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smartcontract-lottery

smp

Server Mining Pool

sms-xombie

An Android spyware which interacts with a remote C&C server to exfiltrate phone data

snifflab

Scripts to create your own MITM'ing, packet sniffing WiFi access point

snmpwn

An SNMPv3 User Enumerator and Attack tool

social-engineer-toolkit

The Social-Engineer Toolkit (SET) repository from TrustedSec - All new versions of SET will be deployed here.

socks

A SOCKS (SOCKS4, SOCKS4A and SOCKS5) Proxy Package for Go

socks_proxy

socks5 proxy server in C

socktrader

🚀 Websocket based trading bot for 💰cryptocurrencies 📈

sonar.js

A framework for identifying and launching exploits against internal network hosts. Works via WebRTC IP enumeration combined with WebSockets and external resource fingerprinting.

specifications

Protocol specifications for various algorithms used @ NiceHash

spectre-poc

Proof of Concept - Spectre

speech_signal_processing_and_classification

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

spiderwho

A very fast whois crawler

spoof

Easily spoof your MAC address in macOS, Windows, & Linux!

spoofsourceip

Golang/gopath example for spoofing UDP source IP addresses

sqli_d0rk3r

sqli_dorker v1.0.8

ssh-brute-forcer

A Simple Multi-Threaded SSH Brute Forcer

ssh-git-go

Simple SSH server that serves anonymous git repositories (read-only)

ssh-mitm

SSH man-in-the-middle tool

ssh-mitm-1

ssh mitm server for security audits supporting public key authentication, session hijacking and file manipulation

ssh-with-pythonto-ip-list-and-use-full-for-bruteforce

this app can brute force with ip list pass list and user list for me use to connect to any mikrotik and run a command and exit i controll all mikrotik os

sshame

brute force SSH public-key authentication

sshaolin

SSH for Ninjas

sshash

ssh bruter

sshb

A password brute forcer for the SSH protocol

sshbrute

ssh brute force

sshbruter

Tiny Golang SSH Bruter developed based on https://github.com/pH-T/brutessh

sshcode

Run VS Code on any server over SSH.

sshlooterc

It's the C version of https://github.com/mthbernardes/sshLooter