wumingruiye's Stars
dennybritz/reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
MorvanZhou/Reinforcement-learning-with-tensorflow
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
logpai/loghub
A large collection of system log datasets for AI-driven log analytics [ISSRE'23]
eastmountyxz/CSDNBlog-Security-Based
为了更好地管理博客文章，分享更好的知识，该系列资源为作者CSDN博客的备份文件。本资源为网络安全自学篇，包括作者安全工具利用、Web渗透、系统安全、CVE漏洞复现、安全论文及会议等知识，希望对您有所帮助！一起加油。
izikgo/AnomalyDetectionTransformations
A simple and effective method for single-class classification of images
aninstein/Network-Security-Situation-Awareness-System
综合了资产检测，主机扫描，流量分析等技术，通过这些技术取得网络资产，脆弱性，威胁等指标，从而根据这些指标计算出当前网络的网络安全态势。
joswr1ght/killerzee
KillerZee: Tools for Attacking and Evaluating Z-Wave Networks
Shauqi/Attack-and-Anomaly-Detection-in-IoT-Sensors-in-IoT-Sites-Using-Machine-Learning-Approaches
Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
abzcoding/aptdetector
Advanced Persistent Threat Detection Using Network Analysis
jones5am/Network_DOS_Attack_Detection
Using the 1998 DARPA Intrusion Detection Evaluation dataset I configured a Random Forest model for anomaly detection
jupadhya1/REINFORCEMENT-LEARNING
Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.
naling98/Joint-Chance-Constraints
We consider a n-player game where each player's strategy set contains some stochastic linear constraints. The existence of a Nash Equilibrium under certain conditions has been proved earlier. We further analyse this problem via a constructed example of a Cournot Competition among electricity firms over a Network. We run simulations of an Iterative scheme to find the Nash Equilibrium in this context and describe the results.
abdelrahmanikram/DDOS_Detection_Real_Time
A method for detection of DoS/DDoS attacks based on an evaluation of the incoming/outgoing packet volume ratio and its variance to the long-time ratio.
kaster-hn/Network-attack-defense-stochastic-differential-game-algorithm
mattjbourque/stochgame
A python module implementing policy improvement for stochastic games.
sdinuka/HSL-algorithm-for-APT-DIFT-games
ItzelOlivos/GOAL-CR
This project is concerned with computing channel access strategies that minimize the expected contention resolution time in single-hop random access networks. The uncertainty in the contention is addressed by modeling the problem as a partially observable stochastic game. A Reinforcement Learning method is implemented to find approximately optimal solutions. In addition, a novel algorithm was developed to compute optimal strategies in more efficient running time.
sdinuka/MA-ARNE-Algorithm-for-DIFT-APT-games
vhiefa/Network-Attack-Detection
In this project I used Matlab to develop 3 layers of Artificial Neural Network model from scratch. This model aims to detect attack of network . I performed undersampling to cope data imbalance. I evaluated using confusion metric.
adra1973/Fishery_Game
Program developed for calculation of a Nash Equilibrium in a Stochastic Dynamic Game
CavenaghiEmanuele/StageRL
dr-natetorious/TIM-7030-Managing_Risks_and_Privacy
Notes and papers on managing risk, security, and privacy in information systems.
fredwangwang/linear-programming-example
example problems to get started with LP
haider4445/AdvDRL
Nabiha-Nasir/Sufficient-Statistic-Based-Suboptimal-Strategies-in-Infinite-Horizon-Two-Player-Zero-Sum-Stochastic-
This repository contains all the codes requires to compute sufficient statistic based suboptimal strategies in an infinite horizon two-player zero-sum stochastic Bayesian game
sdinuka/Control-Islanding-Code