semvec/pypi-packages huggingface dataset:
darwin-ssl-sniffer 0.0.4
Sniffer for encrypted traffic on darwin-based platforms
Requires Python: >=3.8
Keywords: ios,jailbreak,ssl,sniffer,tls,cli,automation,darwin,osx,macos

# Description

**Utility for sniffing SSL/TLS encrypted traffic on a darwin-based platforms.**

`CFNetwork.framework` contains a debug/verbosity global, enforcing a dump of every packet transferred through it, to be
logged into device syslog in plaintext form. In order to have a nicer view with clean control flow of this traffic, we
attach each such packet appropriate TCP flags and write it back into a PCAP file.

This allows us to later dissect this traffic using popular and convenient tools (e.g. Wireshark 🦈).

On iOS, this will require a jailbroken iOS device.

# Installation

```shell
python3 -m pip install -U darwin-ssl-sniffer
```

# Usage

## Local macOS machine

Simply execute:

```shell
# output file can be given using the -o option (traffic.pcapng by default)
python3 -m darwin_ssl_sniffer sniff
```

## Jailbroken iOS device

- Download and install [`rpcserver`](https://rpc-project.readthedocs.io/en/latest/getting_started/installation.html) on
  a jailbroken device.
- Execute:
  ```shell
  python3 -m darwin_ssl_sniffer mobile setup -p 5910
  ```
  This step should be performed only once on the device. The first time will require a device reboot (you will be
  prompted to if this is indeed the first time).
- Execute:
  ```shell
  # output file can be given using the -o option (traffic.pcapng by default)
  python3 -m darwin_ssl_sniffer mobile sniff
  ```
Topsis-Kashish-102117150 1.0
A package for performing TOPSIS analysis
Keywords: python,topsis


# Topsis Implementation
This package is used for topsis implementation, published by Kashish, roll number- 102117150, group- 3CS6.
## Overview
This Python package implements the Topsis (Technique for Order Preference by Similarity to Ideal Solution) algorithm. Topsis is a multi-criteria decision-making (MCDM) method that helps in choosing the best alternative from a set of alternatives based on their performance on multiple criteria.

## Installation
To install the package, use the following command:
pip install topsis-Kashish-102117150

## Data
Fund Name,P1,P2,P3,P4,P5
M1,0.79,0.62,4.8,66.6,18.2
M2,0.69,0.48,5,46.9,13.27
M3,0.77,0.59,3.3,43.9,12.14
M4,0.82,0.67,3.2,67,17.92
M5,0.65,0.42,5.9,34.9,10.47
M6,0.8,0.64,4.8,66.8,18.26
M7,0.75,0.56,3.9,31.7,9.23
M8,0.83,0.69,4.1,55.9,15.38

## Usage
In the terminal,
python 102117150.py 102117150-data.csv "1,1,1,1,1" "+,-,+,-,+" 102117150-result.csv
where,
input_data.csv: The input CSV file containing the decision matrix. You can use the file 102117150-data.csv .
"1,1,1,1,1": Comma-separated weights for each criterion.
"+,-,+,-,+": Comma-separated impacts for each criterion (either '+' or '-').
output_result.csv: The desired name for the output CSV file containing Topsis scores and ranks. You can see the results in file- 102117150-result-1.csv for the respective weights and impacts.

## Result
Fund Name,P1,P2,P3,P4,P5,Topsis Score,Rank
M1,0.79,0.62,4.8,66.6,18.2,0.7879309024122741,6
M2,0.69,0.48,5.0,46.9,13.27,0.4602057102586321,1
M3,0.77,0.59,3.3,43.9,12.14,0.3424774958156483,8
M4,0.82,0.67,3.2,67.0,17.92,0.6236353942759449,4
M5,0.65,0.42,5.9,34.9,10.47,0.3924844873537729,2
M6,0.8,0.64,4.8,66.8,18.26,0.7998958662123036,5
M7,0.75,0.56,3.9,31.7,9.23,0.23219941041702397,3
M8,0.83,0.69,4.1,55.9,15.38,0.6259888691115867,7


```bash
pip install topsis-Kashish-102117150