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1. Towards Efficient Auditing for Real-Time Systems

   https://doi.org/10.1007/978-3-031-17143-7_30  

   Bansal, Ayoosh and (October 2022, European Symposium on Research in Computer Security)

   System auditing is a powerful tool that provides insight into the nature of suspicious events in computing systems, allowing machine operators to detect and subsequently investigate security incidents. While auditing has proven invaluable to the security of traditional computers, existing audit frameworks are rarely designed with consideration for Real-Time Systems (RTS). The transparency provided by system auditing would be of tremendous benefit in a variety of security-critical RTS domains, (e.g., autonomous vehicles); however, if audit mechanisms are not carefully integrated into RTS, auditing can be rendered ineffectual and violate the real-world temporal requirements of the RTS. In this paper, we demonstrate how to adapt commodity audit frameworks to RTS. Using Linux Audit as a case study, we first demonstrate that the volume of audit events generated by commodity frameworks is unsustainable within the temporal and resource constraints of real-time (RT) applications. To address this, we present Ellipsis, a set of kernel-based reduction techniques that leverage the periodic repetitive nature of RT applications to aggressively reduce the costs of system-level auditing. Ellipsis generates succinct descriptions of RT applications’ expected activity while retaining a detailed record of unexpected activities, enabling analysis of suspicious activity while meeting temporal constraints. Our evaluation of Ellipsis, using ArduPilot (an open-source autopilot application suite) demonstrates up to 93% reduction in audit log generation. 

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2. System Auditing for Real-Time Systems

   https://doi.org/10.1145/3625229  

   Bansal, Ayoosh; Kandikuppa, Anant; Hasan, Monowar; Chen, Chien-Ying; Bates, Adam; Mohan, Sibin (November 2023, ACM Transactions on Privacy and Security)

   System auditing is an essential tool for detecting malicious events and conducting forensic analysis. Although used extensively on general-purpose systems, auditing frameworks have not been designed with consideration for the unique constraints and properties of Real-Time Systems (RTS). System auditing could provide tremendous benefits for security-critical RTS. However, a naive deployment of auditing on RTS could violate the temporal requirements of the system while also rendering auditing incomplete and ineffectual. To ensure effective auditing that meets the computational needs of recording complete audit information while adhering to the temporal requirements of the RTS, it is essential to carefully integrate auditing into the real-time (RT) schedule. This work adapts the Linux Audit framework for use in RT Linux by leveraging the common properties of such systems, such as special purpose and predictability.Ellipsis, an efficient system for auditing RTS, is devised that learns the expected benign behaviors of the system and generates succinct descriptions of the expected activity. Evaluations using varied RT applications show thatEllipsisreduces the volume of audit records generated during benign activity by up to 97.55% while recording detailed logs for suspicious activities. Empirical analyses establish that the auditing infrastructure adheres to the properties of predictability and isolation that are important to RTS. Furthermore, the schedulability of RT tasksets under audit is comprehensively analyzed to enable the safe integration of auditing in RT task schedules. 

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3. On the Forensic Validity of Approximated Audit Logs

   https://doi.org/10.1145/3427228.3427272  

   Michael, Noor; Mink, Jaron; Liu, Jason; Gaur, Sneha; Hassan, Wajih Ul; Bates, Adam (December 2020, Annual Computer Security Applications Conference)

   null (Ed.)

   Auditing is an increasingly essential tool for the defense of computing systems, but the unwieldy nature of log data imposes significant burdens on administrators and analysts. To address this issue, a variety of techniques have been proposed for approximating the contents of raw audit logs, facilitating efficient storage and analysis. However, the security value of these approximated logs is difficult to measure—relative to the original log, it is unclear if these techniques retain the forensic evidence needed to effectively investigate threats. Unfortunately, prior work has only investigated this issue anecdotally, demonstrating sufficient evidence is retained for specific attack scenarios. In this work, we address this gap in the literature through formalizing metrics for quantifying the forensic validity of an approximated audit log under differing threat models. In addition to providing quantifiable security arguments for prior work, we also identify a novel point in the approximation design space—that log events describing typical (benign) system activity can be aggressively approximated, while events that encode anomalous behavior should be preserved with lossless fidelity. We instantiate this notion of Attack-Preserving forensic validity in LogApprox, a new approximation technique that eliminates the redundancy of voluminous file I/O associated with benign process activities. We evaluate LogApprox alongside a corpus of exemplar approximation techniques from prior work and demonstrate that LogApprox achieves comparable log reduction rates while retaining 100% of attack-identifying log events. Additionally, we utilize this evaluation to illuminate the inherent trade-off between performance and utility within existing approximation techniques. This work thus establishes trustworthy foundations for the design of the next generation of efficient auditing frameworks. 

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4. Towards Scalable Cluster Auditing through Grammatical Inference over Provenance Graphs

   Hassan, Wajih Ul; Aguse, Lemay; Aguse, Nuraini; Bates, Adam; Moyer, Thomas (February 2018, Network and Distributed Systems Security Symposium)

   Investigating the nature of system intrusions in large distributed systems remains a notoriously difficult challenge. While monitoring tools (e.g., Firewalls, IDS) provide preliminary alerts through easy-to-use administrative interfaces, attack reconstruction still requires that administrators sift through gigabytes of system audit logs stored locally on hundreds of machines. At present, two fundamental obstacles prevent synergy between system-layer auditing and modern cluster monitoring tools: 1) the sheer volume of audit data generated in a data center is prohibitively costly to transmit to a central node, and 2) system- layer auditing poses a “needle-in-a-haystack” problem, such that hundreds of employee hours may be required to diagnose a single intrusion. This paper presents Winnower, a scalable system for audit-based cluster monitoring that addresses these challenges. Our key insight is that, for tasks that are replicated across nodes in a distributed application, a model can be defined over audit logs to succinctly summarize the behavior of many nodes, thus eliminating the need to transmit redundant audit records to a central monitoring node. Specifically, Winnower parses audit records into provenance graphs that describe the actions of individual nodes, then performs grammatical inference over individual graphs using a novel adaptation of Deterministic Finite Automata (DFA) Learning to produce a behavioral model of many nodes at once. This provenance model can be efficiently transmitted to a central node and used to identify anomalous events in the cluster. We have implemented Winnower for Docker Swarm container clusters and evaluate our system against real-world applications and attacks. We show that Winnower dramatically reduces storage and network overhead associated with aggregating system audit logs, by as much as 98%, without sacrificing the important information needed for attack investigation. Winnower thus represents a significant step forward for security monitoring in distributed systems. 

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5. Runtime Analysis of Whole-System Provenance

   https://doi.org/10.1145/3243734.3243776  

   Pasquier, Thomas; Han, Xueyuan; Moyer, Thomas; Bates, Adam; Hermant, Olivier; Eyers, David; Bacon, Jean; Seltzer, Margo (October 2018, 2018 ACM SIGSAC Conference on Computer and Communications Security)

   Identifying the root cause and impact of a system intrusion remains a foundational challenge in computer security. Digital provenance provides a detailed history of the flow of information within a computing system, connecting suspicious events to their root causes. Although existing provenance-based auditing techniques provide value in forensic analysis, they assume that such analysis takes place only retrospectively. Such post-hoc analysis is insufficient for realtime security applications; moreover, even for forensic tasks, prior provenance collection systems exhibited poor performance and scalability, jeopardizing the timeliness of query responses. We present CamQuery, which provides inline, realtime provenance analysis, making it suitable for implementing security applications. CamQuery is a Linux Security Module that offers support for both userspace and in-kernel execution of analysis applications. We demonstrate the applicability of CamQuery to a variety of runtime security applications including data loss prevention, intrusion detection, and regulatory compliance. In evaluation, we demonstrate that CamQuery reduces the latency of realtime query mechanisms, while imposing minimal overheads on system execution. CamQuery thus enables the further deployment of provenance-based technologies to address central challenges in computer security. 

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