Dynamic Information Flow Tracking on Multicores

Dynamic Information Flow Tracking (DIFT) is a promising technique for detecting software attacks. Due to the computationally intensive nature of the technique, prior efficient implementations [21, 6] rely on specialized hardware support whose only purpose is to enable DIFT. Alternatively, prior software implementations are either too slow [17, 15] resulting in execution time increases as much as four fold for SPEC integer programs or they are not transparent [31] requiring source code modifications. In this paper, we propose the use of chip multiprocessors (CMP) to perform DIFT transparently and efficiently. We spawn a helper thread that is scheduled on a separate core and is only responsible for performing information flow tracking operations. This entails the communication of registers and flags between the main and helper threads. We explore software (shared memory) and hardware (dedicated interconnect) approaches to enable this communication. Finally, we propose a novel application of the DIFT infrastructure where, in addition to the detection of the software attack, DIFT assists in the process of identifying the cause of the bug in the code that enabled the exploit in the first place. We conducted detailed simulations to evaluate the overhead for performing DIFT and found that to be 48 % for SPEC integer programs

Exploring the Effectiveness of Transit Security Awareness Campaigns in the San Francisco Bay Area, Research Report 09-19

Public involvement in alerting officials of suspicious and potentially harmful activity is critical to the overall security of a transit system. As part of an effort to get passengers and the public involved, many transit agencies have security awareness campaigns. The objective of this research is to learn how transit agencies seek to make security awareness campaigns effective and explore how they measure the effectiveness of such campaigns, if at all. This research project includes data from case studies of five major agencies that provide transit service in the San Francisco Bay Area region. The case study data are comprised of descriptions of the types of security awareness campaigns the agencies have implemented, the goals of the campaigns, and how they seek to make their campaigns effective, as well as whether and how these agencies measure and determine the effectiveness of their campaigns. A positive finding of this research is the consistency with which Bay Area transit organizations address the need for passenger awareness as part of their overall security program. However, none of the five agencies analyzed for this study measures the effectiveness of their campaigns. Whereas they all have a similar goal—to increase passenger awareness about security issues—little evidence exists confirming to what extent they are achieving this goal. The paper concludes with suggestions for using outcome measurements to provide a reasonable indication of a campaign’s effectiveness by capturing the public’s response to a campaign

Quire: Lightweight Provenance for Smart Phone Operating Systems

Smartphone apps often run with full privileges to access the network and sensitive local resources, making it difficult for remote systems to have any trust in the provenance of network connections they receive. Even within the phone, different apps with different privileges can communicate with one another, allowing one app to trick another into improperly exercising its privileges (a Confused Deputy attack). In Quire, we engineered two new security mechanisms into Android to address these issues. First, we track the call chain of IPCs, allowing an app the choice of operating with the diminished privileges of its callers or to act explicitly on its own behalf. Second, a lightweight signature scheme allows any app to create a signed statement that can be verified anywhere inside the phone. Both of these mechanisms are reflected in network RPCs, allowing remote systems visibility into the state of the phone when an RPC is made. We demonstrate the usefulness of Quire with two example applications. We built an advertising service, running distinctly from the app which wants to display ads, which can validate clicks passed to it from its host. We also built a payment service, allowing an app to issue a request which the payment service validates with the user. An app cannot not forge a payment request by directly connecting to the remote server, nor can the local payment service tamper with the request

Progger: an efficient, tamper-evident kernel-space logger for cloud data provenance tracking

Cloud data provenance, or "what has happened to my data in the cloud", is a critical data security component which addresses pressing data accountability and data governance issues in cloud computing systems. In this paper, we present Progger (Provenance Logger), a kernel-space logger which potentially empowers all cloud stakeholders to trace their data. Logging from the kernel space empowers security analysts to collect provenance from the lowest possible atomic data actions, and enables several higher-level tools to be built for effective end-to-end tracking of data provenance. Within the last few years, there has been an increasing number of proposed kernel space provenance tools but they faced several critical data security and integrity problems. Some of these prior tools' limitations include (1) the inability to provide log tamper-evidence and prevention of fake/manual entries, (2) accurate and granular timestamp synchronisation across several machines, (3) log space requirements and growth, and (4) the efficient logging of root usage of the system. Progger has resolved all these critical issues, and as such, provides high assurance of data security and data activity audit. With this in mind, the paper will discuss these elements of high-assurance cloud data provenance, describe the design of Progger and its efficiency, and present compelling results which paves the way for Progger being a foundation tool used for data activity tracking across all cloud systems