Provenance Analysis involves tracing and documenting the origin, history, and transformation of data or artifacts. It is particularly relevant in fields such as data science, art authentication, and geological studies. Provenance analysis aims to provide transparency and assurance about the authenticity, quality, and reliability of data or objects by revealing their lineage and any changes they may have undergone.

Posts

You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis

/in /by

To subvert recent advances in perimeter and host security, the attacker community has developed and employed various attack vectors to make malware much more stealthy than before to penetrate the target system and prolong its presence. The advanced malware, or stealthy malware, impersonates or abuses benign applications and legitimate system tools to minimize its footprints in the target system. One example of such stealthy malware is fileless malware, which resides its malicious logic mostly in the memory of well-trusted processes. It is difficult for traditional detection tools, such as malware scanners, to detect it, as the malware normally does not expose its malicious payload in a file and hides its malicious behaviors among the benign behaviors of the processes.In this paper, we present PROVDETECTOR, a provenance-based approach for detecting stealthy malware. The intuition behind PROVDETECTOR is that although a stealthy malware may impersonate or abuse a benign process, it still exposes its malicious behaviors in the OS (operating system) level provenance. Based on this intuition, PROVDETECTOR first employs a novel selection algorithm to identify possibly malicious parts in the OS level provenance data of a process. Then, it applies a neural embedding and machine learning pipeline to automatically detect any behavior that deviates significantly from normal behaviors. We evaluate our approach on a large provenance dataset from an enterprise network and demonstrate that it achieves very high detection performance (an average F1 score of 0.974) of stealthy malware. Further, we conduct thorough interpretability studies to understand the internals of the learned machine learning models.

Read more

NODOZE: Combatting Threat Alert Fatigue with Automated Provenance Triage

/in /by

Large enterprises are increasingly relying on threat detection softwares (e.g., Intrusion Detection Systems) to allow them to spot suspicious activities. These softwares generate alerts which must be investigated by cyber analysts to figure out if they are true attacks. Unfortunately, in practice, there are more alerts than cyber analysts can properly investigate. This leads to a “threat alert fatigue” or information overload problem where cyber analysts miss true attack alerts in the noise of false alarms.In this paper, we present NoDoze to combat this challenge using contextual and historical information of generated threat alert in an enterprise. NoDoze first generates a causal dependency graph of an alert event. Then, it assigns an anomaly score to each event in the dependency graph based on the frequency with which related events have happened before in the enterprise. NoDoze then propagates those scores along the edges of the graph using a novel network diffusion algorithm and generates a subgraph with an aggregate anomaly score which is used to triage alerts. Evaluation on our dataset of 364 threat alerts shows that NoDoze decreases the volume of false alarms by 86%, saving more than 90 hours of analysts’ time, which was required to investigate those false alarms. Furthermore, NoDoze generated dependency graphs of true alerts are 2 orders of magnitude smaller than those generated by traditional tools without sacrificing the vital information needed for the investigation. Our system has a low average runtime overhead and can be deployed with any threat detection software.

Read more