Winterdongmu's Stars
WebGoat/WebGoat
WebGoat is a deliberately insecure application
LyleMi/Learn-Web-Hacking
Study Notes For Web Hacking / Web安全学习笔记
firmianay/CTF-All-In-One
CTF竞赛权威指南
Threekiii/Awesome-Redteam
一个攻防知识仓库 Red Teaming and Offensive Security
A-poc/BlueTeam-Tools
Tools and Techniques for Blue Team / Incident Response
awake1t/HackReport
渗透测试报告/资料文档/渗透经验文档/安全书籍
eastmountyxz/NetworkSecuritySelf-study
这是作者的系列网络安全自学教程，主要是关于网安工具和实践操作的在线笔记，希望对大家有所帮助，学无止境，加油。
imrk51/CEH-v11-Study-Guide
botesjuan/Burp-Suite-Certified-Practitioner-Exam-Study
Burp Suite Certified Practitioner Exam Study
chrislockard/api_wordlist
A wordlist of API names for web application assessments
rkhal101/Web-Security-Academy-Series
ReversingID/Awesome-Reversing
A curated list of awesome reverse engineering resources for various topics
danielpoliakov/lisa
Sandbox for automated Linux malware analysis.
malicialab/avclass
AVClass malware labeling tool
chuanconggao/PrefixSpan-py
The shortest yet efficient Python implementation of the sequential pattern mining algorithm PrefixSpan, closed sequential pattern mining algorithm BIDE, and generator sequential pattern mining algorithm FEAT.
falcosecurity/libs
libsinsp, libscap, the kernel module driver, and the eBPF driver sources
chhayac/SQL-hackerrank-problems
My solutions to various hacker-rank SQL problems using DB2 syntax
MalwareSamples/Linux-Malware-Samples
Linux Malware Sample Archive including various types of malicious ELF binaries and viruses. Be careful!
fidelity/seq2pat
[AAAI 2022] Seq2Pat: Sequence-to-Pattern Generation Library
vmwarecloudadvocacy/acme_fitness_demo
Deploys ACME Fitness application across different environments
shanesatterfield/hacker-rank
This is a repository of my solutions to hacker rank problems.
LoLei/spmf-py
Python SPMF Wrapper 🐍 🎁
VladimirAkopyan/IoTSimulator
IoT device Simulation using Kubernetes, with StatefulSets, Secrets, ConfigMap and .Net Core
shreyansh26/ELF-Miner
An implementation of the paper "ELF-Miner: Using Structural Knowledge and Data Mining Methods To Detect New (Linux) Malicious Executables"
subbukandula/Splunk
JulianFeinauer/iotdb-cluster-k8s-example
Simple Example how to run IoTDB as Cluster on Kubernetes
hamedhpajouh/IoTMalware
This project was conducted to create a very first malware dataset for IoT application
MahsaSinaei/Malware-detection-by-system-call-graph-using-Machine-Learning
Use a system call dependency graph to detect malware and analyze their behavior. The system calls are extracted and collected by Fredrickson and et al.[1] it contains two sets of benchmarks: the malware and the regular software set. The malware set comprises 2631 samples pre-classified into 48 families and 11 types. The regular software set comprises 35 samples. A dependency graph is built from these system calls and a set of features for each software is extracted to specify the software behavior. A feature selection method is implemented to reduce the number of features by clustering them. Machine learning algorithms such as Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machines, and Neural Networks are exploited to build two prediction models. The first model is a two-class model that classifies software into malware and regular software. The second model is a multi-class model, which identifies the type of malware, in addition to classifying the software to malware and regular software. [1] Matt Fredrikson, Somesh Jha, Mihai Christodorescu, Reiner Sailer, and Xifeng Yan. Synthesizing near-optimal malware specifications from suspicious behaviors. In Security and Privacy (SP), 2010 IEEE Symposium on, pages 45–60. IEEE, 2010.
iagox86/skullsecurity.org
Jekyll site for my blog
aleatha/provenance-conversion
Code to turn system call traces into a provenance graph.