How to Detect and Stop Social Media Fraud

How to Detect and Stop Social Media Fraud

Social Media – The new stage for threat actors

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In the online world, fraud happens fast. We know this to be true at Bolster through the threat intelligence our systems gather and the customers we serve. In 2020, we detected over 6.9 million phishing and scam pages, resulting in over $320 billion dollars in brand losses and over $1.2 trillion dollars in fraudulent transactions. (read full report). And as 2021 comes to an end, we expect the numbers to notch even higher.

Traditionally, much of the fraud stems from threat actors targeting companies’ websites through typosquatting attacks resulting in fake websites designed to harvest credentials or syphon off business. Recently though, threat actors have shifted their sights to social media platforms to stage fraud and scam attacks. For security professionals, well, they’re finding themselves once again behind the brand protection 8-ball in a highly reactive mode trying to detect and remove social media fraud and scams. Without new tools and technologies coming to their aide, security personnel will continue to struggle to get ahead of the online fraud threat curve.

Detecting Social Media Fraud – Why is it so hard?

Detecting social media fraud and scams is difficult for a number of reasons.

  1. There are a lot of social media platforms. First, and perhaps most obvious, there are a lot of social media platforms out there on the Internet. Sure, there are the big platforms like Facebook, Twitter, YouTube but there are many more spanning social, news, content, personal and sharing sites. For security professionals this means a virtually endless set of tasks scouring various social media platforms on a continual basis.
  2. Social media fraud and scams comes in many forms. Besides a lot of different platforms to monitor, there also are a lot of different types of social media fraud, scams and infringements to be aware of (see examples below). Sometimes it’s fake ads leading unsuspecting visitors to fake websites or offers. Other times, it’s counterfeit product or service sales often complete with fake celebrity endorsements designed to dupe customers. And in yet instances, it’s fake profile pages impersonating executives or celebrities, or threat actors staging attacks on legitimate profile pages through malicious links posted in comment fields. Staying on top of all the different types of social media scams is yet another full-time job.
  3. Social media platforms aren’t governed like websites. This might be obvious but worth noting, nonetheless. Unlike websites that are governed by a global system for registering domain names, and a well-understood set of organizations and policies to report abuse (hosting provider, registrar, registry, ICAAN), with social media platforms it’s more like the proverbial wild west. Each social media platform has its own policies and procedures for creating accounts, posting content, and reporting abuse. For security teams, it means traditional detection techniques like scouring new domain name registration lists aren’t an option, and instead platform-specific approaches need to be adopted to find fraudulent activities or scams.
  4. Taking down and removing social media fraud and scams is a pain in the a**! This is the corollary of each social media platform operating independently. When social media fraud, scams or infringements are in fact detected, the steps that need to be taken to perform takedowns or removals vary drastically from platform to platform. This means security teams not only have to learn how to report abuse or fraud on a platform-by-platform basis, but it also means that monitoring takedown success also must be done on a per platform basis. In comparison, with websites, it is far simpler to initiate a takedown and to monitor takedown success globally by examining changes in global DNS records and SSL certificate transactions, for example.
How to Detect and Stop Social Media Fraud

Figure 1. Fake Apple iPhone Giveaway on Facebook

How to Detect and Stop Social Media Fraud

Figure 2. Fake Apple iPhone Giveaway on YouTube

Critical ingredients to eliminate social media fraud and scams

This isn’t meant to be a scare piece, but social media fraud and scams can wreak havoc not only on your business and brand but also on your security team. And one thing for sure, if this is a problem for your business, it’s not something that can easily be remedied with SOC analysts alone. In fact, it will mostly like grind them to the ground. The only way out given the scope and scale of how threat actors operate is with 1.) automation and 2.) machine learning.  No ifs, ands, or buts.

  1. Automation – Self-driving workflows. With all the different social media platforms out there, and the myriad of scam types, manual workflows for discovery, inspection and evidence gathering simply won’t scale. Instead, what is needed are automated workflows geared to scrape social media sites, retrieving and inspecting ads, content, and URLs. These workflows need to be performed on a daily basis as a baseline operation for fraud and scam detection. Automating these tasks is obviously a huge time-saver for SOC teams by moving repetitive tasks to the background allowing more focus to be applied to mitigation and remediation versus discovery.
  2. Machine learning – Trained eyes that scale. Machine learning is the other side of the social media protection coin. While automation serves to replace and scale repetitive human tasks, machine learning replaces and scales the trained eyes for detection and inspection. Done right, machine learning can offload the process of detecting fake ads, counterfeit product, logo infringements, even content abuses. And unlike a team of analysts, machine learning process don’t get tired over time, experience eye-fatigue, or generally just get tired. Instead, with machine learning you have a detection and inspection engine running 24/7, allowing SOC analysts to assume more valuable roles in the overall security operation…and avoid burnout.

Bolster Social Media Protection solution

At Bolster we recently extended our automation and machine learning capabilities to address social media fraud and scams. With the new capabilities customers can automatically monitor a growing number of social media platforms for fake ads, counterfeit product, trademark infringements, impersonations, phishing campaigns, and content abuses. Bolster’s automated workflows can be spun up quickly enabling the machine learning algorithms to detect social media fraud and infringements in real-time.

As with other modules in the platform, social media threat data is presented in an intuitive dashboard making it easy to track social media threats throughout the complete lifecycle from discovery through to takedown and removal. Bolster’s detection engine, powered by natural language processing, computer vision and deep learning models, is capable of detecting fraud in less than 100 milliseconds with an astonishingly low false-positive rate of 1 in 100,000.

When malicious activities are detected, detailed evidence is supplied including high-resolution screenshots complete with logo detection to facilitate investigations and takedowns. And for any URLs detected, our system will automatically scan the URLs using our CheckPhish engine to determine disposition (clean site, phishing site, scam site). If phishing is detected, the system will issue an automated takedown request resulting in a site takedown in as little as 3 minutes.

How to Detect and Stop Social Media Fraud

Figure 3. Bolster Social Media Protection Dashboard

Test drive Bolster today!

Interested in learning more and seeing our social media protection capabilities in action? Our team is ready when you are. Simply fill out our Request a Demo form for a 30-minute run-through of capabilities. We’re confident the demo will be eye-opening and confident that we have a social media protection solution that is right for your business and brand.

Request a 30-minute demo

Learn more:
Bolster Social Media Protection solution page

*** This is a Security Bloggers Network syndicated blog from Bolster Blog authored by Jeff Baher. Read the original post at: