What Are Social Engineering Attacks? Techniques & Protection

by Tamas Kadar
Fraudsters are just as tech-savvy as they are lazy. How can they multiply attacks to target your business? Through the use of bots.
Thankfully, bot detection is easier than you might first think.
Bot detection is any process designed to identify the presence of bot traffic on a website, server, network, etc. It is made to detect individual bots and scripts, as well as botnets comprised of several bots, which can be in the thousands. The idea is to be able to tell human traffic from bot traffic.
In general terms, you may want to block your average bot. But they’re not all malicious bots – but there are also useful bots out there that you will certainly want to allow, such as Google’s spiders, which index web pages.
In the context of fraud detection, bot traffic always has a negative connotation, as fraudsters will attempt to use bots in order to automate and scale their attempts against a business or organization.
As a business, your goal is to ensure web traffic comes from legitimate users, so you’ll want to detect bots and block their access to all or certain parts of your infrastructure. This is because fraudsters rely on bots to automate actions such as taking over user accounts, making payments with stolen credit cards, or digital onboarding with fake identity details.
To make matters worse, bot attacks are on the rise globally. Cybersecurity firm Spamhaus identified a 23% increase in botnet traffic in Q4 2021 compared with the previous quarter.
Here is a list of where this automated traffic comes from:
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Common bot detection techniques for finding and stopping bot traffic include:
· flagging suspicious tools that enable bots
· using privacy-focused browsers
· using software and apps specifically designed to detect bots
Bot detection techniques vary in their efficacy. As such, if you’re keen to protect your business from the negative consequences of bot traffic, it’s a good idea to use a combination of approaches. Let’s take a look at some of these:
The best strategy is to implement a range of bot detection techniques to ensure your business is well protected.
When it comes to the detection of basic bots, adding a few rudimentary technical hurdles will work. For instance, CAPTCHA is known to reduce bot-driven form submissions by 88%.
Things get more challenging when you deal with bots designed to replicate human behavior.
Referral fraudsters, for instance, utilize sophisticated automation designed to trigger rewards an starting affiliate fraud.
This is an example of what this kind of bot can do:
As you can imagine, complex sequences of events are harder to detect than simple fraud attacks – especially if they’re designed to pass off as a human.
That is, of course, unless you have the right bot detection tool at your disposal.
SEON is a full end-to-end risk mitigation platform that enables fraud detection and bot mitigation through powerful APIs, machine learning and custom rules.
Uniquely, it also harnesses the power of data enrichment, gathering information from 90+ social media and online sources in real-time to learn more about users. Specifically:
SEON also uses device fingerprinting to show exactly how users connect to your site. This is especially good for highlighting emulators and virtual machines for better bot management.
Device fingerprinting examines installed plugins, web browser version, browser window size, screen resolution, language used and more, then automatically creates a device hash.
The device hash can be used along with velocity rules, or velocity checks, to allow you to monitor user actions over time and understand user behavior through data.
If a new user pops up, adds a product to their cart, checks out, then enters three different credit card numbers within a minute, velocity checks can flag this as suspicious.
SEON can also spot suspicious connections between users and transactions. The fact that fraudsters reuse bots to perform the same action multiple times means there will be patterns in the data.
SEON Fraud and Customer Success Manager Conner Keele showed how such data can be used to detect bots when he spotted 700 fraudulent iGaming accounts in one hour. Conner noticed several hundred users signing up from the same internet service provider, all using outlook.com email addresses in the same format (johndoe1234@outlook.com) and all using the same security question. By filtering through the data points, Conner realized he was looking at a fraud ring using bots to sign up and claim rewards (a practice known as bonus abuse).
The key to good bot detection is to combine all of the above in order to model what good or suspicious behavior looks like in the unique context of your business.
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Bot detection starts with gathering data about your user’s behavior and connection. By looking at whether they connect with proxies, VPNs, or Tor, you can increase suspicions. Emulators and virtual machines should also raise red flags. Then you need to compare such data and user actions with others to spot patterns.
An IP address isn’t usually enough to let you know if you’re dealing with a bot or not. However, you can assess whether it points to a VPN, proxy or Tor browser. This can help you raise red flags, depending on the user’s behavior.
The easiest way to block bots is to enable CAPTCHAs. However, it won’t stop the more sophisticated bots. To block those, you need to enable a combination of data enrichment (to learn more about how they connect to your site) and velocity rules (to understand their online behavior).
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Tamás Kádár is the Chief Executive Officer and co-founder of SEON. His mission to create a fraud-free world began after he founded the CEE’s first crypto exchange in 2017 and found it under constant attack. The solution he built now reduces fraud for 5,000+ companies worldwide, including global leaders such as KLM, Avis, and Patreon. In his spare time, he’s devouring data visualizations and injuring himself while doing basic DIY around his London pad.
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