Genetic Algorithm-Based Dynamic Backdoor Attack on Federated
  Learning-Based Network Traffic Classification
Mahmoud Nazzal Nura Aljaafari Ahmed Sawalmeh Abdallah Khreishah Muhammad Anan Abdulelah Algosaibi Mohammed Alnaeem Adel Aldalbahi Abdulaziz Alhumam Conrado P. Vizcarra Shadan Alhamed
Federated learning enables multiple clients to collaboratively contribute to
the learning of a global model orchestrated by a central server. This learning
scheme promotes clients' data privacy and requires reduced communication
overheads. In an application like network traffic classification, this helps
hide the network vulnerabilities and weakness points. However, federated
learning is susceptible to backdoor attacks, in which adversaries inject
manipulated model updates into the global model. These updates inject a salient
functionality in the global model that can be launched with specific input
patterns. Nonetheless, the vulnerability of network traffic classification
models based on federated learning to these attacks remains unexplored. In this
paper, we propose GABAttack, a novel genetic algorithm-based backdoor attack
against federated learning for network traffic classification. GABAttack
utilizes a genetic algorithm to optimize the values and locations of backdoor
trigger patterns, ensuring a better fit with the input and the model. This
input-tailored dynamic attack is promising for improved attack evasiveness
while being effective. Extensive experiments conducted over real-world network
datasets validate the success of the proposed GABAttack in various situations
while maintaining almost invisible activity. This research serves as an
alarming call for network security experts and practitioners to develop robust
defense measures against such attacks.