439 research outputs found
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
Ellipsis: Towards Efficient System Auditing for Real-Time Systems
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.Comment: Extended version of a paper accepted at ESORICS 202
Combatting Advanced Persistent Threat via Causality Inference and Program Analysis
Cyber attackers are becoming more and more sophisticated. In particular, Advanced Persistent Threat (APT) is a new class of attack that targets a specifc organization and compromises systems over a long time without being detected. Over the years, we have seen notorious examples of APTs including Stuxnet which disrupted Iranian nuclear centrifuges and data breaches affecting millions of users. Investigating APT is challenging as it occurs over an extended period of time and the attack process is highly sophisticated and stealthy. Also, preventing APTs is diffcult due to ever-expanding attack vectors.
In this dissertation, we present proposals for dealing with challenges in attack investigation. Specifcally, we present LDX which conducts precise counter-factual causality inference to determine dependencies between system calls (e.g., between input and output system calls) and allows investigators to determine the origin of an attack (e.g., receiving a spam email) and the propagation path of the attack, and assess the consequences of the attack. LDX is four times more accurate and two orders of magnitude faster than state-of-the-art taint analysis techniques. Moreover, we then present a practical model-based causality inference system, MCI, which achieves precise and accurate causality inference without requiring any modifcation or instrumentation in end-user systems.
Second, we show a general protection system against a wide spectrum of attack vectors and methods. Specifcally, we present A2C that prevents a wide range of attacks by randomizing inputs such that any malicious payloads contained in the inputs are corrupted. The protection provided by A2C is both general (e.g., against various attack vectors) and practical (7% runtime overhead)
POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat Hunting
Cyber threat intelligence (CTI) is being used to search for indicators of
attacks that might have compromised an enterprise network for a long time
without being discovered. To have a more effective analysis, CTI open standards
have incorporated descriptive relationships showing how the indicators or
observables are related to each other. However, these relationships are either
completely overlooked in information gathering or not used for threat hunting.
In this paper, we propose a system, called POIROT, which uses these
correlations to uncover the steps of a successful attack campaign. We use
kernel audits as a reliable source that covers all causal relations and
information flows among system entities and model threat hunting as an inexact
graph pattern matching problem. Our technical approach is based on a novel
similarity metric which assesses an alignment between a query graph constructed
out of CTI correlations and a provenance graph constructed out of kernel audit
log records. We evaluate POIROT on publicly released real-world incident
reports as well as reports of an adversarial engagement designed by DARPA,
including ten distinct attack campaigns against different OS platforms such as
Linux, FreeBSD, and Windows. Our evaluation results show that POIROT is capable
of searching inside graphs containing millions of nodes and pinpoint the
attacks in a few minutes, and the results serve to illustrate that CTI
correlations could be used as robust and reliable artifacts for threat hunting.Comment: The final version of this paper is going to appear in the ACM SIGSAC
Conference on Computer and Communications Security (CCS'19), November 11-15,
2019, London, United Kingdo
Efficient system auditing for real-time systems
Auditing is a powerful tool that provides machine operators with the mechanisms to observe, and glean insights from, generic computing systems. The information obtained by auditing systems can be used to detect and explain suspicious activity, from fault/error diagnosis to intrusion detection and forensics after security incidents. While such mechanisms would be beneficial for Real-Time Systems (RTS), existing audit frameworks are rarely designed for this domain. If audit mechanisms are not carefully integrated into real-time operating systems, they can negatively impact the temporal constraints of RTS. In this paper, we demonstrate how to apply commodity audit frameworks to real-time systems. We design novel kernel-based reduction techniques that leverage the periodic, repetitive, nature of real-time (RT) applications to aggressively reduce the costs/overheads of a system-level auditing, viz. Linux Audit (a popular open source audit framework). This is coupled with a rigorous analysis to understand the conflicts between the temporal requirements of RT applications and the audit subsystem. Our approach, Ellipsis, generates succinct behaviors of RT application and retains a lossless record of process activity, enabling analysis/detection of unexpected activity while meeting temporal constraints. Our evaluation of Ellipsis, using ArduPilot (an open-source autopilot application suite) and synthetically generated tasksets, demonstrates up to 93% reduction in audit event generation
Investigating system intrusions with data provenance analytics
To aid threat detection and investigation, enterprises are increasingly relying on commercially available security solutions, such as Intrusion Detection Systems (IDS) and Endpoint Detection and Response (EDR) tools. These security solutions first collect and analyze audit logs throughout the enterprise and then generate threat alerts when suspicious activities occur. Later, security analysts investigate those threat alerts to separate false alarms from true attacks by extracting contextual history from the audit logs, i.e., the trail of events that caused the threat alert.
Unfortunately, investigating threats in enterprises is a notoriously difficult task, even for expert analysts, due to two main challenges. First, existing enterprise security solutions are optimized to miss as few threats as possible â as a result, they generate an overwhelming volume of false alerts, creating a backlog of investigation tasks. Second, modern computing systems are operationally complex that produce an enormous volume of audit logs per day, making it difficult to correlate events for threats that span across multiple processes, applications, and hosts.
In this dissertation, I propose leveraging data provenance analytics to address the challenges mentioned above. I present five provenance-based techniques that enable system defenders to effectively and efficiently investigate malicious behaviors in enterprise settings. First, I present NoDoze, an alert triage system that automatically prioritizes generated alerts based on their anomalous contextual history. Following that, RapSheet brings benefits of data provenance to commercial EDR tools and provides compact visualization of multi-stage attacks to system defenders. Swift then realized a provenance graph database that generates contextual history around generated alerts in real-time even when analyzing audit logs containing tens of millions of events. Finally, OmegaLog and Zeek Agent introduced the vision of universal provenance analysis, which unifies all forensically relevant provenance information on the system regardless of their layer of origin, improving investigation capabilities
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