Biblio

Endpoint Detection and Response (EDR) tools provide visibility into sophisticated intrusions by matching system events against known adversarial behaviors. However, current solutions suffer from three challenges: 1) EDR tools generate a high volume of false alarms, creating backlogs of investigation tasks for analysts; 2) determining the veracity of these threat alerts requires tedious manual labor due to the overwhelming amount of low-level system logs, creating a "needle-in-a-haystack" problem; and 3) due to the tremendous resource burden of log retention, in practice the system logs describing long-lived attack campaigns are often deleted before an investigation is ever initiated.This paper describes an effort to bring the benefits of data provenance to commercial EDR tools. We introduce the notion of Tactical Provenance Graphs (TPGs) that, rather than encoding low-level system event dependencies, reason about causal dependencies between EDR-generated threat alerts. TPGs provide compact visualization of multi-stage attacks to analysts, accelerating investigation. To address EDR's false alarm problem, we introduce a threat scoring methodology that assesses risk based on the temporal ordering between individual threat alerts present in the TPG. In contrast to the retention of unwieldy system logs, we maintain a minimally-sufficient skeleton graph that can provide linkability between existing and future threat alerts. We evaluate our system, RapSheet, using the Symantec EDR tool in an enterprise environment. Results show that our approach can rank truly malicious TPGs higher than false alarm TPGs. Moreover, our skeleton graph reduces the long-term burden of log retention by up to 87%.

Software-defined networking (SDN) overcomes many limitations of traditional networking architectures because of its programmable and flexible nature. Security applications, for instance, can dynamically reprogram a network to respond to ongoing threats in real time. However, the same flexibility also creates risk, since it can be used against the network. Current SDN architectures potentially allow adversaries to disrupt one or more SDN system components and to hide their actions in doing so. That makes assurance and reasoning about past network events more difficult, if not impossible. In this paper, we argue that an SDN architecture must incorporate various notions of accountability for achieving systemwide cyber resiliency goals. We analyze accountability based on a conceptual framework, and we identify how that analysis fits in with the SDN architecture's entities and processes. We further consider a case study in which accountability is necessary for SDN network applications, and we discuss the limits of current approaches.

Defenders of enterprise networks have a critical need to quickly identify the root causes of malware and data leakage. Increasingly, USB storage devices are the media of choice for data exfiltration, malware propagation, and even cyber-warfare. We observe that a critical aspect of explaining and preventing such attacks is understanding the provenance of data (i.e., the lineage of data from its creation to current state) on USB devices as a means of ensuring their safe usage. Unfortunately, provenance tracking is not offered by even sophisticated modern devices. This work presents ProvUSB, an architecture for fine-grained provenance collection and tracking on smart USB devices. ProvUSB maintains data provenance by recording reads and writes at the block layer and reliably identifying hosts editing those blocks through attestation over the USB channel. Our evaluation finds that ProvUSB imposes a one-time 850 ms overhead during USB enumeration, but approaches nearly-bare-metal runtime performance (90% of throughput) on larger files during normal execution, and less than 0.1% storage overhead for provenance in real-world workloads. ProvUSB thus provides essential new techniques in the defense of computer systems and USB storage devices.