lsjw's starred repositories
linux-exploit-suggester
Linux privilege escalation auditing tool
SteamTradingSiteTracker
Steam 挂刀行情站 —— 24小时自动更新的 BUFF & IGXE & C5 & UUYP 挂刀比例数据 | Track cheap Steam Community Market items on buff.163.com, igxe.cn, c5game.com and youpin898.com.
SUDO_KILLER
A tool designed to exploit a privilege escalation vulnerability in the sudo program on Unix-like systems. It takes advantage of a specific misconfiguration or flaw in sudo to gain elevated privileges on the system, essentially allowing a regular user to execute commands as the root user.
LinuxEelvation
Linux Eelvation(持续更新)
kernel-exploit-factory
Linux kernel CVE exploit analysis report and relative debug environment. You don't need to compile Linux kernel and configure your environment anymore.
metasploit-framework
Metasploit Framework
social-network-embeddings
Learned User Representations in Online Social Networks (Twitter) using Temporal Dynamics of Information Diffusion.
Temporal-Network-Embedding
The implementation that infers the temporal latent spaces for a sequence of dynamic graph snapshots
DynamicTriad
Dynamic Network Embedding by Modeling Triadic Closure Process
VAE-ContextAwareLinkPrediction
Networks Embedding (NE) plays a very important role in network analysis in real life. Most of the current Network Representation Learning (NRL) models only consider the structure information, and have static embeddings. However, the identical vertex can exhibit different characters when interacting with different vertices. In this paper, we propose a context-aware text-embedding model which seamlessly integrates the structure information and the text information of the vertex. We employ the Variational AutoEncoder (VAE) to statically obtain the textual information of each vertex and use mutual attention mechanism to dynamically assign the embeddings to a vertex according to different neighbors it interacts with. Comprehensive experiments were conducted on two publicly available link prediction datasets. Experimental results demonstrate that our model performs superior compared to baselines.