AI-Driven Dynamic Cryptography Selection in MapReduce: A Deep Reinforcement Learning Approach for Lightweight Encryption Optimization

Secure large-scale data processing (Big Data) in distributed environments such as Hadoop MapReduce poses a constant challenge of balancing performance and security. While recent approaches (MR-LWT) have demonstrated the effectiveness of lightweight cryptography (LWC) in reducing computational overhead, they generally rely on a static selection of algorithms. This paper proposes Adaptive-Crypto-RL, a dynamic selection system based on a Deep Q-Network (DQN). By integrating directly into the existing MR-LWT architecture, our reinforcement learning agent evaluates the cluster state (CPU, RAM, network load) and data characteristics in real-time to select the optimal algorithm (Chacha20, Rabbit, NOEKEON, or AES-CTR). Experiments demonstrate that this adaptive selection improves overall performance by up to 75% compared to AES(CBC) and 50% compared to HC-128, with a negligible inference overhead of 2 to 4 seconds.