Copyright 2018 The Social-Engineer Firewall (SEF)

Written by Christopher Ngo, Jennifer Zou, Kyle O'Brien, and Omri Gabay.

Founded Treehacks 2018, Stanford University.

## Inspiration

No matter how secure your code is, the biggest cybersecurity vulnerability is the human vector. It takes very little to exploit an end-user with social engineering, yet the consequences are severe.

Practically every platform, from banking to social media, to email and corporate data, implements some form of self-service password reset feature based on security questions to authenticate the account “owner.”

Most people wouldn’t think twice to talk about their favourite pet or first car, yet such sensitive information is all that stands between a social engineer and total control of all your private accounts.

## What it does

The Social-Engineer Firewall (SEF) aims to protect us from these threats. Upon activation, SEF actively monitors for known attack signatures with voice to speech transcription courtesy of SoundHound’s Houndify engine. SEF is the world’s first solution to protect the OSI Level 8 (end-user/human) from social engineer attacks.

## How it was built

SEF is a Web Application written in React-Native deployed on Microsoft Azure with node.js. iOS and Android app versions are powered by Expo. Real-time audio monitoring is powered by the Houndify SDK API.

## Todo List

Complete development of TensorFlow model

## Development challenges

Our lack of experience with new technologies provided us with many learning opportunities.
## Inspiration

1 in 2 Canadians will personally experience a mental health issue by age 40, with minority communities at a greater risk. As the mental health epidemic surges and support at its capacity, we sought to build something to connect trained volunteer companions with people in distress in several ways for convenience.

## What it does

Vulnerable individuals are able to call or text any available trained volunteers during a crisis. If needed, they are also able to schedule an in-person meet-up for additional assistance. A 24/7 chatbot is also available to assist through appropriate conversation. You are able to do this anonymously, anywhere, on any device to increase accessibility and comfort.

## How I built it

Using Figma, we designed the front end and exported the frame into Reacts using Acovode for back end development.

## Challenges I ran into

Setting up the firebase to connect to the front end react app.

## Accomplishments that I'm proud of

Proud of the final look of the app/site with its clean, minimalistic design.

## What I learned

The need for mental health accessibility is essential but unmet still with all the recent efforts. Using Figma, firebase and trying out many open-source platforms to build apps.

## What's next for HearMeOut

We hope to increase chatbot’s support and teach it to diagnose mental disorders using publicly accessible data. We also hope to develop a modeled approach with specific guidelines and rules in a variety of languages.
## Inspiration

As technological innovations continue to integrate technology into our lives, our personal property becomes more and more tied to the digital world. Our work, our passwords, our banking information, and more are all stored in our personal computers. Security is ever more imperative—especially so at a university campus like Princeton's, where many students feel comfortable leaving their laptops unattended.

## What it does

Our solution is Cognito—an application that alerts users if their laptops are being used by other people. After launching the app, the user simply types in their phone number, gets in position for a clear photo, and at the press of a button, Cognito will now remember the user's face. If someone other than the main user tries to use the laptop without the user being in frame, Cognito sends a text message to the user, alerting them of the malicious intruder.

## How we built it

The application was written in Java using Eclipse and Maven. We used [Webcam Capture API](https://github.com/sarxos/webcam-capture) to interface with the laptop webcam and [OpenIMAJ](http://openimaj.org/) to initially detect faces. Then, using Microsoft Cognitive Services ([Face API](https://azure.microsoft.com/en-us/services/cognitive-services/face/)), we compare all of the faces in frame to the user's stored face. Finally, if an intruder is detected, we integrated with [Twilio](https://www.twilio.com/) to send an SMS message to the user's phone.

## Challenges we ran into

Ranged from members' adversity to sleep deprivation. Also, the cold (one of our group members is from the tropics).
On a more serious note, for many of us, this was our first time participating in a Hackathon or working on integrating APIs. A significant portion of the time was spent choosing and learning to use them, and even after that, working on bug fixes in the individual components took up a substantial chunk of time.

## Accomplishments that we're proud of

Most of us did not have any experience working with APIs prior, much less under the time constraint of a Hackathon. Thus, the feeling of seeing the final working product that we had worked so hard to develop functioning as intended was pretty satisfying.

## What we learned

We did not have significant project experience outside of class assignments, and so we all learned a lot about APIs, bug fixing, and the development process in general.

## What's next for Cognito

Things we'd like to do:

* Hold a *database* of authorized "friends" that won't trigger a warning (in addition to just the core user).
* Provide more choice regarding actions triggered by detection of an unauthorized user (e.g. sending a photo of the malicious user to the main user, disabling mouse & keyboard, turning off screen, system shutdown altogether).
* Develop a clean and more user-friendly UI.