Program Behavior Modeling involves creating abstractions or representations of how a computer program behaves during its execution. This modeling can encompass various aspects, including the sequence of instructions executed, the flow of data, resource utilization, and interactions with the environment. The goal is to create a high-level understanding or representation of how the program operates, facilitating analysis, optimization, and validation of software systems. Program behavior modeling is often used in areas such as performance analysis, debugging, and software verification.


Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification

Attentional Heterogeneous Graph Neural Network:Application to Program Reidentfication Program or process is an integral part of almost every IT/OT system. Can we trust the identity/ID (e.g., executable name) of the program? To avoid detection, malware may disguise itself using the ID of a legitimate program, and a system tool (e.g., PowerShell) used by the attackers may have the fake ID of another common software, which is less sensitive. However, existing intrusion detection techniques often overlook this critical program reidentification problem (i.e., checking the program’s identity). In this paper, we propose an attentional heterogeneous graph neural network model (DeepHGNN) to verify the program’s identity based on its system behaviors. The key idea is to leverage the representation learning of the heterogeneous program behavior graph to guide the reidentification process. We formulate the program reidentification as a graph classification problem and develop an effective attentional heterogeneous graph embedding algorithm to solve it. Extensive experiments — using real-world enterprise monitoring data and real attacks — demonstrate the effectiveness of DeepHGNN across multiple popular metrics and the robustness to the normal dynamic changes like program version upgrades.