Networked control systems used in energy, manufacturing, and transportation combine large, vulnerable attack surfaces with far overprovisioned privileges. Often, compromising a single computer or user account is sufficient to give an attacker free reign over physical machinery. Significant reduction of attack surface size is an ongoing problem, so we shift our focus to reducing the privileges granted to system operators and embedded controllers. To this end, we introduce C2, an enforcement mechanism for policies governing the usage of electromechanical devices. In presenting C2, we address two basic problems: (i.) How should a policy for physical device usage be expressed and enforced? This is a challenging question, as the safe usage of physical devices is dependent on mechanical limitations and the behavior of nearby devices. (ii.) What actions should be taken if a physical machine is issued an operation that violates the policy? C2 takes measures to ensure unsafe behaviors are not caused when denying slightly erroneous yet legitimate operations. We evaluate C2 against six representative control systems, and show that it can efficiently perform policy checks with less than 3.7% overhead, while not introducing new unsafe behaviors into a control system.

The advanced metering infrastructure (AMI) is a crucial component of the smart grid, replacing traditional analog devices with computerized smart meters. Smart meters have not only allowed for efficient management of many end-users, but also have made AMI an attractive target for remote exploits and local physical tampering with the end goal of stealing energy. While smart meters posses multiple sensors and data sources that can indicate energy theft, in practice, the individual methods exhibit many false positives. In this paper, we present AMIDS, an AMI intrusion detection system that uses information fusion to combine the sensors and consumption data from a smart meter to more accurately detect energy theft. AMIDS combines meter audit logs of physical and cyber events with consumption data to more accurately model and detect theft-related behavior. Our experimental results on normal and anomalous load profiles show that AMIDS can identify energy theft efforts with high accuracy. Furthermore, AMIDS correctly identified legitimate load profile changes that more elementary analyses classified as malicious.

Data provenance---a record of the origin and evolution of data in a system---is a useful tool for forensic analysis. However, existing provenance collection mechanisms fail to achieve sufficient breadth or fidelity to provide a holistic view of a system's operation over time. We present Hi-Fi, a kernel-level provenance system which leverages the Linux Security Modules framework to collect high-fidelity whole-system provenance. We demonstrate that Hi-Fi is able to record a variety of malicious behavior within a compromised system. In addition, our benchmarks show the collection overhead from Hi-Fi to be less than 1% for most system calls and 3% in a representative workload, while simultaneously generating a system measurement that fully reflects system evolution. In this way, we show that we can collect broad, high-fidelity provenance data which is capable of supporting detailed forensic analysis.

Programmable Logic Controllers (PLCs) drive the behavior of industrial control systems according to uploaded programs. It is now known that PLCs are vulnerable to the uploading of malicious code that can have severe physical consequences. What is not understood is whether an adversary with no knowledge of the PLC's interface to the control system can execute a damaging, targeted, or stealthy attack against a control system using the PLC. In this paper, we present SABOT, a tool that automatically maps the control instructions in a PLC to an adversary-provided specification of the target control system's behavior. This mapping recovers sufficient semantics of the PLC's internal layout to instantiate arbitrary malicious controller code. This lowers the prerequisite knowledge needed to tailor an attack to a control system. SABOT uses an incremental model checking algorithm to map a few plant devices at a time, until a mapping is found for all adversary-specified devices. At this point, a malicious payload can be compiled and uploaded to the PLC. Our evaluation shows that SABOT correctly compiles payloads for all tested control systems when the adversary correctly specifies full system behavior, and for 4 out of 5 systems in most cases where there where unspecified features. Furthermore, SABOT completed all analyses in under 2 minutes.

Smart electric meters pose a substantial threat to the privacy of individuals in their own homes. Combined with non-intrusive load monitors, smart meter data can reveal precise home appliance usage information. An emerging solution to behavior leakage in smart meter measurement data is the use of battery-based load hiding. In this approach, a battery is used to store and supply power to home devices at strategic times to hide appliance loads from smart meters. A few such battery control algorithms have already been studied in the literature, but none have been evaluated from an adversarial point of view. In this paper, we first consider two well known battery privacy algorithms, Best Effort (BE) and Non-Intrusive Load Leveling (NILL), and demonstrate attacks that recover precise load change information, which can be used to recover appliance behavior information, under both algorithms. We then introduce a stepping approach to battery privacy algorithms that fundamentally differs from previous approaches by maximizing the error between the load demanded by a home and the external load seen by a smart meter. By design, precise load change recovery attacks are impossible. We also propose mutual-information based measurements to evaluate the privacy of different algorithms. We implement and evaluate four novel algorithms using the stepping approach, and show that under the mutual-information metrics they outperform BE and NILL.

ABSTRACT Smartphones are now ubiquitous. However, the security requirements of these relatively new systems and the applications they support are still being understood. As a result, the security infrastructure available in current smartphone operating systems is largely underdeveloped. In this paper, we consider the security requirements of smartphone applications and augment the existing Android operating system with a framework to meet them. We present Secure Application INTeraction (Saint), a modified infrastructure that governs install‐time permission assignment and their run‐time use as dictated by application provider policy. An in‐depth description of the semantics of application policy is presented. The architecture and technical detail of Saint are given, and areas for extension, optimization, and improvement are explored. We demonstrate through a concrete example and study of real‐world applications that Saint provides necessary utility for applications to assert and control the security decisions on the platform. Copyright © 2011 John Wiley & Sons, Ltd.

The smart grid introduces concerns for the loss of consumer privacy; recently deployed smart meters retain and distribute highly accurate profiles of home energy use. These profiles can be mined by Non Intrusive Load Monitors (NILMs) to expose much of the human activity within the served site. This paper introduces a new class of algorithms and systems, called Non Intrusive Load Leveling (NILL) to combat potential invasions of privacy. NILL uses an in-residence battery to mask variance in load on the grid, thus eliminating exposure of the appliance-driven information used to compromise consumer privacy. We use real residential energy use profiles to drive four simulated deployments of NILL. The simulations show that NILL exposes only 1.1 to 5.9 useful energy events per day hidden amongst hundreds or thousands of similar battery-suppressed events. Thus, the energy profiles exhibited by NILL are largely useless for current NILM algorithms. Surprisingly, such privacy gains can be achieved using battery systems whose storage capacity is far lower than the residence's aggregate load average. We conclude by discussing how the costs of NILL can be offset by energy savings under tiered energy schedules.

Portable storage devices, such as key-chain USB devices, are ubiquitous. These devices are often used with impunity, with users repeatedly using the same storage device in open computer laboratories, Internet cafes, and on office and home computers. Consequently, they are the target of malware that exploit the data present or use them as a means to propagate malicious software. This paper presents the Kells mobile storage system. Kells limits untrusted or unknown systems from accessing sensitive data by continuously validating the accessing host's integrity state. We explore the design and operation of Kells, and implement a proof-of-concept USB 2.0 storage device on experimental hardware. Our analysis of Kells is twofold. We first prove the security of device operation (within a freshness security parameter Δt) using the LS2 logic of secure systems. Second, we empirically evaluate the performance of Kells. These experiments indicate nominal overheads associated with host validation, showing a worst case throughput overhead of 1.22% for read operations and 2.78% for writes.

The advanced metering infrastructure (AMI) is revolutionizing electrical grids. Intelligent AMI "smart meters" report real time usage data that enables efficient energy generation and use. However, aggressive deployments are outpacing security efforts: new devices from a dizzying array of vendors are being introduced into grids with little or no understanding of the security problems they represent. In this paper we develop an archetypal attack tree approach to guide penetration testing across multiple-vendor implementations of a technology class. In this, we graft archetypal attack trees modeling broad adversary goals and attack vectors to vendor-specific concrete attack trees. Evaluators then use the grafted trees as a roadmap to penetration testing. We apply this approach within AMI to model attacker goals such as energy fraud and denial of service. Our experiments with multiple vendors generate real attack scenarios using vulnerabilities identified during directed penetration testing, e.g., manipulation of energy usage data, spoofing meters, and extracting sensitive data from internal registers. More broadly, we show how we can reuse efforts in penetration testing to efficiently evaluate the increasingly large body of AMI technologies being deployed in the field.

Portable storage devices, such as key-chain USB devices, are ubiquitous and used everywhere; users repeatedly use the same storage device in open computer laboratories, Internet cafes, and on office and home computers. Consequently, they are the target of malware that exploit the data present or use them as a means to propagate malicious software., e.g., Conficker and Agent.bz. We present the Kells mobile storage system, which limits untrusted or unknown systems from accessing sensitive data by continuously validating the accessing host's integrity state. We explore the design and operation of Kells, and implement a proof-of-concept USB 2.0 storage device of experimental hardware. Our experiments indicate nominal overheads associated with host validation, with a worst-case throughput overhead of 1.22% for reads and 2.78% for writes.

Storage is increasingly becoming a vector for data compromise. Solutions for protecting on-disk data confidentiality and integrity to date have been limited in their effectiveness. Providing authenticated encryption, or simultaneous encryption with integrity information, is important to protect data at rest. In this paper, we propose that disks augmented with non-volatile storage (e.g., hybrid hard disks) and cryptographic processors (e.g., FDE drives) may provide a solution for authenticated encryption, storing security metadata within the drive itself to eliminate dependences on other parts of the system. We augment the DiskSim simulator with a flash simulator to evaluate the costs associated with managing operational overheads. These experiments show that proper tuning of system parameters can eliminate many of the costs associated with managing security metadata, with less than a 2% decrease in IOPS versus regular disks.

Smartphones are now ubiquitous. However, the security requirements of these relatively new systems and the applications they support are still being understood. As a result, the security infrastructure available in current smartphone operating systems is largely underdeveloped. In this paper, we consider the security requirements of smartphone applications and augment the existing Android operating system with a framework to meet them. We present Secure Application INTeraction (Saint), a modified infrastructure that governs install-time permission assignment and their run-time use as dictated by application provider policy. An in-depth description of the semantics of application policy is presented. The architecture and technical detail of Saint is given, and areas for extension, optimization, and improvement explored. As we show through concrete example, Saint provides necessary utility for applications to assert and control the security decisions on the platform.

Side channels are typically viewed as attacks that leak cryptographic keys during cryptographic algorithm processing, by observation of system side effects. In this paper, we present new side channels that leak password information during X Windows keyboard processing of password input. Keylogging is one approach for stealing passwords, but current keylogging techniques require special hardware or privileged processes. However, we have found that the unprivileged operation of modifying the user key mappings for X Windows clients enables a side channel sufficient for unprivileged processes to steal that user's passwords, even enabling the attacker to gain root access via sudo. We successfully tested one version on Linux 2.6; we were able to obtain a high degree of control over the scheduler, and thus we can obtain accurate timing information. A second version (logon detection) works without depending on accurate clocks or cache effects. Thus, in addition to demonstrating new side channels, we show that (a) side channels cannot be eliminated by removing accurate clocks or hardware cache mechanisms (b) side channels are of continued concern for computer security as well as cryptographic processing.

Rootkits are now prevalent in the wild. Users affected by rootkits are subject to the abuse of their data and resources, often unknowingly. Suchmalware becomes even more dangerous when it is persistent-infected disk images allow the malware to exist across reboots and prevent patches or system repairs from being successfully applied. In this paper, we introduce rootkit-resistant disks (RRD) that label all immutable system binaries and configuration files at installation time. During normal operation, the disk controller inspects all write operations received from the host operating system and denies those made for labeled blocks. To upgrade, the host is booted into a safe state and system blocks can only be modified if a security token is attached to the disk controller. By enforcing immutability at the disk controller, we prevent a compromised operating system from infecting its on-disk image.