This paper presents a novel differential fault attack (DFA) on the Grain-128AEAD stream cipher that integrates fault signature analysis with machine learning and reinforcement learning. The proposed attack operates under a realistic adversarial model in which the attacker can target a register but cannot control the precise fault location. We first develop a signature-based technique and two machine learning models, namely Random Forest and Gradient Boosted Trees, for fault location identification, which achieve over 99.9% accuracy. We then introduce a Multi-Armed Bandit (MAB) framework to identify optimal fault targets that maximize information gain for state recovery. Experimental results show that our MAB-driven strategy recovers the 256-bit internal state of the cipher with an average of 96 and 134 faults under the precise and random fault models, respectively.

Described here is a method for navigating multiple mobile robots which estimate locations and motions of unknown moving objects. Each robot with conic projection image sensor COPIS can observe an omnidirectional view around the robot in real-time with use of a conic mirror. First, each robot communicates and exchanges information of sensory data. Then, unknown moving objects and a reciprocating robot are discriminated, and motions and locations of unknown moving objects are estimated by cooperative observation of azimuth changes between the two reciprocating robots. >