# Cyber attack simulation

The KTH team released the MAL Simulator, which builds cyber attack and defense simulation on the Meta Attack Language so a new target domain can be modeled without changing simulator source code. Across 30 scenarios derived from the ADS-24 network in the CRATE cyber range, a trained reinforcement-learning attacker returned −301±10.3 on 15 unseen test scenarios, better than breadth-first, depth-first and random search, and a defender trained under 1% false and missed alerts returned −60355±945 against −61653±914 for a heuristic defender. Against the reinforcement-learning attacker, every defender's return dropped markedly.

The KTH team released the MAL Simulator, which builds cyber attack and defense simulation on the Meta Attack Language so a new target domain can be modeled without changing simulator source code. Across 30 scenarios derived from the ADS-24 network in the CRATE cyber range, a trained reinforcement-learning attacker returned −301±10.3 on 15 unseen test scenarios, better than breadth-first, depth-first and random search, and a defender trained under 1% false and missed alerts returned −60355±945 against −61653±914 for a heuristic defender. Against the reinforcement-learning attacker, every defender's return dropped markedly.