A new foundation for control-dependence and slicing for modern program structures.

The notion of control dependence underlies many program analysis and transformation techniques. Despite being widely used, existing definitions and approaches to calculating control dependence are difficult to apply directly to modern program structures because these make substantial use of exception processing and increasingly support reactive systems designed to run indefinitely. This article revisits foundational issues surrounding control dependence, and develops definitions and algorithms for computing several variations of control dependence that can be directly applied to modern program structures. To provide a foundation for slicing reactive systems, the article proposes a notion of slicing correctness based on weak bisimulation, and proves that some of these new definitions of control dependence generate slices that conform to this notion of correctness. This new framework of control dependence definitions, with corresponding correctness results, is even able to support programs with irreducible control flow graphs. Finally, a variety of properties show that the new definitions conservatively extend classic definitions. These new definitions and algorithms form the basis of the Indus Java slicer, a publicly available program slicer that has been implemented for full Java. Permission to make digital or hard copies part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +

Very static enforcement of dynamic policies

Security policies are naturally dynamic. Reflecting this, there has been a growing interest in studying information-flow properties which change during program execution, including concepts such as declassification, revocation, and role-change. A static verification of a dynamic information flow policy, from a semantic perspective, should only need to concern itself with two things: 1) the dependencies between data in a program, and 2) whether those dependencies are consistent with the intended flow policies as they change over time. In this paper we provide a formal ground for this intuition. We present a straightforward extension to the principal flow-sensitive type system introduced by Hunt and Sands (POPL’06, ESOP’11) to infer both end-to-end dependencies and dependencies at intermediate points in a program. This allows typings to be applied to verification of both static and dynamic policies. Our extension preserves the principal type system’s distinguishing feature, that type inference is independent of the policy to be enforced: a single, generic dependency analysis (typing) can be used to verify many different dynamic policies of a given program, thus achieving a clean separation between (1) and (2). We also make contributions to the foundations of dynamic information flow. Arguably, the most compelling semantic definitions for dynamic security conditions in the literature are phrased in the so-called knowledge-based style. We contribute a new definition of knowledge-based progress insensitive security for dynamic policies. We show that the new definition avoids anomalies of previous definitions and enjoys a simple and useful characterisation as a two-run style property

Static Safety for an Actor Dedicated Process Calculus by Abstract Interpretation

The actor model eases the definition of concurrent programs with non uniform behaviors. Static analysis of such a model was previously done in a data-flow oriented way, with type systems. This approach was based on constraint set resolution and was not able to deal with precise properties for communications of behaviors. We present here a new approach, control-flow oriented, based on the abstract interpretation framework, able to deal with communication of behaviors. Within our new analyses, we are able to verify most of the previous properties we observed as well as new ones, principally based on occurrence counting

Optimizing investments in cyber hygiene for protecting healthcare users

Cyber hygiene measures are often recommended for strengthening an organization’s security posture, especially for protecting against social engineering attacks that target the human element. However, the related recommendations are typically the same for all organizations and their employees, regardless of the nature and the level of risk for different groups of users. Building upon an existing cybersecurity investment model, this paper presents a tool for optimal selection of cyber hygiene safeguards, which we refer as the Optimal Safeguards Tool (OST). The model combines game theory and combinatorial optimization (0-1 Knapsack) taking into account the probability of each user group to being attacked, the value of assets accessible by each group, and the efficacy of each control for a particular group. The model considers indirect cost as the time employees could require for learning and trainning against an implemented control. Utilizing a game-theoretic framework to support the Knapsack optimization problem permits us to optimally select safeguards’ application levels minimizing the aggregated expected damage within a security investment budget. We evaluate OST in a healthcare domain use case. In particular, on the Critical Internet Security (CIS) Control group 17 for implementing security awareness and training programs for employees belonging to the ICT, clinical and administration personnel of a hospital. We compare the strategies implemented by OST against alternative common-sense defending approaches for three different types of attackers: Nash, Weighted and Opportunistic. Our results show that Nash defending strategies are consistently better than the competing strategies for all attacker types with a minor exception where the Nash defending strategy, for a specific game, performs at least as good as other common-sense approaches. Finally, we illustrate the alternative investment strategies on different Nash equilibria (called plans) and discuss the optimal choice using the framework of 0-1 Knapsack optimization

Correctly slicing extended finite state machines

We consider slicing extended finite state machines. Extended finite state machines (EFSMs) combine a finite state machine with a store and can model a range of computational phenomena, from high-level software to cyber-physical systems. EFSMs are essentially interactive, possibly non-terminating or with multiple exit states and may be nondeterministic, so standard techniques for slicing, developed for control flow graphs of programs with a functional semantics, are not immediately applicable. This paper addresses the various aspects of correctness for slicing of EFSMs, and provides syntactic criteria that we prove are sufficient for our proposed notions of semantic correctness. The syntactic criteria are based on the ``weak commitment" and ``strong commitment" properties highlighted by Danicic et alia. We provide polynomial-time algorithms to compute the least sets satisfying each of these two properties. We have conducted experiments using widely-studied benchmark and industrial EFSMs that compare our slicing algorithms with those using existing definitions of control dependence. We found that our algorithms produce the smallest average slices sizes, 21% of the original EFSMs when ``weak commitment'' is sufficient and 58% when ``strong commitment'' is needed (to preserve termination properties)