samybtt's Stars
friendllcc/Malware-Detection-API-Sequence-Intrinsic-Features
ElNiak/BountyDrive
BountyDrive is a comprehensive tool designed for penetration testers and cybersecurity researchers. It integrates various modules for performing attacks (google dorking, sqli, xss), reporting, and managing VPN/proxy settings, making it an indispensable asset for any security professional.
pralab/secml_malware
Create adversarial attacks against machine learning Windows malware detectors
ElNiak/PANTHER-Ivy
IVy is a research tool intended to allow interactive development of protocols and their proofs of correctness and to provide a platform for developing and experimenting with automated proof techniques. In particular, IVy provides interactive visualization of automated proofs, and supports a use model in which the human protocol designer and the aut
ElNiak/cupp-rs
Common User Passwords Profiler (CUPP) in Rust
ElNiak/PANTHER
This tool presents a novel approach to bolstering network protocol verification by integrating the Shadow network simulator with the Ivy formal verification tool to check time properties. Furthermore, it extends Ivy’s capabilities with a dedicated time module, enabling the verification of complex quantitative-time properties.
senzee1984/InflativeLoading
Dynamically convert an unmanaged EXE or DLL file to PIC shellcode by prepending a shellcode stub.
nemesida-waf/waf-bypass
Check your WAF before an attacker does
aemmitt-ns/radius2
radius2 is a fast binary emulation and symbolic execution framework using radare2
seakkas/gnn-explainability-examples
m30m/gnn-explainability
ElNiak/PySSH3
Translation of SSH3 project (from commit c39bb79cdce479f6095ab154a32a168e14d73b57) to Python 3 library. Check the original project for more information !
ucsb-seclab/syml
flyingdoog/awesome-graph-explainability-papers
Papers about explainability of GNNs
FelixOpolka/Graph-Classification-Gaussian-Processes-via-Spectral-Features
Code for the UAI 2023 paper Graph Classification Gaussian Processes via Spectral Features
h0ru/AMSI-Reaper
officialarijit/DW-FedAvg
A Federated Learning based Android Malware Classification System
brechtvandervliet/ResistancePoisoningFederatedMalwareClassifier
Mobile devices contain highly sensitive data, making them an attractive target to attackers. As an Android malware classifier, LiM aims to tackle security issues while respecting the privacy of users by leveraging the power of federated learning. Compared to centralized ways of learning, the unique properties of federated learning open up new attack surfaces for adversaries. For instance, an adversary can attempt to let a targeted malicious app be misclassified as clean by sending poisoned model updates in the federation. This work builds on LiM with the aim of improving its resistance against these poisoning attacks. First, I formulate and test several targeted model update poisoning attacks. Depending on assumptions regarding the adversary's knowledge, the attacks are able to successfully compromise around 10 to 25\% of the honest client devices in the federation. Second, while most defenses result in a trade-off between improving resistance and maintaining performance, I propose a simple defense strategy that can never decrease the performance of the federation. Against a strong adversary, who has knowledge of the algorithm used to aggregate the model updates, the defense was mostly insufficient to prevent poisoning. In the presence of a more realistic adversary, the defense caused LiM to regain best-case performance, comparable to the performance in a scenario without adversary.
S2E/s2e-env
Your S2E project management tools. Visit https://s2e.systems/docs to get started.
RexYing/gnn-model-explainer
gnn explainer
diegovalsesia/ran-gnn-molpcba
RAN-GNN code for molpcba open graph benchmark
DMaroo/GhidRust
GhidRust: Rust decompiler plugin for Ghidra
csvl/SEMA
SEMA is based on angr, a symbolic execution engine used to extract API calls. Especially, we extend ANGR with strategies to create representative signatures based on System Call Dependency graph (SCDG). Those SCDGs can be exploited in machine learning modules to do classification/detection.
dsgiitr/graph_nets
PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
Octoberfest7/Inline-Execute-PE
Execute unmanaged Windows executables in CobaltStrike Beacons