dlr-ne-ics-security

DLR-NE for ICS Security Games Dynamical Low-Rank Equilibrium Computation for Stochastic Games between Advanced Persistent Threats and Moving Target Defense Regular Paper, under review at Automatica resubmitted October 2026 Overview This repository contains the implementation and experimental...

Game of Phones: Harnessing Game Theory and LLMs in 'Bot Wars' to Counteract Phone Scams

Phone scams impose substantial and growing costs on individuals and financial systems worldwide, yet existing countermeasures impose no resource cost on perpetrators. Strategic scam-baiting, in which automated agents engage scammers to deplete their operational capacity, offers a proactive...

The Deceptive Bandit Problem: Exploratory Coupling and the Fragility of Multi-Agent Learning

Randomized exploration is central to bandit learning, multi-agent reinforcement learning, and zeroth-order policy search, yet its independence and privacy are usually only treated as technical assumptions. We show that these properties are critical for security purposes and demonstrate how an...

Too Late to Slash: Coordinating a Risk-Free Equivocation Attack

Slashing is commonly argued to secure proof-of-stake blockchains by confiscating the stake of misbehaving validators. The usual justification is that, without slashing, validators can solicit an equivocation attack by signing conflicting blocks: if enough others join, the attack succeeds;...

Resource-Aware Intrusion Detection in Infrastructure Networks: A Game-Theoretic Approach

Infrastructure networks increasingly rely on distributed sensing to detect intrusions before attackers reach valuable assets. Yet sensing devices, communication resources, and edge server capacity are limited, while intelligent attackers can adapt their routes to the deployed defense. Motivated b...

A Surveillance Evasion Game with Continuous Sensor Redeployment Via Bilevel Optimization

Uncrewed Aerial Systems UASs have become a growing threat to the security of critical infrastructure, exploiting spatiotemporal gaps in sensor perimeters to infiltrate restricted airspace undetected. We formulate this interaction as a two-player zero-sum differential game between an adversarial U...

Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing

In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion. To addres...