DIT is a DTLS MitM proxy implemented in Python 3. It can intercept, manipulate and suppress datagrams between two DTLS endpoints and supports psk-based and certificate-based authentication schemes (RSA + ECC).

Overview

DIT - DTLS Interception Tool

DIT is a MitM proxy tool to intercept DTLS traffic.

It can intercept, manipulate and/or suppress DTLS datagrams between two DTLS endpoints. To achieve this, the machine DIT is running on has to be put in a MitM position with tools like arpspoof. DIT has been built with Python 3.8, utilizes scapy and python-mbedtls to process datagrams and supports a wide variety of cipher suites. It can handle PSK-based and certificate-based (RSA + ECC) authentication schemes and has been built and tested on Debian-based Linux operating systems like Ubuntu or Kali Linux.

DIT has been built to analyze traffic between IoT devices but can intercept any DTLS traffic in a local network. It has been tested and evaluated with OpenSSL and devices from the IKEA TRÅDFRI and Philips Hue series. DIT can print the decrypted datagram payload to stdout or write it into a logfile. The tool can be configured via CLI arguments or via a configuration file (./config/dit_config.yaml).

1. Installation

DIT works with raw sockets and needs to run with root privileges. You can install DIT by simply cloning the repository and installing the dependencies listed in requirements.txt with elevated privileges.

git clone https://github.com/CountablyInfinite/dit
pip3 install -r requirements.txt

2. Getting started

2.1 Verifying the installation

After cloning and installing the dependencies you can run the following command with elevated privileges to see if DIT has been installed successfully:

./dit.py -h
**************************
*   ___    ___   _____   *
*  |   \  |_ _| |_   _|  *
*  | |) |  | |    | |    *
*  |___/  |___|   |_|    *
*                        *
* DTLS INTERCEPTION TOOL *
*                        *
**************************

usage: ./dit.py [optional arguments] start

check configuration stored in ./config/dit_config.yaml before running DIT. 
edit the file or use optional command line arguments to override the default configuration. 
DIT needs root privileges and custom iptable rules to work properly.

run DIT:
  start                 run DIT with the current settings (args override config file settings)

target configuration:
  -isi , --iot_srv_ip   iot server ip address (listening service) to be intercepted (config file: 192.168.183.129)
  -isp , --iot_srv_po   iot server port to be intercepted. (config file: 1337)
  -ici , --iot_cli_ip   iot client ip address to be intercepted. (config file: 192.168.183.128)

interface configuration:
  -eif , --ex_if_name   external interface name (e.g. "eth0") to listen for incoming connections. (config file: ens33)
  -lif , --lh_if_name   local interface name (e.g. "lo") to communicate with local services. (config file: lo)

psk configuration:
  -cid , --cli_id       client identity to configure server and client handler with. (config file: Client_identity)
  -psk , --pre_sh_key   pre-shared key to configure server and client handler with. (config file: DIT_secret)
  --ciphers  [ ...]     list of ciphers to use, separated by spaces. (config file: None)

certificate configuration:
  -cer, --use_cert      [FLAG] use certificates as a method of authentication (instead of a psk). (config file: False)
  -ks , --key_size      length of the RSA/ECC key in bits. (config file: 2048)
  -ecc, --use_ecc       [FLAG] use 521 bit ECC instead of RSA to generate a key pair. disables --key_size. (config file: False)

local services configuration:
  -lci , --lh_cli_ip    local ip address to start a client handler (DTLS server) on. (config file: 127.0.0.1)
  -lcp , --lh_cli_po    local port to start a client handler (DTLS server listener) on. (config file: 1338)
  -lsi , --lh_srv_ip    local ip address to connect a server handler (DTLS client) to. (config file: 127.0.0.1)
  -lsp , --lh_srv_po    local port to connect a server handler (DTLS client) to. (config file: 1339)

miscellaneous:
  -ibl, --icmp_block    [FLAG] automatically create an iptables rule to suppress icmp 'destination unreachable' messages
  -o , --output_file    append intercepted unencrypted messages to an output file
  -v, --verbose         [FLAG] increase verbosity to DEBUG level
  -h, --help            [FLAG] show this help text and exit

examples:
./dit.py -isi 192.168.0.1 -isp 1337 -ici 192.168.0.2 --ciphers TLS-PSK-WITH-AES-128-CCM-8 TLS-PSK-WITH-CHACHA20-POLY1305-SHA256 -psk DIT_secret start
./dit.py --iot_srv_ip 192.168.0.1 --iot_cli_ip 192.168.0.2 --use_cert --key_size 3072 --ciphers TLS-RSA-WITH-AES-128-GCM-SHA256 --verbose start
./dit.py -isi 192.168.0.1 -ici 192.168.0.2 --use_cert -ecc --output_file logfile.log --verbose start

this tool has been created for the purposes of academic research. 
use responsibly and only when explicitly authorized.

2.2 Prerequisite

2.2.1 Elevated privileges

DIT uses raw sockets and therefore needs to run with elevated (root) privileges.

2.2.2 iptables rule

DIT builds four proxy layers with scapy that are communicating between the external interface and the DTLS services running on localhost. To suppress upcoming "Destination unreachable" errors - that cause DIT to halt with an error - a custom iptables rule is necessary. You can generate it with the following command:

iptables -I OUTPUT -d localhost-ip -p icmp --icmp-type destination-unreachable -j DROP

The iptable rules can be set/unset automatically by using the --icmp_block argument when starting DIT.

./dit.py --icmp_block start

2.2.3 MitM position

For DIT to work it has to be run from a MitM position. A MitM position can be achieved in many ways, one of them is by using the tool arpspoof (part of the dsniff tool suite). To gain a MitM position in a local network between the clients 192.168.0.1 and 192.168.0.2 you can use the following command:

arpspoof -i ens33 -t 192.168.0.1 -r 192.168.0.2

2.3 Configuring DIT

DIT can be configured via CLI arguments or via a configuration file (./config/dit_config.yaml). CLI arguments override settings stored in the configuration file. When calling ./dit.py -h - as depicted in section 2.1 - DIT prints out the current configuration that has been read from the configuration file.

2.3.1 ./config/dit_config.yaml

DIT comes with a default configuration you'll need to adapt before running an attack.

cat ./config/dit_config.yaml 
# configure spoofing/sniffing targets
targets:  
  iot_srv_ip: 192.168.183.129
  iot_srv_po: 1337
  iot_cli_ip: 192.168.183.128
  ciphers:
    # if no ciphers are configured, DIT will offer all ciphersuites available with mbedTLS
    #- TLS-ECDHE-ECDSA-WITH-AES-256-CBC-SHA384
    #- TLS-PSK-WITH-AES-128-CCM-8
    #- TLS-RSA-WITH-AES-128-GCM-SHA256

# configure interface names
interfaces:
  ex_if_name: ens33
  lh_if_name: lo

# configure psk options
psk:
  cli_id: Client_identity
  pre_sh_key: DIT_secret

# configure certificate options
certificate:
  # default is RSA. "use_ecc" arg enables ECC and disables key_size
  use_cert: False
  key_size: 2048
  use_ecc: False

# configure local dtls services
local_services:
  lh_cli_ip: 127.0.0.1
  lh_cli_po: 1338
  lh_srv_ip: 127.0.0.1
  lh_srv_po: 1339

targets:

  • iot_srv_ip: IP address of the dtls server
  • iot_srv_po: Port the dtls server is listening on
  • iot_cli_ip: IP address of the dtls client
  • ciphers: List of cipher suites (using the OpenSSL format) DIT will offer/support when establishing the connections. When no suites are configured DIT offers/supports all cipher suites available with mbedTLS.

interfaces:

  • ex_if_name: Name of the external interface DIT will operate on.
  • lh_if_name: Name of the internal interface DIT will operate on. Local DTLS server and client services will operate on this interface.

psk:

  • cli_id: Client identy to be used when accepting / establishing DTLS connections. (Default key is 'Client_identity')
  • pr_sh_key: PSK to be used when accepting / establishing DTLS connections. (ASCII encoded)

certificate:

  • use_cert: Boolean value. Activates the usage of RSA certificates. DIT automatically creates and uses a corresponding certificate with "key_size" Bits in length.
  • key_size: Length of the RSA key in bits.
  • use_ecc: Boolean value. Activates the usege of ECC certificates. Only works when "use_cert" is set. Deactivates "key_size".

local services:

  • lh_cli_ip: IP address of the localhost interface the dtls client is running on. (typically 127.0.0.1)
  • lh_cli_po: Port the local client instance is accepting traffic on. (needn't be changed in a typical setup)
  • lh_srv_ip: IP address of the localhost interface the dtls server is running on. (typically 127.0.0.1)
  • lh_srv_po: Port the local server instance is accepting traffic on. (needn't be changed in a typical setup)

2.3.2 Command Line Arguments

DIT can be configured via Command Line Arguments. The arguments are listed and described when calling ./dit.py -h - as shown in section 2.1. Command Line Arguments override settings stored in the configuration file and are a fast way to adapt/test settings without changing the config file.

3. Use cases / Evaluation

Refer to https://github.com/CountablyInfinite/dit/tree/master/doc

Re-implememtation of MAE (Masked Autoencoders Are Scalable Vision Learners) using PyTorch.

mae-repo PyTorch re-implememtation of "masked autoencoders are scalable vision learners". In this repo, it heavily borrows codes from codebase https:/

Peng Qiao 1 Dec 14, 2021
TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks

TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks [Paper] [Project Website] This repository holds the source code, pretra

Humam Alwassel 83 Dec 21, 2022
AAAI-22 paper: SimSR: Simple Distance-based State Representationfor Deep Reinforcement Learning

SimSR Code and dataset for the paper SimSR: Simple Distance-based State Representationfor Deep Reinforcement Learning (AAAI-22). Requirements We assum

7 Dec 19, 2022
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

YeongHyeon Park 7 Aug 28, 2022
YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4. YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitraril

Adam Van Etten 161 Jan 06, 2023
It helps user to learn Pick-up lines and share if he has a better one

Pick-up-Lines-Generator(Open Source) It helps user to learn Pick-up lines Share and Add one or many to the DataBase Unique SQLite DataBase AI Undercon

knock_nott 0 May 04, 2022
The 7th edition of NTIRE: New Trends in Image Restoration and Enhancement workshop will be held on June 2022 in conjunction with CVPR 2022.

NTIRE 2022 - Image Inpainting Challenge Important dates 2022.02.01: Release of train data (input and output images) and validation data (only input) 2

Andrés Romero 37 Nov 27, 2022
Train the HRNet model on ImageNet

High-resolution networks (HRNets) for Image classification News [2021/01/20] Add some stronger ImageNet pretrained models, e.g., the HRNet_W48_C_ssld_

HRNet 866 Jan 04, 2023
Crowd-sourced Annotation of Human Motion.

Motion Annotation Tool Live: https://motion-annotation.humanoids.kit.edu Paper: The KIT Motion-Language Dataset Installation Start by installing all P

Matthias Plappert 4 May 25, 2020
Asynchronous Advantage Actor-Critic in PyTorch

Asynchronous Advantage Actor-Critic in PyTorch This is PyTorch implementation of A3C as described in Asynchronous Methods for Deep Reinforcement Learn

Reiji Hatsugai 38 Dec 12, 2022
Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

CoMatch: Semi-supervised Learning with Contrastive Graph Regularization (Salesforce Research) This is a PyTorch implementation of the CoMatch paper [B

Salesforce 107 Dec 14, 2022
Modular Gaussian Processes

Modular Gaussian Processes for Transfer Learning 🧩 Introduction This repository contains the implementation of our paper Modular Gaussian Processes f

Pablo Moreno-Muñoz 10 Mar 15, 2022
Code base for the paper "Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation"

This repository contains code for the paper Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiati

8 Aug 28, 2022
Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion Models

Label-Efficient Semantic Segmentation with Diffusion Models Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion

Yandex Research 355 Jan 06, 2023
A PyTorch implementation of DenseNet.

A PyTorch Implementation of DenseNet This is a PyTorch implementation of the DenseNet-BC architecture as described in the paper Densely Connected Conv

Brandon Amos 771 Dec 15, 2022
Code and data for "TURL: Table Understanding through Representation Learning"

TURL This Repo contains code and data for "TURL: Table Understanding through Representation Learning". Environment and Setup Data Pretraining Finetuni

SunLab-OSU 63 Nov 23, 2022
Text to Image Generation with Semantic-Spatial Aware GAN

text2image This repository includes the implementation for Text to Image Generation with Semantic-Spatial Aware GAN This repo is not completely. Netwo

CVDDL 124 Dec 30, 2022
hySLAM is a hybrid SLAM/SfM system designed for mapping

HySLAM Overview hySLAM is a hybrid SLAM/SfM system designed for mapping. The system is based on ORB-SLAM2 with some modifications and refactoring. Raú

Brian Hopkinson 15 Oct 10, 2022
A python library for self-supervised learning on images.

Lightly is a computer vision framework for self-supervised learning. We, at Lightly, are passionate engineers who want to make deep learning more effi

Lightly 2k Jan 08, 2023
Malware Bypass Research using Reinforcement Learning

Malware Bypass Research using Reinforcement Learning

Bobby Filar 76 Dec 26, 2022