Search for: All records

Exploration of Internet of Things (IoT) security often focuses on threats posed by external and technically-skilled attackers. While it is important to understand these most extreme cases, it is equally important to understand the most likely risks of harm posed by smart device ownership. In this paper, we explore how smart devices are misused – used without permission in a manner that causes harm – by device owners’ everyday associates such as friends, family, and romantic partners. In a preliminary characterization survey (n = 100), we broadly capture the kinds of unauthorized use and misuse incidents participants have experienced or engaged in. Then, in a prevalence survey (n = 483), we assess the prevalence of these incidents in a demographically-representative population. Our findings show that unauthorized use of smart devices is widespread (experienced by 43% of participants), and that misuse is also common (experienced by at least 19% of participants). However, highly individual factors determine whether these unauthorized use events constitute misuse. Through a focus on everyday abuses rather than severe-but-unlikely attacks, this work sheds light on the most prevalent security and privacy threats faced by smart homeowners today. 

Reliable methods for host-layer intrusion detection remained an open problem within computer security. Recent research has recast intrusion detection as a provenance graph anomaly detection problem thanks to concurrent advancements in machine learning and causal graph auditing. While these approaches show promise, their robustness against an adaptive adversary has yet to be proven. In particular, it is unclear if mimicry attacks, which plagued past approaches to host intrusion detection, have a similar effect on modern graph-based methods. In this work, we reveal that systematic design choices have allowed mimicry attacks to continue to abound in provenance graph host intrusion detection systems (Prov-HIDS). Against a corpus of exemplar Prov-HIDS, we develop evasion tactics that allow attackers to hide within benign process behaviors. Evaluating against public datasets, we demonstrate that an attacker can consistently evade detection (100% success rate) without modifying the underlying attack behaviors. We go on to show that our approach is feasible in live attack scenarios and outperforms domain-general adversarial sample techniques. Through open sourcing our code and datasets, this work will serve as a benchmark for the evaluation of future Prov-HIDS. 

Auditing, a central pillar of operating system security, has only recently come into its own as an active area of public research. This resurgent interest is due in large part to the notion of data provenance, a technique that iteratively parses audit log entries into a dependency graph that explains the history of system execution. Provenance facilitates precise threat detection and investigation through causal analysis of sophisticated intrusion behaviors. However, the absence of a foundational audit literature, combined with the rapid publication of recent findings, makes it difficult to gain a holistic picture of advancements and open challenges in the area.In this work, we survey and categorize the provenance-based system auditing literature, distilling contributions into a layered taxonomy based on the audit log capture and analysis pipeline. Recognizing that the Reduction Layer remains a key obstacle to the further proliferation of causal analysis technologies, we delve further on this issue by conducting an ambitious independent evaluation of 8 exemplar reduction techniques against the recently-released DARPA Transparent Computing datasets. Our experiments uncover that past approaches frequently prune an overlapping set of activities from audit logs, reducing the synergistic benefits from applying them in tandem; further, we observe an inverse relation between storage efficiency and anomaly detection performance. However, we also observe that log reduction techniques are able to synergize effectively with data compression, potentially reducing log retention costs by multiple orders of magnitude. We conclude by discussing promising future directions for the field. 

System logs are invaluable to forensic audits, but grow so large that in practice fine-grained logs are quickly discarded – if captured at all – preventing the real-world use of the provenance-based investigation techniques that have gained popularity in the literature. Encouragingly, forensically-informed methods for reducing the size of system logs are a subject of frequent study. Unfortunately, many of these techniques are designed for offline reduction in a central server, meaning that the up-front cost of log capture, storage, and transmission must still be paid at the endpoints. Moreover, to date these techniques exist as isolated (and, often, closed-source) implementations; there does not exist a comprehensive framework through which the combined benefits of multiple log reduction techniques can be enjoyed. In this work, we present FAuST, an audit daemon for performing streaming audit log reduction at system endpoints. After registering with a log source (e.g., via Linux Audit’s audisp utility), FAuST incrementally builds an in-memory provenance graph of recent system activity. During graph construction, log reduction techniques that can be applied to local subgraphs are invoked immediately using event callback handlers, while techniques meant for application on the global graph are invoked in periodic epochs. We evaluate FAuST, loaded with eight different log reduction modules from the literature, against the DARPA Transparent Computing datasets. Our experiments demonstrate the efficient performance of FAuST and identify certain subsets of reduction techniques that are synergistic with one another. Thus, FAuST dramatically simplifies the evaluation and deployment of log reduction techniques. 

Information flow control is a canonical approach to access control in systems, allowing administrators to assure confidentiality and integrity through restricting the flow of data. Decentralized Information Flow Control (DIFC) harnesses application-layer semantics to allow more precise and accurate mediation of data. Unfortunately, past approaches to DIFC have depended on dedicated instrumentation efforts or developer buy-in. Thus, while DIFC has existed for decades, it has seen little-to-no adoption in commodity systems; the requirement for complete redesign or retrofitting of programs has proven too high a barrier. In this work, we make the surprising observation that developers have already unwittingly performed the instrumentation efforts required for DIFC — application event logging, a software development best practice used for telemetry and debugging, often contains the information needed to identify application-layer event processes that DIFC mediates. We present T-difc, a kernel-layer reference monitor framework that leverages the insights of application event logs to perform precise decentralized flow control. T-difc identifies and extracts these application events as they are created by monitoring application I/O to log files, then references an administrator-specified security policy to assign data labels and mediate the flow of data through the system. To our knowledge, T-difc is the first approach to DIFC that does not require developer support or custom instrumentation. In a survey of 15 popular open source applications, we demonstrate that T-difc works seamlessly on a variety of popular open source programs while imposing negligible runtime overhead on realistic policies and workloads. Thus, T-difc demonstrates a transparent and non-invasive path forward for the dissemination of decentralized information flow controls. 

Provenance-based causal analysis of audit logs has proven to be an invaluable method of investigating system intrusions. However, it also suffers from dependency explosion, whereby long-running processes accumulate many dependencies that are hard to unravel. Execution unit partitioning addresses this by segmenting dependencies into units of work, such as isolating the events that processed a single HTTP request. Unfortunately, we discover that current designs have a semantic gap problem due to how system calls and application log messages are used to infer complex internal program states. We demonstrate how attackers can modify existing code exploits to control event partitioning, breaking links in the attack and framing innocent users. We also show how our techniques circumvent existing program and log integrity defenses. We then propose a new design for execution unit partitioning that leverages additional runtime data to yield verified partitions that resist manipulation. Our design overcomes the technical challenges of minimizing additional overhead while accurately connecting low level code instructions to high level audit events, in part with the use of commodity hardware processor tracing. We implement a prototype of our design for Linux, MARSARA, and extensively evaluate it on 14 real-world programs, targeted with expertly crafted exploits. MARSARA's verified partitions successfully capture all the attack provenances while only reintroducing 2.82% of false dependencies, in the worst case, with an average overhead of 8.7%. Using a new metric called Partitioning Attack Surface, we show that MARSARA eliminates 47,642 more repartitioning gadgets per program than integrity defenses like CFI, demonstrating our prototype's effectiveness and the novelty of the attacks it prevents. 

Third-party security analytics allow companies to outsource threat monitoring tasks to teams of experts and avoid the costs of in-house security operations centers. By analyzing telemetry data from many clients these services are able to offer enhanced insights, identifying global trends and spotting threats before they reach most customers. Unfortunately, the aggregation that drives these insights simultaneously risks exposing sensitive client data if it is not properly sanitized and tracked. In this work, we present SCIFFS, an automated information flow monitoring framework for preventing sensitive data exposure in third-party security analytics platforms. SCIFFS performs decentralized information flow control over customer data it in a serverless setting, leveraging the innate polyinstantiated nature of serverless functions to assure precise and lightweight tracking of data flows. Evaluating SCIFFS against a proof-of-concept security analytics framework on the widely-used OpenFaaS platform, we demonstrate that our solution supports common analyst workflows data ingestion, custom dashboards, threat hunting) while imposing just 3.87% runtime overhead on event ingestion and the overhead on aggregation queries grows linearly with the number of records in the database (e.g., 18.75% for 50,000 records and 104.27% for 500,000 records) as compared to an insecure baseline. Thus, SCIFFS not only establishes a privacy-respecting model for third-party security analytics, but also highlights the opportunities for security-sensitive applications in the serverless computing model. 

Software-defined networking (SDN) has emerged as a flexible network architecture for central and programmatic control. Although SDN can improve network security oversight and policy enforcement, ensuring the security of SDN from sophisticated attacks is an ongoing challenge for practitioners. Existing network forensics tools attempt to identify and track such attacks, but holistic causal reasoning across control and data planes remains challenging. We present PicoSDN, a provenance-informed causal observer for SDN attack analysis. PicoSDN leverages fine-grained data and execution partitioning techniques, as well as a unified control and data plane model, to allow practitioners to efficiently determine root causes of attacks and to make informed decisions on mitigating them. We implement PicoSDN on the popular ONOS SDN controller. Our evaluation across several attack case studies shows that PicoSDN is practical for the identification, analysis, and mitigation of SDN attacks.