Four Secrets To Help You Stop Long-Term Fraud Attacks


Today, bad actors attacking digital businesses and user touchpoints rrely on a complex ecosystem to help them carry out their work. As businesses face an organized and formidable enemy, they can still successfully stop cyber attacks by undermining the return on investment behind them. If the attackers cannot make any money, they will stop what they are doing. It’s that simple.

As is, attackers are able to share data and tools to freely launch scripted attacks among themselves to attack multiple companies. These tools are often tested on a company’s website and, if proven effective, deployed at scale in the digital commerce landscape. They are highly scalable and easily reproducible, which means that attackers spend relatively little time and money on attacks.

Fighting off these attacks can be difficult for businesses, as they typically deploy defenses to stop specific attacks, which in turn rotates attackers and leads to perpetual cat-and-mouse play. The secret to tackling the spectrum of attack methods is to find ways to increase the time, cost, and effort required for bad actors to carry out attacks. Some examples of this could be making it more expensive for attackers to purchase proxies using robust IP intelligence. Or the device’s fingerprints forcing them to invest in more software. The point is that by making their attacks more expensive and time consuming, it forces the attackers to stop. Here are the main steps to achieve this.

Embrace zero tolerance for bad bots

Bots are the key to making money from fraudulent attacks. Bots allow attackers to tackle a large number of targets with minimal effort and expense. It is this scale that makes attacks profitable.

Now, of course, there are good bots, such as search engine bots, but we are talking about automation used for malicious purposes, such as Credentials stuffing, scraping, or hoarding of stocks.

Businesses must take a tiered approach to identify and eliminate automated attacks. This should involve dynamic real-time traffic assessment, segmentation of traffic based on level of suspicion, and then providing an appropriate response, such as escalation challenges designed against machine vision technology that can shut down machine vision technology. malicious robots.

Leverage shared intelligence

Share intelligence between trusted companies can also ensure that existing attacks cannot be copied and pasted across different organizations. Patterns will emerge very quickly, indicative of a malicious session when analyzing risk signals on a global network.

A sophisticated analysis of visitor behavior from the start of the customer journey is essential today; by eliminating the bad actors at the perimeter, companies can prevent a host of costly and damaging downstream abuses.

Criminals often launch the same attacks against many different targets; With a holistic view of attack patterns, these can be easier to identify and stop before they wreak havoc on businesses and consumers.

Combine robust detection with advanced authentication

Businesses need a detection platform that can determine intent and actions, rather than just collecting identity data. This means performing a thorough traffic analysis and segmenting all suspicious activity to then be dealt with appropriately.

Many attackers use proxies to hide their true identity when attacking a business. This can make malicious bots and malicious humans appear different than they really are, by masking their geography, IP address, and other identifying factors. Businesses need to collect and analyze real-time information to uncover fraudulent behavior across devices and networks.

Analysis and machine learning

stop fraudulent attacksCustomer interaction assessment

Detection of an anomaly

Historical attack patterns

stop fraudulent attacksTraffic model analysis

Then, if necessary, the application of adaptive intensification can be applied to the potentially suspicious traffic. This is frustrating and wastes attackers time and resources.

Change the fraud attack surface

Attackers must be able to control decision points while on a company’s digital property and deploy the appropriate tactics to evade established anti-fraud measures. They are adept at masquerading as legitimate consumers by using hacked devices, stolen identities, and masking their IP address and other credentials in single request attacks.

It’s time to take control of the scammers. Businesses can really benefit from using third-party solutions to act as a buffer between themselves and the bad actors who are so used to attacking them.

This shifts the attack surface by redirecting suspicious sessions to an intermediate battlefield where they obey your rules. Taking back control of these decision points means that fraudsters will quickly be frustrated when their proven methods stop working. When they can no longer fall back on their existing arsenal of automated tools, stolen identities, and click on the farms, this will escalate costs for reduced earnings, removing the financial motivation behind organized fraud

Arkose Labs’ approach to stopping fraudulent attacks

Arkose Labs takes a unique approach to attack prevention and user security, one that seeks to undermine the financial incentive behind attacks, thereby deterring bad actors from launching attacks in the first place. A key success factor for the platform is seeing attacks not only detected, but also dropped, providing longer term solutions.

Arkose Labs’ fraud and abuse platform combines real-time insights, rich analytics and adaptive challenges to gradually decrease the profitability of attacks while adapting to changing attack models.

stop fraudulent attacks

Rather than outright blocking traffic and negatively impacting user experience, Arkose Labs’ approach is to use targeted friction, reserved only for high-risk traffic. Our custom application challenges are context-based adaptive visual challenges that will thwart automated and human account takeover attempts, wasting fraudsters time and resources. Thanks to a sophisticated decision engine, the vast majority of good users can pass without being challenged.

By offsetting the balance between the time and money that fraudsters spend on attacks versus what they get out of them, they will eventually move on. It is the most effective way to stop fraud for real.

*** This is a Syndicated Security Bloggers Network blog by Arkose Laboratories written by Bryan Yurcan. Read the original post on:


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