Top botnet detection tools for e-commerce businesses
Believe it or not, humans don’t make up the majority of internet users. That title goes to bots, which comprise 52% of internet traffic. And whether they’re good bots or malicious bots, they all perform a variety of functions.
Good bots are used to crawl websites for search engines and social media feeds. But bad actors can program bots to conduct brute-force attacks and assist in fraudulent activity. And it’s that activity that businesses need to account for when they’re looking for a bot detection solution.
So let’s take a deeper dive into what a botnet is, the kinds of fraud attacks bad actors use bots in, and techniques for detecting bots. Then we’ll explore why machine learning and real-time detection are the future of cybersecurity and fraud prevention.
What is a botnet?
A botnet is a group of internet-connected devices controlled by a central system. Internet-connected devices can include everything from laptops and smartphones to digital doorbells and home security systems.
Unfortunately, lax security in these everyday internet devices make them easy for bad actors to program and use for botnet attacks, account takeover attacks, and Distributed Denial of Service (DDoS) attacks. Some botnets are privately held and controlled. But many are freely available for bad actors to purchase or lease by the hour to launch attacks.
Once a bad actor compromises internet-connected devices to create a botnet, they can program it with username and password combinations or credit card data to test on any number of websites. For example, bad actors can program botnets to go to websites, open particular screens, and fill out checkout pages with thousands of email addresses, credit card details, and shipping addresses. Within hours, a botnet can make thousands of purchases, login attempts, or account changes.
Essentially, bad actors use botnets to circumvent basic velocity checks and carry out common fraud activities like credential stuffing, card testing, and coupon cracking.
- Credential stuffing involves testing hundreds of thousands of combinations of usernames, emails, and passwords in quick succession on a target website. Credential stuffing is a common type of account takeover.
- Card testing occurs when bad actors need to validate stolen or purchased credit card numbers. Once they confirm which credit card numbers are live, they can make larger fraudulent purchases. Card testing is common among QSRs and businesses that sell small, low-dollar-value goods.
- Coupon cracking is a form of promo abuse in which a bad actor finds every possible coupon code for a single campaign. Referral bonuses and loyalty discounts are targets for this kind of activity.
3 bot protection and detection techniques and tools
88% of organizations say malicious bot attacks are becoming more difficult for their security tools to detect, Kount research reports. That makes botnet detection tools and bot protection techniques essential for e-commerce business success.
1. Event-based bot detection
Today, the best technique for bot protection is employing event-based bot detection. Because sophisticated bots can mimic human interactions, basic detection tools can’t differentiate human activity and bot activity. But businesses can embed modern bot protection into their business workflow to compare network, device, and behavioral characteristics with identity trust signals in real time.
Plus, event-based solutions protect the entire customer journey, from account creation and login to payment and checkout. Basic bot detection solutions typically protect the perimeter and identify bots using network characteristics, device characteristics, and behavior characteristics. Event-based bot detection examines these characteristics, along with past behaviors and identity trust signals, to better understand malicious bot behavior.
2. Account takeover protection
One essential tool in botnet detection is an account takeover protection solution. A solution like Kount Control takes a multi-layered approach to customer account protection. It evaluates user behavior, device, and network anomalies to detect high-risk activity from bots programmed to carry out credential stuffing and brute-force attacks.
Kount Control’s protection layer determines in real time whether to allow, decline, or challenge login activity. In addition, e-commerce businesses can customize business policies around login velocities to approve or decline activity automatically. An account takeover protection solution can stop bad actors from breaking into customer accounts to steal data or make purchases.
3. Digital fraud prevention
Another essential tool in botnet detection is a digital fraud prevention solution. A tool like Kount Command uses the largest network of fraud and trust-related signals, advanced AI, and machine learning to identify high-risk activity. Kount’s premiere fraud solution can accurately gauge identity trust and risk levels behind events across the customer journey. This is especially useful for detecting and protecting against bot activity at the point of payment or checkout.
If a bad actor has programmed bots for card testing or coupon code cracking, Kount Command can compare transaction, device, and location data to determine if the bot’s purchase is consistent with activity typically associated with each payment. Kount relays a safety rating for each transaction, based on global network data and custom business policies. A low safety rating may indicate abnormal activity, so the transaction is declined or held for review automatically.
How machine learning is used in cybersecurity
Machine learning is essential for bot detection, fraud prevention, and cybersecurity. Machine learning is what makes it possible for artificial intelligence systems to evaluate past fraud activity and emerging fraud trends. For example, Kount’s AI learns from supervised and unsupervised machine learning.
Supervised machine learning learns from past transaction decisions. And unsupervised machine learning detects emerging fraud attacks. The combination of these two types of machine learning helps businesses increase order acceptance rates and reduce false declines. Plus, improving approve and decline decisions can reduce manual reviews and operational costs.
Is real-time attack detection the future of cybersecurity?
Real-time attack detection is the future of cybersecurity and fraud prevention, especially when it comes to malicious bot activity. A lot can happen in a matter of hours. Only 11% of businesses can detect bot attacks in less than an hour, according to Kount’s research. 34% of businesses say they can detect bot attacks in three to five hours.
The longer it takes to identify and neutralize malicious bot attacks, the more time malicious programs have to cause serious damage. Real-time identity trust decisions can help businesses accept more good orders, reduce false declines, reduce manual reviews, protect against chargebacks, and improve business operations.