211 skills found · Page 5 of 8
Kaminyou / PGD Implemented Adversarial Attack On CIFAR10An example code of implement of PGD and FGSM algorithm for adversarial attack
thehananasif / BruteForceAttract By Hanan AsifThis repository contains a Python script designed to perform a brute force attack on Instagram accounts by generating and testing 1 Billion password combinations. The script allows the user to input an Instagram username and define the length of passwords to try. It then uses an efficient algorithm to generate possible password combinations
PythonJulia / NSL KDD Data AnalysisNSL-KDD (for network-based intrusion detection systems (IDS)) is a dataset suggested to solve some of the inherent problems of the parent KDD'99 dataset. This IDS basically helps to determine security of systems and alarming when intrusion is noticed or detected. Choosing NSL-KDD provides insightful analysis using various machine learning algorithms for intrusion detection. Myself expecting to explore intuitive insights of intrusion detection and work on various machine learning algorithms that is reasonable to understand future instance of attacks and its types.
LucasHartman / MayaScript HouseGenerator‘Generative House Algorithm’ was constructed for one simple reason, being one click away from creating a range of uniquely designed model houses. At the beginning of 2020, the start of the covid-19 pandemic, I started learning programming. My background is in developing 3D motion graphics, but my work goes into different directions. I feel inspired by trying out new things, but often feel constrained by the software I use. I never found the right software that could satisfy my every need. A few years back, I visited a motion graphics event in Prague. Here I saw a presentation by Simon Homedal from Man vs. Machine and he introduced me to procedural programming for digital art. And so my journey into learning to code started. Being stuck at home because of covid-19, I was presented with a change to really jump in and start developing a few coding projects. I started out with a simple board game in Java, where I was introduced to ‘object oriented programming’ and UI development and many other general concepts. At the end of this project I came to the conclusion that simple programming is not enough, I needed to combine with something I already have experience of. So I started using Python inside Maya, focusing on asset development of simple programs I could execute whenever I’m working on a 3D project. At the time I was wondering if I could deconstruct houses to an algorithm. The inspiration for this project came from wandering around the residential areas where I lived. Zandberg has very diverse styles of architecture; Terrace houses with high ceilings, classical villas with roofs made of straw and modern villas built after WWII. I was captivated by the diversity in design. Breakdown A simple UI inside Maya, where the uses can specify the value for generating a number of houses. Simple things like level and roof height, number of doors, max number of levels, etc. Lastly a button that would take in the value and run the algorithm. The back-end consists of a number of Python modules, textures and .obj files. One Python file called the “Main”, is where the files are assembled and executed. Process Developing a generative algorithm is a process of trial and error. At the start of the project I treated the project like any other modeling project, only every design decision was programmed in with a number of possible solutions. Over time this would become very complex and unstructured. It became impossible to go back and modify what I already wrote down. Another problem was that the algorithm was creating the model for running the code. This meant that selecting, adding and subtracting mesh to the model cost a lot of processing power, to the point my computer would freeze up. I needed to rethink my process and develop a framework which is easy to modify and light on the processor. My new plan of attack was to do as little as possible in Maya. All design instructions needed to be solved before anything can be created in Maya. Going into this direction was a hard choice. First off, it’s not a guarantee for success. The moment I would go too deep, things can get messy very easily. Besides I consider myself more of a visual thinker. Working outside of Maya meant every hurdle would be some sort of math problem. I already knew I had no choice, and understood this is the type of problem solving a programmer has to deal with. So I started out doing a little bit of RnD. My first test was to create a number of lists. Generally every list would hold some type of value. Like positional data, labels, dimensions, objects etc. and the rest would be a range of functions iterating, generating, gathering, and sorting data into these lists. These seemed flexible enough, if I needed to add new details to the model, I would make a new list and apply this into the framework. This type of framework was not very structured as I hoped. Luckily I discarded this ideal before it really began. I was already attracted by the idea of using a matrix instead of lists at the top of lists. The matrix would provide data in three dimensions, like a volume or a box made out of separate units. I would add an extra dimension to each unit, which is a list of six values. Each value would represent each side of a unit. The general ideal of a matrix is like a fluid simulation, which is made out of a matrix of voxels, or like Minecraft where each unit can be some type of block. This would create a data structure that is easy to modify. The next step would be to feed the matrix with values. A value can represent walls, doors, windows, levels, rooftops, position and direction. It starts with an empty matrix, and secondly fill it with values of 1 (later on inside Maya, value 1 would generate a wall, the location within the matrix would be translated to 3D space). If you’d stop here and translate the matrix to mesh in Maya, you would get a cluster of boxes stacked next or on top of each other. Adding more data to the matrix meant it needed to structure itself, so it would generate a cohesive design. If not the final result would be a house with holes in the wall or floating rooms. Therefore a number of functions are needed for searching for patterns, and modifying the data. A standard function would iterate over each unit in the matrix and check the neighboring values. If some sort of condition is met, the proper value will be modified. Going back to our cluster of boxes example. If a has a neighbouring box in front and to the left, but nothing on top, this would be a condition where a corner roof would be generated. And so different functions would solve design problems. In the end you would be left with a matrix of values that would serve as a blueprint for generating in house inside Maya. Finally the model needs to be made in Maya. A number of parts like a wall, door or window are generated or imported in Maya. When iterating over the finished matrix, a certain value in a certain place in the matrix will decide which objects (example wall or roof) needs to be instanced and placed in the right position and direction. When the matrix is fully realised in Maya the model gets a final cleanup, by merging the model, deleting unused parts and empty groups. What is left is the house model. If a range of houses needs to be generated, the process is simply looped over a number of times. Final word This project took way longer than I had anticipated and is far from finished. I learned a lot and at the same time it feels like I have only just begun. I hope to pick up this project again in the near future. I would love to add more elements to the house, like roof-windows or balconies and create procedural shaders. And possibly try out machine learning or some type of genetic algorithm. If you have any questions or are intrigued please contact me at ljh.hartman@gmail.com. Cheers!
tum-i4 / OedipusA Python framework that uses machine learning algorithms to implement the metadata recovery attack against obfuscated programs.
Aditya-1500 / Bot IoTThe project aims to analyse different types of attacks using the Bot-IoT dataset and also apply & compare different classification algorithms. In the project, machine learning algorithms are applied and tested using ten best features from the dataset.
AnirudhJanagam / Network Intrusion Detection System With RLHere we try to detect the attack at it's first attempt using machine learning algorithms(Reinforcement l)
toliz / Fairness AttacksRe-implementation of the paper "Exacerbating Algorithmic Bias through Fairness Attacks"
EnableSecurity / Burp Luhn Payload ProcessorA plugin for Burp Suite Pro to work with attacker payloads and automatically generate check digits for credit card numbers and similar numbers that end with a check digit generated using the Luhn algorithm or formula (also known as the "modulus 10" or "mod 10" algorithm).
mfigura / Resilient Consensus Based MARLThis repository includes a realization of the resilient projection-based consensus actor-critic algorithm that is resilient to adversarial attacks on communication channels.
js-eng / ML Detection Of Phishing WebsiteIn todays era, due to the surge in the usage of internet and other online platforms, security has been a major concern. Many cyber attacks take place each day out of which website phishing is the most common issue. It is an act of imitating a legitimate website and thereby duping the users and stealing their sensitive information. So, with respect to this problem this paper will introduce a possible solution in order to avoid such attacks by checking whether the provided URLs are phishing URL or legitimate URL. It is basically a Machine Learning based system particularly supervised learning where we have provided 2000 phishing and 2000 legitimate URL dataset. We have taken into consideration Random Forest algorithm due to its performance and accuracy. It takes 9 features into consideration and hence detects whether the URL is safe to access or a phishing URL.
bradheintz / TravSalesTravSales is a parallelized genetic algorithm attack on the Traveling Salesman problem using Hadoop.
ssqueen / Quantum Resistant CryptographyA library of cryptographic algorithms designed to withstand attacks from quantum computers. Includes post-quantum cryptographic schemes such as lattice-based, hash-based, and multivariate polynomial cryptography.
shubham0d / Crack ItA toolkit to crack hash value which works on dictionary attack..Supported most common hash algorithm.Also able to create hash and crack salted hash.
ahmedyounis1st / IDS Feature Selection Based On GWO AlgorithmOne critical issue within network security refers to intrusion detection. The nature of intrusion attempts appears to be nonlinear, wherein the network traffic performance is unpredictable, and the problematic space features are numerous. These make intrusion detection systems (IDSs) a challenge within the research arena. Hence, selecting the essential aspects for intrusion detection is crucial in information security and with that, this study identified the related features in building a computationally efficient and effective intrusion system. Accordingly, a modified feature selection (FS) algorithm called modified binary grey wolf optimisation (MBGWO) is proposed in this study. The proposed algorithm is based on binary grey wolf optimisation to boost the performance of IDS. The new FS algorithm selected an optimal number of features. In order to evaluate the proposed algorithm, the benchmark of NSL-KDD network intrusion, which was modified from 99-data set KDD cup to assess issues linked with IDS, had been applied in this study. Additionally, the support vector machine was employed to classify the data set effectively. The proposed FS and classification algorithms enhanced the performance of the IDS in detecting attacks. The simulation outcomes portrayed that the proposed algorithm enhanced the accuracy of intrusion detection and reduction in the number of features.
rblaze / A52crackImplementation of attack on A5/2 GSM encryption algorithm. Useful as math optimizations and parallelization playground.
sharmaroshan / Titanic Passenger Survival PredictionUsing Classification Techniques, Data reprocessing, Feature Engineering, Feature Extraction and Classification Algorithms from Machine Learning to Predict who can Survive the attack of Tsunami.
Abhirambs-08 / DDos Detection Using Machine Learning Algorithms PythonDDoS Detection using Machine Learning: Enhancing network security by implementing machine learning algorithms for effective DDoS attack detection and mitigation.
VITA-Group / SparseADV Homotopy[ICML 2021 Long Talk] "Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm" by Mingkang Zhu, Tianlong Chen, Zhangyang Wang
asmaaadel0 / RSACryptography project includes communication between sender and receiver with RSA encryption algorithm and breaking it with CCA (chosen cipher attack) and mathematical attack using python socket.
