Vern  Greenholt

Vern Greenholt

1596386640

CWT Travel Agency Faces $4.5M Ransom in Cyberattack, Report

The corporate-travel leader has confirmed an attack that knocked systems offline.

CWT, a giant in the corporate travel agency world with a global clientele, may have faced payment of $4.5 million to unknown hackers in the wake of a ransomware attack.

Independent malware hunter @JAMESWT tweeted on Thursday that a malware sample used against CWT (formerly known as Carlson Wagonlit Travel) had been uploaded to VirusTotal on July 27; he also included a ransom note indicating that the ransomware in question is Ragnar Locker.

In a media statement to Threatpost, CWT confirmed the cyberattack, which it said took place this past weekend: “We can confirm that after temporarily shutting down our systems as a precautionary measure, our systems are back online and the incident has now ceased.”

@JAMESWT also reported that the ransom demanded clocked in at 414 Bitcoin, or about $4.5 million at the current exchange rate. A CWT spokesperson declined to comment on whether the ransom was paid, or any technical details of the attack, or how it was able to recover so quickly.

Despite assurances of recovery, the impact of the incident could be wide: CWT says that it provides travel services to 33 percent of the Fortune 500 and countless smaller companies. And according to the ransom note uploaded by @JAMESWT, the hackers claim to have downloaded 2TB of the firm’s data, including “billing info, insurance cases, financial reports, business audit, banking accounts…corporate correspondence…[and] information about your clients such as AXA Equitable, Abbot Laboratories, AIG, Amazon, Boston Scientific, Facebook, J&J, SONOCO, Estee Lauder and many others.”

If true, the tactic fits in with the one-two punch trend that many ransomware operators have taken of late – locking up files, but also stealing and threatening to release sensitive data if victims don’t pay up. Such was the case of celebrity law firm Grubman Shire Meiselas & Sacks, which was hit with the REvil ransomware in May. Attackers threatened to leak 756 gigabytes of stolen data, including personal info on Lady Gaga, Drake and Madonna.

And in fact, the attackers behind the Ragnar Locker ransomware in particular are known for stealing data before encrypting networks, as was the case in April, in an attack on the North American network of Energias de Portugal (EDP). The cyberattackers claimed to have stolen 10 TB of sensitive company data, and demanded a payment of 1,580 Bitcoin (approximately $11 million).

“Ragnar Locker is a novel and insidious ransomware group, as Portuguese energy provider EDP found out earlier this year,” Matt Walmsley, EMEA director at Vectra, said via email. “Mirroring the ‘name and shame’ tactic used by Maze Group ransomware, victim’s data is exfiltrated prior to encryption and used to leverage ransomware payments. The bullying tactics used by these ransomware groups are making attacks even more expensive, and they are not going to stop any time soon, particularly within the current climate.”

#breach #malware #data analysisa

What is GEEK

Buddha Community

CWT Travel Agency Faces $4.5M Ransom in Cyberattack, Report

A Lightweight Face Recognition and Facial Attribute Analysis

deepface

Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib.

Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.

Installation

The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well. The library is mainly based on TensorFlow and Keras.

pip install deepface

Then you will be able to import the library and use its functionalities.

from deepface import DeepFace

Facial Recognition - Demo

A modern face recognition pipeline consists of 5 common stages: detect, align, normalize, represent and verify. While Deepface handles all these common stages in the background, you don’t need to acquire in-depth knowledge about all the processes behind it. You can just call its verification, find or analysis function with a single line of code.

Face Verification - Demo

This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or based64 encoded images is also welcome. Then, it is going to return a dictionary and you should check just its verified key.

result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")

Face recognition - Demo

Face recognition requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output.

df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")

Face recognition models - Demo

Deepface is a hybrid face recognition package. It currently wraps many state-of-the-art face recognition models: VGG-Face , Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib. The default configuration uses VGG-Face model.

models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]

#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1])

#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])

FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments. You can find out the scores of those models below on both Labeled Faces in the Wild and YouTube Faces in the Wild data sets declared by its creators.

ModelLFW ScoreYTF Score
Facenet51299.65%-
ArcFace99.41%-
Dlib99.38 %-
Facenet99.20%-
VGG-Face98.78%97.40%
Human-beings97.53%-
OpenFace93.80%-
DeepID-97.05%

Similarity

Face recognition models are regular convolutional neural networks and they are responsible to represent faces as vectors. We expect that a face pair of same person should be more similar than a face pair of different persons.

Similarity could be calculated by different metrics such as Cosine Similarity, Euclidean Distance and L2 form. The default configuration uses cosine similarity.

metrics = ["cosine", "euclidean", "euclidean_l2"]

#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1])

#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[1])

Euclidean L2 form seems to be more stable than cosine and regular Euclidean distance based on experiments.

Facial Attribute Analysis - Demo

Deepface also comes with a strong facial attribute analysis module including age, gender, facial expression (including angry, fear, neutral, sad, disgust, happy and surprise) and race (including asian, white, middle eastern, indian, latino and black) predictions.

obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion'])

Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision and 95.05% recall as mentioned in its tutorial.

Streaming and Real Time Analysis - Demo

You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.

DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")

Even though face recognition is based on one-shot learning, you can use multiple face pictures of a person as well. You should rearrange your directory structure as illustrated below.

user
├── database
│   ├── Alice
│   │   ├── Alice1.jpg
│   │   ├── Alice2.jpg
│   ├── Bob
│   │   ├── Bob.jpg

Face Detectors - Demo

Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. OpenCV, SSD, Dlib, MTCNN and RetinaFace detectors are wrapped in deepface.

All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.

backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']

#face verification
obj = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", detector_backend = backends[4])

#face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4])

#facial analysis
demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4])

#face detection and alignment
face = DeepFace.detectFace(img_path = "img.jpg", target_size = (224, 224), detector_backend = backends[4])

Face recognition models are actually CNN models and they expect standard sized inputs. So, resizing is required before representation. To avoid deformation, deepface adds black padding pixels according to the target size argument after detection and alignment.

RetinaFace and MTCNN seem to overperform in detection and alignment stages but they are much slower. If the speed of your pipeline is more important, then you should use opencv or ssd. On the other hand, if you consider the accuracy, then you should use retinaface or mtcnn.

The performance of RetinaFace is very satisfactory even in the crowd as seen in the following illustration. Besides, it comes with an incredible facial landmark detection performance. Highlighted red points show some facial landmarks such as eyes, nose and mouth. That's why, alignment score of RetinaFace is high as well.

You can find out more about RetinaFace on this repo.

API - Demo

Deepface serves an API as well. You can clone /api/api.py and pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.

python api.py

Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be http://127.0.0.1:5000/verify for face recognition, http://127.0.0.1:5000/analyze for facial attribute analysis, and http://127.0.0.1:5000/represent for vector representation. You should pass input images as base64 encoded string in this case. Here, you can find a postman project.

Tech Stack - Vlog, Tutorial

Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images.

embedding = DeepFace.represent(img_path = "img.jpg", model_name = 'Facenet')

Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size.

Contribution

Pull requests are welcome. You should run the unit tests locally by running test/unit_tests.py. Please share the unit test result logs in the PR. Deepface is currently compatible with TF 1 and 2 versions. Change requests should satisfy those requirements both.

Support

There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏

You can also support this work on Patreon

 

Citation

Please cite deepface in your publications if it helps your research. Here are its BibTeX entries:

@inproceedings{serengil2020lightface,
  title        = {LightFace: A Hybrid Deep Face Recognition Framework},
  author       = {Serengil, Sefik Ilkin and Ozpinar, Alper},
  booktitle    = {2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
  pages        = {23-27},
  year         = {2020},
  doi          = {10.1109/ASYU50717.2020.9259802},
  url          = {https://doi.org/10.1109/ASYU50717.2020.9259802},
  organization = {IEEE}
}
@inproceedings{serengil2021lightface,
  title        = {HyperExtended LightFace: A Facial Attribute Analysis Framework},
  author       = {Serengil, Sefik Ilkin and Ozpinar, Alper},
  booktitle    = {2021 International Conference on Engineering and Emerging Technologies (ICEET)},
  pages        = {1-4},
  year         = {2021},
  doi          = {10.1109/ICEET53442.2021.9659697},
  url.         = {https://doi.org/10.1109/ICEET53442.2021.9659697},
  organization = {IEEE}
}

Also, if you use deepface in your GitHub projects, please add deepface in the requirements.txt.

Author: Serengil
Source Code: https://github.com/serengil/deepface 
License: MIT License

#python #machine-learning 

Top 6 Alternatives To Hugging Face

  • With Hugging Face raising $40 million funding, NLPs has the potential to provide us with a smarter world ahead.

In recent news, US-based NLP startup, Hugging Face  has raised a whopping $40 million in funding. The company is building a large open-source community to help the NLP ecosystem grow. Its transformers library is a python-based library that exposes an API for using a variety of well-known transformer architectures such as BERT, RoBERTa, GPT-2, and DistilBERT. Here is a list of the top alternatives to Hugging Face .

Watson Assistant

LUIS:

Lex

Dialogflow

#opinions #alternatives to hugging face #chatbot #hugging face #hugging face ai #hugging face chatbot #hugging face gpt-2 #hugging face nlp #hugging face transformer #ibm watson #nlp ai #nlp models #transformers

Vern  Greenholt

Vern Greenholt

1596386640

CWT Travel Agency Faces $4.5M Ransom in Cyberattack, Report

The corporate-travel leader has confirmed an attack that knocked systems offline.

CWT, a giant in the corporate travel agency world with a global clientele, may have faced payment of $4.5 million to unknown hackers in the wake of a ransomware attack.

Independent malware hunter @JAMESWT tweeted on Thursday that a malware sample used against CWT (formerly known as Carlson Wagonlit Travel) had been uploaded to VirusTotal on July 27; he also included a ransom note indicating that the ransomware in question is Ragnar Locker.

In a media statement to Threatpost, CWT confirmed the cyberattack, which it said took place this past weekend: “We can confirm that after temporarily shutting down our systems as a precautionary measure, our systems are back online and the incident has now ceased.”

@JAMESWT also reported that the ransom demanded clocked in at 414 Bitcoin, or about $4.5 million at the current exchange rate. A CWT spokesperson declined to comment on whether the ransom was paid, or any technical details of the attack, or how it was able to recover so quickly.

Despite assurances of recovery, the impact of the incident could be wide: CWT says that it provides travel services to 33 percent of the Fortune 500 and countless smaller companies. And according to the ransom note uploaded by @JAMESWT, the hackers claim to have downloaded 2TB of the firm’s data, including “billing info, insurance cases, financial reports, business audit, banking accounts…corporate correspondence…[and] information about your clients such as AXA Equitable, Abbot Laboratories, AIG, Amazon, Boston Scientific, Facebook, J&J, SONOCO, Estee Lauder and many others.”

If true, the tactic fits in with the one-two punch trend that many ransomware operators have taken of late – locking up files, but also stealing and threatening to release sensitive data if victims don’t pay up. Such was the case of celebrity law firm Grubman Shire Meiselas & Sacks, which was hit with the REvil ransomware in May. Attackers threatened to leak 756 gigabytes of stolen data, including personal info on Lady Gaga, Drake and Madonna.

And in fact, the attackers behind the Ragnar Locker ransomware in particular are known for stealing data before encrypting networks, as was the case in April, in an attack on the North American network of Energias de Portugal (EDP). The cyberattackers claimed to have stolen 10 TB of sensitive company data, and demanded a payment of 1,580 Bitcoin (approximately $11 million).

“Ragnar Locker is a novel and insidious ransomware group, as Portuguese energy provider EDP found out earlier this year,” Matt Walmsley, EMEA director at Vectra, said via email. “Mirroring the ‘name and shame’ tactic used by Maze Group ransomware, victim’s data is exfiltrated prior to encryption and used to leverage ransomware payments. The bullying tactics used by these ransomware groups are making attacks even more expensive, and they are not going to stop any time soon, particularly within the current climate.”

#breach #malware #data analysisa

Amitesh Travels In Madurai | Travel Agency In Madurai | Travel Agent In Madurai

Amitesh Travels in Madurai is the best tour operator in Madurai. Since I have experience with this tour agency I am saying that they are the best. We provide cost-effective devotional, Honeymoon, Seasonal Tours, Business Tours, Family Tours, Industrial packages that are the best when comparing with others.

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  • Well Experienced Drivers
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Travel Agency in Madurai offers exclusive tailor-made tour packages for individuals and groups of medium & large with choice of destinations. Before booking tour operators from Madurai City, fill the form via Amitesh Travels Get Free Quote and Compare the prices and choose the best domestic tour packages from via with affordable rates.

#travel #travels #tour #trip #tourism #travellers

Monty  Boehm

Monty Boehm

1640622240

Automatically Tag A Branch with The Next Semantic Version Tag

Auto-Tag

PyPI PyPI - Implementation PyPI - Python Version codecov PyPI - License

Automatically tag a branch with the next semantic version tag.

This is useful if you want to generate tags every time something is merged. Microservice and GitOps repository are good candidates for this type of action.

TOC

How to install

~ $ pip install auto-tag

To see if it works, you can try

~ $ auto-tag  -h
usage: auto-tag [-h] [-b BRANCH] [-r REPO]
                [-u [UPSTREAM_REMOTE [UPSTREAM_REMOTE ...]]]
                [-l {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}]
                [--name NAME] [--email EMAIL] [-c CONFIG]
                [--skip-tag-if-one-already-present] [--append-v-to-tag]
                [--tag-search-strategy {biggest-tag-in-repo,biggest-tag-in-branch,latest-tag-in-repo,latest-tag-in-branch}]

.....

How it Works

The flow is as follows:

  • figure our repository based on the argument
  • load detectors from file if specified (-c option), if none specified load default ones (see Detectors)
  • check for the last tag (depending on the search strategy see Search Strategy
  • look at all commits done after that tag on a specific branch (or from the start of the repository if no tag is found)
  • apply the detector (see Detectors) on each commit and save the highest change detected (PATH, MINOR, MAJOR)
  • bump the last tag with the approbate change and apply it using the default git author in the system or a specific one (see Git Author)
  • if an upstream was specified push the tag to that upstream

Examples

Here we can see in commit 2245d5d that it stats with feature( so the latest know tag (0.2.1) was bumped to 0.3.0

~ $ git log --oneline
2245d5d (HEAD -> master) feature(component) commit #4
939322f commit #3
9ef3be6 (tag: 0.2.1) commit #2
0ee81b0 commit #1
~ $ auto-tag
2019-08-31 14:10:24,626: Start tagging <git.Repo "/Users/matei/git/test-auto-tag-branch/.git">
2019-08-31 14:10:24,649: Bumping tag 0.2.1 -> 0.3.0
2019-08-31 14:10:24,658: No push remote was specified
~ $ git log --oneline
2245d5d (HEAD -> master, tag: 0.3.0) feature(component) commit #4
939322f commit #3
9ef3be6 (tag: 0.2.1) commit #2
0ee81b0 commit #1

In this example we can see 2245d5deb5d97d288b7926be62d051b7eed35c98 introducing a feature that will trigger a MINOR change but we can also see 0de444695e3208b74d0b3ed7fd20fd0be4b2992e having a BREAKING_CHANGE that will introduce a MAJOR bump, this is the reason the tag moved from 0.2.1 to 1.0.0

~ $ git log
commit 0de444695e3208b74d0b3ed7fd20fd0be4b2992e (HEAD -> master)
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 21:58:01 2019 +0300

    fix(something) ....

    BREAKING_CHANGE: this must trigger major version bump

commit 65bf4b17669ea52f84fd1dfa4e4feadbc299a80e
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 21:57:47 2019 +0300

    fix(something) ....

commit 2245d5deb5d97d288b7926be62d051b7eed35c98
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 19:52:10 2019 +0300

    feature(component) commit #4

commit 939322f1efaa1c07b7ed33f2923526f327975cfc
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 19:51:24 2019 +0300

    commit #3

commit 9ef3be64c803d7d8d3b80596485eac18e80cb89d (tag: 0.2.1)
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 19:51:18 2019 +0300

    commit #2

commit 0ee81b0bed209941720ee602f76341bcb115b87d
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 19:50:25 2019 +0300

    commit #1
~ $ auto-tag
2019-08-31 14:10:24,626: Start tagging <git.Repo "/Users/matei/git/test-auto-tag-branch/.git">
2019-08-31 14:10:24,649: Bumping tag 0.2.1 -> 1.0.0
2019-08-31 14:10:24,658: No push remote was specified
~ $ git log
commit 0de444695e3208b74d0b3ed7fd20fd0be4b2992e (HEAD -> master, tag: 1.0.0)
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 21:58:01 2019 +0300

    fix(something) ....

    BREAKING_CHANGE: this must trigger major version bump

commit 65bf4b17669ea52f84fd1dfa4e4feadbc299a80e
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 21:57:47 2019 +0300

    fix(something) ....

commit 2245d5deb5d97d288b7926be62d051b7eed35c98
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 19:52:10 2019 +0300

    feature(component) commit #4

commit 939322f1efaa1c07b7ed33f2923526f327975cfc
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 19:51:24 2019 +0300

    commit #3

commit 9ef3be64c803d7d8d3b80596485eac18e80cb89d (tag: 0.2.1)
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 19:51:18 2019 +0300

    commit #2

commit 0ee81b0bed209941720ee602f76341bcb115b87d
Author: Matei-Marius Micu <micumatei@gmail.com>
Date:   Fri Aug 30 19:50:25 2019 +0300

    commit #1

Detectors

If you want to detect what commit enforces a specific tag bump(PATH, MINOR, MAJOR) you can configure detectors. They are configured in a yaml file that looks like this:

detectors:

  check_for_feature_heading:
    type: CommitMessageHeadStartsWithDetector
    produce_type_change: MINOR
    params:
      pattern: 'feature'


  check_for_breaking_change:
    type: CommitMessageContainsDetector
    produce_type_change: MAJOR
    params:
      pattern: 'BREAKING_CHANGE'
      case_sensitive: false

Here is the default configuration for detectors if none is specified. We can see we have two detectors check_for_feature_heading and check_for_breaking_change, with a type, what change they will trigger and specific parameters for each one. This configuration will do the following:

  • if the commit message starts with feature( a MINOR change will BE triggered
  • if the commit has BREAKIN_CHANGE in the message a MAJOR change will be triggered The bump on the tag will be based on the higher priority found.

The type and produce_type_change parameters are required params is specific to every detector.

To pass the file to the process just use the -c CLI parameter.

Currently we support the following triggers:

  • CommitMessageHeadStartsWithDetector
    • Parameters:
      • case_sensitive of type bool, if the comparison is case sensitive
      • strip of type bool, if we strip the spaces from the commit message
      • pattern of type string, what pattern is searched at the start of the commit message
  • CommitMessageContainsDetector
    • case_sensitive of type bool, if the comparison is case sensitive
    • strip of type bool, if we strip the spaces from the commit message
    • pattern of type string, what pattern is searched in the body of the commit message
  • CommitMessageMatchesRegexDetector
    • strip of type bool, if we strip the spaces from the commit message
    • pattern of type string, what regex pattern to match against the commit message

The regex detector is the most powerful one.

Git Author

When creating and tag we need to specify a git author, if a global one is not set (or if we want to make this one with a specific user), we have the option to specify one. The following options will add a temporary config to this repository(local config). After the tag was created it will restore the existing config (if any was present)

  --name NAME           User name used for creating git objects.If not
                        specified the system one will be used.
  --email EMAIL         Email name used for creating git objects.If not
                        specified the system one will be used.

If another user interacts with git while this process is taking place it will use the temporary config, but we assume we are run in a CI pipeline and this is the only process interacting with git.

Search Strategy

If you want to bump a tag first you need to find the last one, we have a few implementations to search for the last tag that can be configured with --tag-search-strategy CLI option.

  • biggest-tag-in-repo consider all tags in the repository as semantic versions and pick the biggest one
  • biggest-tag-in-branch consider all tags on the specified branch as semantic versions and pick the biggest one
  • latest-tag-in-repo compare commit date for each commit that has a tag in the repository and take the latest
  • latest-tag-in-branch compare commit date for each commit that has a tag one the specifid branch and take the latest

Download Details: 
Author: Mateimicu
Source Code: https://github.com/mateimicu/auto-tag 
License: View license

#git #github