + 4.51*x3 <= 102)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Objective: {model.objval}")
        print(f"x0: {x0.varValue}")
        print(f"x1: {x1.varValue}")
        print(f"x2: {x2.varValue}")
        print(f"x3: {x3.varValue}")
    else:
        print("No optimal solution found.")

optimize_problem()
```