Biblio

We introduce a novel framework for the stability analysis of discrete-time linear switching systems with switching sequences constrained by an automaton. The key element of the framework is the algebraic concept of multinorm, which associates a different norm per node of the automaton, and allows to exactly characterize stability. Building upon this tool, we develop the first arbitrarily accurate approximation schemes for estimating the constrained joint spectral radius ρˆ, that is the exponential growth rate of a switching system with constrained switching sequences. More precisely, given a relative accuracy r > 0, the algorithms compute an estimate of ρˆ within the range [ ˆρ, (1+r)ρˆ]. These algorithms amount to solve a well defined convex optimization program with known time-complexity, and whose size depends on the desired relative accuracy r > 0.

Given the ever increasing number of research tools to automatically generate inputs to test Android applications (or simply apps), researchers recently asked the question "Are we there yet?" (in terms of the practicality of the tools). By conducting an empirical study of the various tools, the researchers found that Monkey (the most widely used tool of this category in industrial settings) outperformed all of the research tools in the study. In this paper, we present two signi cant extensions of that study. First, we conduct the rst industrial case study of applying Monkey against WeChat, a popular  messenger app with over 762 million monthly active users, and report the empirical ndings on Monkey's limitations in an industrial setting. Second, we develop a new approach to address major limitations of Monkey and accomplish substantial code-coverage improvements over Monkey. We conclude the paper with empirical insights for future enhancements to both Monkey and our approach.

Container-based network emulation offers high fidelity and a scalable testing environment to bridge the gap between research ideas and real-world network applications. However, containers take their notions of time from the physical system clock, and thus the time-stamped events from different containers are multiplexed to reflect the scheduling serialization by the Linux operating system. Conjoining the emulator and other simulators is also challenging due to the difficulties of synchronizing the virtual simulation clock with the physical system clock. Virtual time systems for network emulation shed light on both issues. In this paper, we develop a lightweight container-based virtual time system in Linux Kernel. We use time dilation to trade time with system resources by precisely scaling the time of interactions between containers and physical devices. We develop a time freezer to enable the precise pause and resume of an emulation experiment, which offers the virtual time support to interface with simulators for close synchronization. We integrate the virtual time system into a software-defined networking emulator, Mininet, and evaluate the system accuracy, scalability, and overhead. Finally, we use the virtual-time-enabled emulation testbed to conduct a case study of equal-cost multi-path routing protocol analysis in a data center network.

Code Hunt (https://www.codehunt.com/) from Microsoft Research is a web-based serious gaming platform being popularly used for various programming contests. In this paper, we demonstrate preliminary statistical analysis of a Code Hunt data set that contains the programs written by students (only) worldwide during a contest over 48 hours. There are 259 users, 24 puzzles (organized into 4 sectors), and about 13,000 programs submitted by these users. Our analysis results can help improve the creation of puzzles in a future contest.

We present ConVenus, a system that performs rapid congestion verification of network updates in softwaredefined networks. ConVenus is a lightweight middleware between the SDN controller and network devices, and is capable to intercept flow updates from the controller and verify whether the amount of traffic in any links and switches exceeds the desired capacity. To enable online verification, ConVenus dynamically identifies the minimum set of flows and switches that are affected by each flow update, and creates a compact network model. ConVenus uses a four-phase simulation algorithm to quickly compute the throughput of every flow in the network model and report network congestion. The experimental results demonstrate that ConVenus manages to verify 90% of the updates in a network consisting of over 500 hosts and 80 switches within 5 milliseconds.

SDN’s logically centralized control provides an insertion point for programming the network. While it is generally agreed that higherlevel abstractions are needed to make that programming easy, there is little consensus on what are the “right” abstractions. Indeed, as SDN moves beyond its initial specialized deployments to broader use cases, it is likely that network control applications will require diverse abstractions that evolve over time. To this end, we champion a perspective that SDN control fundamentally revolves around data representation. We discard any application-specific structure that might be outgrown by new demands. Instead, we adopt a plain data representation of the entire network — network topology, forwarding, and control applications — and seek a universal data language that allows application programmers to transform the primitive representation into any high-level representations presented to applications or network operators. Driven by this insight, we present a system, Ravel, that implements an entire SDN network control infrastructure within a standard SQL database. In Ravel, network abstractions take the form of user-defined SQL views expressed by SQL queries that can be added on the fly. A key challenge in realizing this approach is to orchestrate multiple simultaneous abstractions that collectively affect the same underlying data. To achieve this, Ravel enhances the database with novel data integration mechanisms that merge the multiple views into a coherent forwarding behavior. Moreover, Ravel is exposed to applications through the one simple, familiar and highly interoperable SQL interface. While this is an ambitious long-term goal, our prototype built on the PostgreSQL database exhibits promising performance even for large scale networks.

The human factor is often regarded as the weakest link in cybersecurity systems. The investigation of several security breaches reveals an important impact of human errors in exhibiting security vulnerabilities. Although security researchers have long observed the impact of human behavior, few improvements have been made in designing secure systems that are resilient to the uncertainties of the human element.

In this talk, we discuss several psychological theories that attempt to understand and influence the human behavior in the cyber world. Our goal is to use such theories in order to build predictive cyber security models that include the behavior of typical users, as well as system administrators. We then illustrate the importance of our approach by presenting a case study that incorporates models of human users. We analyze our preliminary results and discuss their challenges and our approaches to address them in the future.

This article describes our recent progress on the development of rigorous analytical metrics for assessing the threat-performance trade-off in control systems. Computing systems that monitor and control physical processes are now pervasive, yet their security is frequently an afterthought rather than a first-order design consideration. We investigate a rational basis for deciding—at the design level—how much investment should be made to secure the system.

Mobile malware attempts to evade detection during app analysis by mimicking security-sensitive behaviors of benign apps that provide similar functionality (e.g., sending SMS mes- sages), and suppressing their payload to reduce the chance of being observed (e.g., executing only its payload at night). Since current approaches focus their analyses on the types of security- sensitive resources being accessed (e.g., network), these evasive techniques in malware make differentiating between malicious and benign app behaviors a difficult task during app analysis. We propose that the malicious and benign behaviors within apps can be differentiated based on the contexts that trigger security- sensitive behaviors, i.e., the events and conditions that cause the security-sensitive behaviors to occur. In this work, we introduce AppContext, an approach of static program analysis that extracts the contexts of security-sensitive behaviors to assist app analysis in differentiating between malicious and benign behaviors. We implement a prototype of AppContext and evaluate AppContext on 202 malicious apps from various malware datasets, and 633 benign apps from the Google Play Store. AppContext correctly identifies 192 malicious apps with 87.7% precision and 95% recall. Our evaluation results suggest that the maliciousness of a security-sensitive behavior is more closely related to the intention of the behavior (reflected via contexts) than the type of the security-sensitive resources that the behavior accesses.

We present a controller synthesis algorithm for a discrete time reach-avoid problem in the presence of adversaries. Our model of the adversary captures typical malicious attacks en- visioned on cyber-physical systems such as sensor spoofing, controller corruption, and actuator intrusion. After formu- lating the problem in a general setting, we present a sound and complete algorithm for the case with linear dynamics and an adversary with a budget on the total L2-norm of its actions. The algorithm relies on a result from linear control theory that enables us to decompose and precisely compute the reachable states of the system in terms of a symbolic simulation of the adversary-free dynamics and the total uncertainty induced by the adversary. With this de- composition, the synthesis problem eliminates the universal quantifier on the adversary’s choices and the symbolic con- troller actions can be effectively solved using an SMT solver. The constraints induced by the adversary are computed by solving second-order cone programmings. The algorithm is later extended to synthesize state-dependent controller and to generate attacks for the adversary. We present prelimi- nary experimental results that show the effectiveness of this approach on several example problems.

This paper considers a decentralized switched control problem where exact conditions for controller synthesis are obtained in the form of semidefinite programming (SDP). The formulation involves a discrete-time switched linear plant that has a nested structure, and whose system matrices switch between a finite number of values according to finite-state automation. The goal of this paper is to synthesize a commensurately nested switched controller to achieve a desired level of 2-induced norm performance. The nested structures of both plant and controller are characterized by block lower-triangular system matrices. For this setup, exact conditions are provided for the existence of a finite path-dependent synthesis. These include conditions for the completion of scaling matrices obtained through an extended matrix completion lemma.When individual controller dimensions are chosen at least as large as the plant, these conditions reduce to a set of linear matrix inequalities. The completion lemma also provides an algorithm to complete closed-loop scaling matrices, leading to inequalities for  ontroller synthesis that are solvable either algebraically or numerically through SDP.

In distributed optimization and iterative consensus literature, a standard problem is for N agents to minimize a function f over a subset of Rⁿ, where the cost function is expressed as Σ fi . In this paper, we study the private distributed optimization (PDOP) problem with the additional requirement that the cost function of the individual agents should remain differentially private.  The adversary attempts to infer information about the private cost functions from the messages that the agents exchange. Achieving differential privacy requires that any change of an individual’s cost function only results in unsubstantial changes in the statistics of the messages. We propose a class of iterative algorithms for solving PDOP, which achieves differential privacy and convergence to the optimal value.  Our analysis reveals the dependence of the achieved accuracy and the privacy levels on the the parameters of the algorithm.

Many current VM monitoring approaches require guest OS modifications and are also unable to perform application level monitoring, reducing their value in a cloud setting. This paper introduces hprobes, a framework that allows one to dynamically monitor applications and operating systems inside a VM. The hprobe framework does not require any changes to the guest OS, which avoids the tight coupling of monitoring with its target. Furthermore, the monitors can be customized and enabled/disabled while the VM is running. To demonstrate the usefulness of this framework, we present three sample detectors: an emergency detector for a security vulnerability, an application watchdog, and an infinite-loop detector. We test our detectors on real applications and demonstrate that those detectors achieve an acceptable level of performance overhead with a high degree of flexibility.

It is critical to ensure that network policy remains consistent during state transitions. However, existing techniques impose a high cost in update delay, and/or FIB space. We propose the Customizable Consistency Generator (CCG), a fast and generic framework to support customizable consistency policies during network updates. CCG effectively reduces the task of synthesizing an update plan under the constraint of a given consistency policy to a verification problem, by checking whether an update can safely be installed in the network at a particular time, and greedily processing network state transitions to heuristically minimize transition delay. We show a large class of consistency policies are guaranteed by this greedy heuristic alone; in addition, CCG makes judicious use of existing heavier-weight network update mechanisms to provide guarantees when necessary. As such, CCG nearly achieves the “best of both worlds”: the efficiency of simply passing through updates in most cases, with the consistency guarantees of more heavyweight techniques. Mininet and physical testbed evaluations demonstrate CCG’s capability to achieve various types of consistency, such as path and bandwidth properties, with zero switch memory overhead and up to a 3× delay reduction compared to previous solutions.

This paper presents a factor graph based framework (namely AttackTagger) for high accuracy and preemptive detection of attacks. We use security logs on real-incidents that occurred over a six-year period at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign to evaluate AttackTagger. Our data consist of attacks that led directly to the target system being compromised, i.e., not detected in advance, either by the security analysts or by intrusion detection systems. AttackTagger detected 74 percent of attacks, a vast majority of them were detected before the system misuse. AttackTagger uncovered six hidden attacks that were not detected by security analysts.

This paper presents a factor graph based framework (namely AttackTagger)
for high accuracy and preemptive detection of attacks. We use security logs
on real-incidents that occurred over a six-year period at the National Cen-
ter for Supercomputing Applications (NCSA) at the University of Illinois at
Urbana-Champaign to evaluate AttackTagger. Our data consist of attacks
that led directly to the target system being compromised, i.e., not detected
in advance, either by the security analysts or by intrusion detection sys-
tems. AttackTagger detected 74 percent of attacks, a vast majority of them
were detected before the system misuse. AttackTagger uncovered six hidden
attacks that were not detected by security analysts.

Critical infrastructures, such as power grids and transportation systems, are increasingly using open networks for operation. The use of open networks poses many challenges for control systems.  The  classical  design  of  control systems  takes  into  account  modeling uncertainties  as  well  as  physical  disturbances,  providing  a  multitude  of control design methods such as robust control, adaptive control, and stochastic control. With the growing level of integration of control systems with new information technologies, modern control systems face uncertainties not only from the physical world but also from the cybercomponents of the system.  The vulnerabilities of the software deployed in the new control system infra- structure will expose the control system to many potential Game-Theoretic Methods for Robustness, Security, and Resilience of Cyberphysical Control Systems risks and threats from attackers. Exploitation of these vulnerabilities can lead to severe damage as has been reported in various news outlets [1], [2]. More recently, it has been reported in [3] and [4] that a computer worm, Stuxnet, was spread to target Siemens supervisory control and data acquisition (SCADA) systems that are configured to control and monitor specific industrial processes.

Sophistication and flexibility of software development make it easy to leave security vulnerabilities in software applications for attack- ers. It is critical to educate and train software engineers to avoid in- troducing vulnerabilities in software applications in the first place such as adopting secure coding mechanisms and conducting secu- rity testing. A number of websites provide training grounds to train people’s hacking skills, which are highly related to security test- ing skills, and train people’s secure coding skills. However, there exists no interactive gaming platform for instilling gaming aspects into the education and training of secure coding. To address this issue, we propose to construct secure coding duels in Code Hunt, a high-impact serious gaming platform released by Microsoft Re- search. In Code Hunt, a coding duel consists of two code segments: a secret code segment and a player-visible code segment. To solve a coding duel, a player iteratively modifies the player-visible code segment to match the functional behaviors of the secret code seg- ment. During the duel-solving process, the player is given clues as a set of automatically generated test cases to characterize sample functional behaviors of the secret code segment. The game aspect in Code Hunt is to recognize a pattern from the test cases, and to re-engineer the player-visible code segment to exhibit the expected behaviors. Secure coding duels proposed in this work are coding duels that are carefully designed to train players’ secure coding skills, such as sufficient input validation and access control.

Healthcare technology—sometimes called “healthtech” or “healthsec”—is enmeshed with security and privacy via usability, performance, and cost-effectiveness issues. It is multidisciplinary, distributed, and complex—and it also involves many competing stakeholders and interests. To address the problems that arise in such a multifaceted field—comprised of physicians, IT professionals, management information specialists, computer scientists, edical informaticists, and epidemiologists, to name a few—the Healthtech Declaration was initiated at the most recent USENIX Summit on Information Technologies for Health (Healthtech 2015) held in Washington, DC. This Healthtech Declaration includes an easy-touse—and easy-to-cite—checklist of key issues that anyone proposing a solution must consider (see “The Healthtech Declaration Checklist” sidebar). In this article, we provide the context and motivation for the declaration.