Convolutional Neural Networks over Control Flow Graphs for Software Defect Prediction

Using deep learning to solve computer security challenges: a survey

“…So far, no works have realized Just-In-Time checking for program's control flow. In order to provide more insightful results, in this section, we try not to narrow down our focus on CFI detecting attacks at run-time, but to extend our scope to papers that take good use of control flow related data, combined with Deep Learning techniques (Phan et al 2017;Nguyen et al 2018). In one work, researchers used self-constructed instruction-level CFG to detect program defection (Phan et al 2017).…”

“…Therefore, they chose LSTM architecture to better learn the relationship between instructions. While in the other two papers (Phan et al 2017;Nguyen et al 2018), they trained CNN and directed graph-based CNN to extract information from control-flow graph and image, respectively.…”

“…In this section, we will review the very recent four representative works that use Deep Learning for memory forensics (Song et al 2018;Petrik et al 2018;Michalas and Murray 2017;Dai et al 2018). DeepMem (Song et al 2018) recognized the kernel objects from raw memory dumps by generating abstract representations of kernel objects with a graph-based Deep Learning approach.…”

“…DeepMem (Song et al 2018) recognized the kernel objects from raw memory dumps by generating abstract representations of kernel objects with a graph-based Deep Learning approach. MDMF (Petrik et al 2018) detected OS and architecture-independent malware from memory snapshots with several pre-processing techniques, domain unaware feature selection, and a suite of machine learning algorithms. MemTri (Michalas and Murray 2017) predicts the likelihood of criminal activity in a memory image using a Bayesian network, based on evidence data artefacts generated by several applications.…”

“…Among these four works (Song et al 2018;Petrik et al 2018;Michalas and Murray 2017;Dai et al 2018), two representative works (i.e., (Song et al 2018;Petrik et al 2018)) are already summarized phase-by-phase in Table 1. We direct interested readers to Table 2 for a concise overview of these two works.…”