35 skills found · Page 1 of 2
kamorin / DHCPigDHCP exhaustion script written in python using scapy network library
s4core / S4core🚀3x faster than MinIO and RustFS. S4Core is an open-source, Rust-based S3-compatible server. Say goodbye to inode exhaustion and hello to atomic operations and smart deduplication
foreni-packages / Dhcpigdhcpig : initiates an advanced DHCP exhaustion attack
AjayAntoIsDev / WaSonarWhatsApp Intelligence & Resource Exhaustion Tool. Features real-time device tracking, silent RTT probing, and protocol stress testing for security research.
uber / Denial By DnsTesting local DOS due to getaddrinfo() exhaustion
fmmattioni / LactaterTools for analyzing lactate thresholds from a step incremental test to exhaustion 📈
initc3 / I Cant Believe Its Not Stakeresource exhaustion vulnerabilities in PoSv3 cryptocurrencies
zeroxjf / SEP Exhaustion Kernel PanicSEP firmware panic via AppleKeyStore - iOS/macOS 26.x kernel vulnerability
peppelinux / PyDHCPStarvatorDHCP starvation with Python Scapy
saaramar / IOS Memory Exhaustion WriteupNo description available
usethisname1419 / HashKillerDecrypt Hashes. md5, sha-1, sha-256, shaw-512(unix), and Windows NT hashes. Multi-threaded and includes a saftey to prevent cpu exhaustion
crowdcompass / DoodleKitThe DoodleKit manages up to four users' drawings on a single canvas, allowing doodlers to create and share content across their devices. A demo app, Doodle Party, is included in the repository. Developed with care and exhaustion at iOSDevCamp 2013.
prashantdukecyfi / Driver Monitoring System For Automobile Using Machine Learning And OpenCVThe following project can be used to monitor the driver continuously for fatigue and the case of exhaustion and that of falling asleep. In such a case when the computer finds that the driver is sleeping the computer will trigger an alarm automatically, and the alarm will only stop on killing the process/stopping the car.
SUHONGJIAN / Matlab Distributed Sensoorithm Judege LEACH Clustering Energy Exhaustion首先需要随机产生100个点，挑选出簇头以及每个簇头下的簇成员，需要注意一点的是，每个节点仅能当一次簇头，即本轮的簇头在以后的分簇中只能当簇成员，我将簇成员用*来表示，簇头用实心圆心来表示，且普通簇成员用‘黑色*’来表示，当过簇头的簇成员用‘蓝色*’，普通簇头用‘红色实心圆心’来表示，第一个耗尽簇头用‘绿色实心圆心’来表示
cmedina-dev / TS3 64bit PatchA collection of patches for The Sims 3 that bypass the 32-bit limitations causing common errors like VAS exhaustion (Error 12).
ameyskulkarni / Detection And Localization Of Traffic Lights Using RCNNs On Bosch BSTLD DatasetThis repository presents a code to detect the rear of cars using RCNNs. The dataset consists of road images in different conditions like daylight and night conditions. The labels are given in the .csv format. Each row of the labels file consists of name of the image, details about coordinates of the bounding box(x_min, x_max, y_min and y_max), and the label itself. Details are extracted from the csv file and stored in a dataframe. ONly a subset of the data was trained on due to the resource exhaustion. All the details will be given below. Object detection: There are two parts to object detection- Object classification Object localization Bounding boxes are used usually for the localization purpose and the labels are used for classification. The two major techniques used in the industry for object detection are RCNNs and YOLO. I have dedicated the time spent on these assignments to learn about one of these techniques: RCNNs. Region Based Convolutional Neural Networks The Architecture of RCNN is very extensive as it has different blocks of layes for the above mentioned purposes: classification and localization. The code I have used takes VGG-16 as the first block of layers which take in the images as 3D tensors and and give out feature maps. To understand the importance of Transfer learning, I have used pre-trained weights of this model. This is the base network. The next network block is the Region Proposal Network. This is a Fully Convolutional Network. This network uses a concept of Anchors. It is a very interesting concept. This solves the problem of using exactly what length of bounding boxes. The image is scaled down and now each pixel woks as an anchor. Each anchor defines a certain number of bounding box primitives. The RPN is used to predict the score of object being inside each of this bounding box primitive. A Region of INterest pooling layer appears next. This is a layer which takes in ROIs of the feature map to compare and classify each bounding box. A post processing technique of Non-maximal supression is used to select the bounding box with the highest probability of the object being there. The image is scaled back up and this box is displayed. Hyperparameters used- Number of samples for training- 2252 Number of samples for testing- 176 ROIs- 4 epoch length- 500 Epochs- 91 Anchors-9 All results are visible in the ipynb files of training and testing. With only running the 40 epochs the mAP over the test data gave 0.68 value. THis is close to the 75% expected. I trained more and the accuracy visibly improved from the loss graph and the bounding box accuracy but sadly I am not able to find the mAP after this training round because the I increased the dataset size and I always get and error of resource exhaustion. I am planning to make the code more modular so that I can allocate resources to different modules separately and this issue is overcome. The accuarcy can further be improved by training over a larger dataset and running for more epochs. I will try to do this and improve the accuracy.
hyvor / Php Json ExporterExport large datasets to a JSON file without memory exhaustion
moloch-- / NetnadePreform DHCP exhaustion attacks using an Arduino device.
sal-scar / MedusaMedusa is designed to demonstrate the vulnerability of certain IoT cameras (specifically those using AltoBeam/V380 chips) to network-based Resource Exhaustion and Brute Force Attack.
iana-org / Ipv4 Recovery AlgorithmImplementation of the Global Policy for Post Exhaustion IPv4 Allocation by IANA
