We conducted three exploratory empirical studies to validate our method. Our measurement method is agnostic to a software system's implementation language and is applicable to systems of all sizes we demonstrate our method by measuring the attack surfaces of small desktop applications and large enterprise systems implemented in C and Java. We formalize the notion of a system's attack surface and introduce an attack surface metric to measure the attack surface in a systematic manner. In this paper, we propose using a software system's attack surface measurement as an indicator of the system's security. Hence, the need for metrics is more pressing now due to a growing demand for secure software. At the same time, practical security metrics and measurements are essential for secure software development. Measurement of software security is a long-standing challenge to the research community. We also compare IoTInfer with two other state-of-the-art blackbox IoT device fuzzing tools and find that IoTInfer is better at eliciting different types of responses from the fuzzing targets. Our experimental results with a variety of IoT devices reveal that IoTInfer is efficient at generating meaningful test cases, some of which can expose previously unknown vulnerabilities or implementation deviations from protocol specifications. We implement IoTInfer for both Bluetooth and Telnet protocols, which are widely used by existing IoT devices. IoTInfer also applies clustering techniques to coarsen the FSM inferred when there are limited computational resources provisioned for fuzzing tests. Our method, which is called IoTInfer, balances exploration and exploitation by continuously monitoring how likely mutation of an input message leads to counterexamples conflicting with the prediction by the current FSM. In this work, we explore a new heuristic based on finite state machine (FSM) inference to guide generation of test cases for blackbox fuzzing tests of IoT network protocol implementations. The popularity of Internet of Things (IoT) devices calls for effective yet efficient methods to assess the security and resilience of IoT devices. We present experimental results illustrating how the proposed metric scales for graphs of realistic sizes, and illustrate its application to real‐world testbeds. Our analysis goes beyond the scope of traditional attack surface metrics, and considers the depth and implications of potential attacks, leading to the definition of a new family of metrics, which we refer to as attack volume metrics. In our work, building upon previous research on vulnerability metrics and on graphical models to capture such interdependencies, we propose a novel approach to evaluate the potential risk associated with exposed vulnerabilities by studying how the effect of each vulnerability exploit propagates through chains of dependencies. However, most approaches to tackle this problem have failed to consider the complex interdependencies that exist between the many components of a distributed system, its vulnerabilities, and its configuration parameters. We evaluate SCIBORG on an IoT testbed.įor more than a decade, the notion of attack surface has been used to define the set of vulnerable assets that an adversary may exploit to penetrate a system, and various metrics have been developed to quantify the extent of a system's attack surface. SCIBORG also provides supporting evidence for the proposed configuration changes. It formulates a Constraint Satisfaction Problem from the graph-based model and uses an SMT solver to find optimal configuration parameter values that minimize the impact of attacks while preserving system functionality. To address this gap, we present SCIBORG, a framework that improves the security posture of distributed systems by examining the impact of configuration changes across interdependent components using a graph-based model of the system and its vulnerabilities. Unfortunately, prior work on configuration errors has largely ignored the security impact of configurations of connected components. Given the growing scale of cyber systems, this task must be highly automated. Owners and operators must go beyond tuning parameters of individual components and consider the security implications of configuration changes on entire systems. Addressing security misconfiguration in complex distributed systems, such as networked Industrial Control Systems (ICS) and Internet of Things (IoT) is challenging.
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