1,638 skills found · Page 9 of 55
astomodynamics / Cddp CppConstrained Differential Dynamic Programming Solver for Trajectory Optimization and Model Predictive Control
concurrence / ConcurrenceConcurrence is a framework for creating massively concurrent network applications in Python. It takes a Lightweight-tasks-with-message-passing approach to concurrency. The goal of Concurrence is to provide an easier programming model for writing high performance network applications than existing solutions (Multi-threading, Twisted, asyncore etc).
bookworm52 / EthicalHackingFromScratchWelcome to my comprehensive course on python programming and ethical hacking. The course assumes you have NO prior knowledge in any of these topics, and by the end of it you'll be at a high intermediate level being able to combine both of these skills to write python programs to hack into computer systems exactly the same way that black hat hackers do. That's not all, you'll also be able to use the programming skills you learn to write any program even if it has nothing to do with hacking. This course is highly practical but it won't neglect the theory, we'll start with basics of ethical hacking and python programming and installing the needed software. Then we'll dive and start programming straight away. You'll learn everything by example, by writing useful hacking programs, no boring dry programming lectures. The course is divided into a number of sections, each aims to achieve a specific goal, the goal is usually to hack into a certain system! We'll start by learning how this system work and its weaknesses, then you'll lean how to write a python program to exploit these weaknesses and hack the system. As we write the program I will teach you python programming from scratch covering one topic at a time. By the end of the course you're going to have a number of ethical hacking programs written by yourself (see below) from backdoors, keyloggers, credential harvesters, network hacking tools, website hacking tools and the list goes on. You'll also have a deep understanding on how computer systems work, how to model problems, design an algorithm to solve problems and implement the solution using python. As mentioned in this course you will learn both ethical hacking and programming at the same time, here are some of the topics that will be covered in the course: Programming topics: Writing programs for python 2 and 3. Using modules and libraries. Variables, types ...etc. Handling user input. Reading and writing files. Functions. Loops. Data structures. Regex. Desiccation making. Recursion. Threading. Object oriented programming. Packet manipulation using scapy. Netfilterqueue. Socket programming. String manipulation. Exceptions. Serialisation. Compiling programs to binary executables. Sending & receiving HTTP requests. Parsing HTML. + more! Hacking topics: Basics of network hacking / penetration testing. Changing MAC address & bypassing filtering. Network mapping. ARP Spoofing - redirect the flow of packets in a network. DNS Spoofing - redirect requests from one website to another. Spying on any client connected to the network - see usernames, passwords, visited urls ....etc. Inject code in pages loaded by any computer connected to the same network. Replace files on the fly as they get downloaded by any computer on the same network. Detect ARP spoofing attacks. Bypass HTTPS. Create malware for Windows, OS X and Linux. Create trojans for Windows, OS X and Linux. Hack Windows, OS X and Linux using custom backdoor. Bypass Anti-Virus programs. Use fake login prompt to steal credentials. Display fake updates. Use own keylogger to spy on everything typed on a Windows & Linux. Learn the basics of website hacking / penetration testing. Discover subdomains. Discover hidden files and directories in a website. Run wordlist attacks to guess login information. Discover and exploit XSS vulnerabilities. Discover weaknesses in websites using own vulnerability scanner. Programs you'll build in this course: You'll learn all the above by implementing the following hacking programs mac_changer - changes MAC Address to anything we want. network_scanner - scans network and discovers the IP and MAC address of all connected clients. arp_spoofer - runs an arp spoofing attack to redirect the flow of packets in the network allowing us to intercept data. packet_sniffer - filters intercepted data and shows usernames, passwords, visited links ....etc dns_spoofer - redirects DNS requests, eg: redirects requests to from one domain to another. file_interceptor - replaces intercepted files with any file we want. code_injector - injects code in intercepted HTML pages. arpspoof_detector - detects ARP spoofing attacks. execute_command payload - executes a system command on the computer it gets executed on. execute_and_report payload - executes a system command and reports result via email. download_and_execute payload - downloads a file and executes it on target system. download_execute_and_report payload - downloads a file, executes it, and reports result by email. reverse_backdoor - gives remote control over the system it gets executed on, allows us to Access file system. Execute system commands. Download & upload files keylogger - records key-strikes and sends them to us by email. crawler - discovers hidden paths on a target website. discover_subdomains - discovers subdomains on target website. spider - maps the whole target website and discovers all files, directories and links. guess_login - runs a wordlist attack to guess login information. vulnerability_scanner - scans a target website for weaknesses and produces a report with all findings. As you build the above you'll learn: Setting up a penetration testing lab to practice hacking safely. Installing Kali Linux and Windows as virtual machines inside ANY operating system. Linux Basics. Linux terminal basics. How networks work. How clients communicate in a network. Address Resolution Protocol - ARP. Network layers. Domain Name System - DNS. Hypertext Transfer Protocol - HTTP. HTTPS. How anti-virus programs work. Sockets. Connecting devices over TCP. Transferring data over TCP. How website work. GET & POST requests. And more! By the end of the course you're going to have programming skills to write any program even if it has nothing to do with hacking, but you'll learn programming by programming hacking tools! With this course you'll get 24/7 support, so if you have any questions you can post them in the Q&A section and we'll respond to you within 15 hours. Notes: This course is created for educational purposes only and all the attacks are launched in my own lab or against devices that I have permission to test. This course is totally a product of Zaid Sabih & zSecurity, no other organisation is associated with it or a certification exam. Although, you will receive a Course Completion Certification from Udemy, apart from that NO OTHER ORGANISATION IS INVOLVED. What you’ll learn 170+ videos on Python programming & ethical hacking Install hacking lab & needed software (on Windows, OS X and Linux) Learn 2 topics at the same time - Python programming & Ethical Hacking Start from 0 up to a high-intermediate level Write over 20 ethical hacking and security programs Learn by example, by writing exciting programs Model problems, design solutions & implement them using Python Write programs in Python 2 and 3 Write cross platform programs that work on Windows, OS X & Linux Have a deep understanding on how computer systems work Have a strong base & use the skills learned to write any program even if its not related to hacking Understand what is Hacking, what is Programming, and why are they related Design a testing lab to practice hacking & programming safely Interact & use Linux terminal Understand what MAC address is & how to change it Write a python program to change MAC address Use Python modules and libraries Understand Object Oriented Programming Write object oriented programs Model & design extendable programs Write a program to discover devices connected to the same network Read, analyse & manipulate network packets Understand & interact with different network layers such as ARP, DNS, HTTP ....etc Write a program to redirect the flow of packets in a network (arp spoofer) Write a packet sniffer to filter interesting data such as usernames and passwords Write a program to redirect DNS requests (DNS Spoofer) Intercept and modify network packets on the fly Write a program to replace downloads requested by any computer on the network Analyse & modify HTTP requests and responses Inject code in HTML pages loaded by any computer on the same network Downgrade HTTPS to HTTP Write a program to detect ARP Spoofing attacks Write payloads to download a file, execute command, download & execute, download execute & report .....etc Use sockets to send data over TCP Send data reliably over TCP Write client-server programs Write a backdoor that works on Windows, OS X and Linux Implement cool features in the backdoor such as file system access, upload and download files and persistence Write a remote keylogger that can register all keystrikes and send them by Email Interact with files using python (read, write & modify) Convert python programs to binary executables that work on Windows, OS X and Linux Convert malware to torjans that work and function like other file types like an image or a PDF Bypass Anti-Virus Programs Understand how websites work, the technologies used and how to test them for weaknesses Send requests towebsites and analyse responses Write a program that can discover hidden paths in a website Write a program that can map a website and discover all links, subdomains, files and directories Extract and submit forms from python Run dictionary attacks and guess login information on login pages Analyse HTML using Python Interact with websites using Python Write a program that can discover vulnerabilities in websites Are there any course requirements or prerequisites? Basic IT knowledge No Linux, programming or hacking knowledge required. Computer with a minimum of 4GB ram/memory Operating System: Windows / OS X / Linux Who this course is for: Anybody interested in learning Python programming Anybody interested in learning ethical hacking / penetration testing Instructor User photo Zaid Sabih Ethical Hacker, Computer Scientist & CEO of zSecurity My name is Zaid Al-Quraishi, I am an ethical hacker, a computer scientist, and the founder and CEO of zSecurity. I just love hacking and breaking the rules, but don’t get me wrong as I said I am an ethical hacker. I have tremendous experience in ethical hacking, I started making video tutorials back in 2009 in an ethical hacking community (iSecuri1ty), I also worked as a pentester for the same company. In 2013 I started teaching my first course live and online, this course received amazing feedback which motivated me to publish it on Udemy. This course became the most popular and the top paid course in Udemy for almost a year, this motivated me to make more courses, now I have a number of ethical hacking courses, each focusing on a specific field, dominating the ethical hacking topic on Udemy. Now I have more than 350,000 students on Udemy and other teaching platforms such as StackSocial, StackSkills and zSecurity. Instructor User photo z Security Leading provider of ethical hacking and cyber security training, zSecurity is a leading provider of ethical hacking and cyber security training, we teach hacking and security to help people become ethical hackers so they can test and secure systems from black-hat hackers. Becoming an ethical hacker is simple but not easy, there are many resources online but lots of them are wrong and outdated, not only that but it is hard to stay up to date even if you already have a background in cyber security. Our goal is to educate people and increase awareness by exposing methods used by real black-hat hackers and show how to secure systems from these hackers. Video course
fsndzomga / MetadspyMetadspy: The framework for specifying—not programming—language models
parkchamchi / DepthViewerUnity program that creates 3D scenes for VR using MiDaS model
JIA-Lab-research / MoTCoderThis is the official code repository of MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks.
andrejbauer / AlgAlg is a program that generates all finite models of a first-order theory. It is optimized for equational theories.
gedeschaines / PropNavA 3-DOF point mass kinematic model of an ideal proportional navigation guidance missile written entirely in Python 3. Capable of creating trajectory files compatible with the pyThreeD program provided herein and the threeD program available at https://www.github.com/gedeschaines/threeD
VivekPa / BinomialOptModelA python program to implement the discrete binomial option pricing model
OpenSourceEconomics / RespyFramework for the simulation and estimation of some finite-horizon discrete choice dynamic programming models.
SAP-samples / Cloud Cap Risk ManagementThe SAP Risk Management example applications show how to deploy a CAP (SAP Cloud Application Programming model) application to Cloud Foundry and Kyma.
buptmiao / ParallelA golang parallel library, used for business logic aggregation and refactory without changing user function declaration.
SAP-samples / Hana Opensap Cloud 2020Exercise materials for openSAP courses targeted to application developers, SAP HANA developers, Cloud Application Programming Model developers, and SAP Business Technology Platform developers.
SAP-samples / Btp Developer Guide CapSAP BTP Developer’s Guide offers developer guidance to software developers on how to implement business applications using SAP Cloud Application Programming Model on SAP BTP Cloud Foundry and SAP BTP Kyma Runtime following the best practices.
seznam / EuphoriaEuphoria is an open source Java API for creating unified big-data processing flows. It provides an engine independent programming model which can express both batch and stream transformations.
BlenderAI / BlenderRLAn open-source project for Blender 3D connects all the controls within Blender to an AI, allowing the AI to learn how to use the program through trial-and-error. This results in an efficient AI assistant for Blender, allowing for the smooth creation of new models, improvement of existing ones, automatic material generation, and optimized workflows.
lucmoreau / ProvToolboxJava toolkit to create and convert W3C PROV data model representations, and build provenance-enabled applications in a variety of programming languages (java, python, typescript, javascript)
chkwon / Complementarity.jlprovides a modeling interface for mixed complementarity problems (MCP) and math programs with equilibrium problems (MPEC) via JuMP
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
coldchair / CPRet[NeurIPS'25] CPRet: A Dataset, Benchmark, and Model for Retrieval in Competitive Programming
