论文标题
确定对具有有限流程知识的ICS的近乎最佳的一次性攻击
Identifying Near-Optimal Single-Shot Attacks on ICSs with Limited Process Knowledge
论文作者
论文摘要
工业控制系统(ICS)依靠不安全的协议和设备来监视和操作关键基础架构。先前的工作表明,具有详细系统知识的强大攻击者可以操纵交换的传感器数据以恶化流程的性能,甚至导致植物的完全关闭。识别这些攻击需要在所有可能的传感器值上进行迭代,并运行详细的系统模拟或分析以识别最佳攻击。该设置使对手能够在系统操作员意识到操作之前首次应用于系统时最有影响力的攻击。 在这项工作中,我们调查是否没有详细的系统知识的受限攻击者,模拟器可以识别出可比的攻击。特别是,攻击者只需要对工厂中一般信息流的抽象知识,而不是精确的算法,操作参数,过程模型或模拟器。我们提出了一种允许单发攻击的方法,即近乎最佳的攻击,这些攻击可可靠地在第一次尝试时关闭系统。该方法在两种用例上应用和验证,并证明是为了获得与先前工作的可比结果,这些结果依赖于详细的系统信息和仿真。
Industrial Control Systems (ICSs) rely on insecure protocols and devices to monitor and operate critical infrastructure. Prior work has demonstrated that powerful attackers with detailed system knowledge can manipulate exchanged sensor data to deteriorate performance of the process, even leading to full shutdowns of plants. Identifying those attacks requires iterating over all possible sensor values, and running detailed system simulation or analysis to identify optimal attacks. That setup allows adversaries to identify attacks that are most impactful when applied on the system for the first time, before the system operators become aware of the manipulations. In this work, we investigate if constrained attackers without detailed system knowledge and simulators can identify comparable attacks. In particular, the attacker only requires abstract knowledge on general information flow in the plant, instead of precise algorithms, operating parameters, process models, or simulators. We propose an approach that allows single-shot attacks, i.e., near-optimal attacks that are reliably shutting down a system on the first try. The approach is applied and validated on two use cases, and demonstrated to achieve comparable results to prior work, which relied on detailed system information and simulations.