Bad bots are one of the most serious threats to business today. Malicious bot traffic can lead to a decrease in website performance, distortion of information about the availability of goods and services of online platforms, violation of the confidentiality of personal data and, as a result, to customer churn and loss of profits.
Bots attack websites, mobile apps and APIs, causing a range of business issues such as account hijacking, DDoS attacks on apps, API misuse, web scraping, spam generation, analytics corruption and ad fraud ...
At the same time, good bots contribute to the growth of traffic and audience of web resources. They crawl site pages to determine search engine rankings and index real-time updates, and also allow users to find the best price for an item or identify stolen content. The ability to distinguish between good and bad bots is a top priority for modern companies. However, according to Radware's Web Application Security Report, 79% of organizations cannot accurately distinguish between useful and malicious bot traffic.
The traffic of bots and the threat of their destructive impact on the business is rapidly increasing. This means that tailored bot protection solutions are critical to business continuity and success. The situation has become even more complicated with the emergence of a new generation of malicious bots (so-called humanoid bots) that can imitate human / real user behavior and easily bypass traditional standard information security and protection systems against bots.
Developing and maintaining a full-fledged anti-bot solution requires enormous resources, including constant tuning, adjusting security policies, and managing exceptions to reduce false positives - an effort and expense that only very large companies can afford. Analyst organizations such as Forrester Research and Gartner are increasingly emphasizing the importance of anti-bot solutions for businesses of all sizes. What criteria should be followed when choosing and comparing solutions to counter bots? Such information, and even more so a consensus on this matter, is practically absent. This article provides an overview of the key properties that an effective solution should have to protect websites, mobile applications and APIs from bot attacks.
When choosing a solution to mitigate bot attacks and determining the most appropriate tool in each case, it is important to consider the criteria presented below.
Accuracy in detecting bots is critical. A solution to repel bot attacks should not only distinguish useful bots from malicious bots, but also improve the quality of service for users and allow access to authorized partner bots and search robots. Maintaining a consistent quality of service for users on sites such as retail e-commerce portals during peak hours is not an easy task. The security solution must be scalable to handle surges in traffic and peak loads.
It is equally important to maintain a minimum level of false positives so that the work and quality of services for users are not disrupted. Real users should never solve CAPTCHA tests or prove that they are not a bot. An enterprise-class bot detection engine must have deep learning and automated optimization techniques. These are necessary conditions for identifying and blocking continuously evolving bots, which constantly change their characteristics to bypass the means of detecting basic defense systems.
An important advantage is the ability to integrate via API - such a solution will have minimal impact on your online resources.
Finally, and ideally, a bot mitigation solution provider should have multiple distributed points of presence to maximize service availability and minimize latency.
Bot countermeasures equipped with advanced technologies such as machine learning help automate the management process and significantly reduce the team's time required to track bot traffic and repel bot attacks. Automatic response to bot threats, as well as the absence of the need for manual tuning of the system, significantly reduce its total cost of ownership.
From a financial point of view, it makes sense to purchase a subscription-based anti-bot solution in the cloud to minimize capital costs. In this option, companies receive all the benefits of protecting their online resources without the need for investment and prepayment.
Bots attack websites, mobile apps and APIs, causing a range of business issues such as account hijacking, DDoS attacks on apps, API misuse, web scraping, spam generation, analytics corruption and ad fraud ...
At the same time, good bots contribute to the growth of traffic and audience of web resources. They crawl site pages to determine search engine rankings and index real-time updates, and also allow users to find the best price for an item or identify stolen content. The ability to distinguish between good and bad bots is a top priority for modern companies. However, according to Radware's Web Application Security Report, 79% of organizations cannot accurately distinguish between useful and malicious bot traffic.
The traffic of bots and the threat of their destructive impact on the business is rapidly increasing. This means that tailored bot protection solutions are critical to business continuity and success. The situation has become even more complicated with the emergence of a new generation of malicious bots (so-called humanoid bots) that can imitate human / real user behavior and easily bypass traditional standard information security and protection systems against bots.
Developing and maintaining a full-fledged anti-bot solution requires enormous resources, including constant tuning, adjusting security policies, and managing exceptions to reduce false positives - an effort and expense that only very large companies can afford. Analyst organizations such as Forrester Research and Gartner are increasingly emphasizing the importance of anti-bot solutions for businesses of all sizes. What criteria should be followed when choosing and comparing solutions to counter bots? Such information, and even more so a consensus on this matter, is practically absent. This article provides an overview of the key properties that an effective solution should have to protect websites, mobile applications and APIs from bot attacks.
Criteria for choosing a solution for tracking bot traffic and protecting against bot attacks
Countering sophisticated and automated bot attacks requires in-depth analysis of the attackers' intentions and tactics used. According to Forrester's “The Forrester New Wave ™: Bot Management,” the main factors in evaluating anti-bot solutions are their ability to detect and respond to attacks, and research and collect data on bot threats. Anti-bot tools vary greatly in how they identify threats. In addition, many of these defenses have very limited - or no - capabilities to automatically respond to an attack. Anti-bot tools need to be able to identify real-time bot traffic assignments to differentiate traffic from good bots from bad bots.When choosing a solution to mitigate bot attacks and determining the most appropriate tool in each case, it is important to consider the criteria presented below.
Basic functionality of a solution to protect against bots
When considering solutions, it is important to compare the set of possible actions for responding to an attack: blocking, restricting traffic, the ability to "outplay" a competitor and provide fake information, as well as custom actions based on signatures and types of bots ... An effective solution should allow different approaches to be applied for different sections and subdomains of the site. Additionally, an enterprise-grade solution should be able to connect popular analytics tools such as Adobe or Google Analytics to report bot traffic.Ability to detect large-scale distributed humanlike bot attacks
When deciding on a counter-bots solution, it is helpful to understand what technology the tool uses to identify and mitigate sophisticated bypass attacks. These threats include large-scale distributed botnet attacks and “low and slow” attacks that cannot be detected by traditional security tools. Here are just a few examples: in a dynamic IP attack, it is useless to use IP list attack mitigation; setting rate-limiting without using behavioral analysis mechanisms means disrupting the work of real users during an attack. Some firewalls or rate-limiting tools in content delivery network (CDN) services are not capable of detecting complex bot attacks, imitating human / real user behavior. Given the dramatic growth in traffic from cleverly organized humanoid bots, much more advanced technology is needed to detect and repel them. The focus when choosing and evaluating a solution to protect against bots should be on various methodologies for detecting bots, for example: fingerprinting devices and browsers, analyzing behavior and intent, collecting data on bot signatures (collective bot intelligence) and conducting in-house threat research. bots - as well as other fundamental technologies.Opportunities for continuous adaptation of the bot detection engine to reflect new threats
- How advanced is bot detection technology?
- Does it use fingerprinting techniques for devices and browsers?
- Is intent analysis (machine learning-based attack detection models that ascertain the intent of each site visitor and provide much higher accuracy in detecting an attack compared to simple behavioral analysis) is used in addition to analyzing user behavior, collecting data on bots (collective bot intelligence), dynamic Turing tests, etc.?
- How advanced and effective are fingerprinting and user behavior models?
- Do these models use collective intelligence on threats?
User Experience Impact - Latency, Accuracy, and Scalability
A delay in website and application performance reduces the quality of service for users. A solution to protect against bot attacks should not increase this delay, but, on the contrary, identify the causes and help to eliminate it.Accuracy in detecting bots is critical. A solution to repel bot attacks should not only distinguish useful bots from malicious bots, but also improve the quality of service for users and allow access to authorized partner bots and search robots. Maintaining a consistent quality of service for users on sites such as retail e-commerce portals during peak hours is not an easy task. The security solution must be scalable to handle surges in traffic and peak loads.
It is equally important to maintain a minimum level of false positives so that the work and quality of services for users are not disrupted. Real users should never solve CAPTCHA tests or prove that they are not a bot. An enterprise-class bot detection engine must have deep learning and automated optimization techniques. These are necessary conditions for identifying and blocking continuously evolving bots, which constantly change their characteristics to bypass the means of detecting basic defense systems.
Expandability and flexibility
Taking a serious approach to protecting against bots goes beyond just securing your website. An enterprise-class anti-bot solution must ensure the security of all online resources, including website, mobile applications and APIs. Protecting mobile apps and APIs is equally important. You also need the ability to integrate the solution with partner systems and important partner APIs.Flexible deployment options
A bot protection solution should be easy to deploy and work with existing infrastructure with services such as content delivery networks and firewalls (CDNs and WAFs), as well as various technology stacks and application servers. Preferred a solution that offers a variety of integration options, including plugins for web servers / content management networks (CDN) / content management systems (CMS), development kits (SDKs) for Java, PHP, .NET, Python, ColdFusion, Node. js, etc., as well as JavaScript tags and virtual devices.An important advantage is the ability to integrate via API - such a solution will have minimal impact on your online resources.
Finally, and ideally, a bot mitigation solution provider should have multiple distributed points of presence to maximize service availability and minimize latency.
Automatically repel bot attacks or manually configure policies
Page requests for popular websites can run in the millions per minute, and the processing of information to detect bots must be done in real time. This means that it is impossible to manually tweak the solution - even adding suspicious IP ranges is useless to identify bots that circulate between a huge number of addresses in order to bypass detection tools. The key question that needs to be answered is whether a separate specialized team will be required to manage the anti-bot protection tool, or is this solution capable of working autonomously after initial configuration?Bot countermeasures equipped with advanced technologies such as machine learning help automate the management process and significantly reduce the team's time required to track bot traffic and repel bot attacks. Automatic response to bot threats, as well as the absence of the need for manual tuning of the system, significantly reduce its total cost of ownership.
Development or purchase of a specialized solution
Large organizations have the resources to develop their own in-house solution to protect against bots, but most companies do not have the time, resources, or funds for this task. Building an adaptive and high-tech bot protection solution that can resist the ever-evolving bots can take years of dedicated development.From a financial point of view, it makes sense to purchase a subscription-based anti-bot solution in the cloud to minimize capital costs. In this option, companies receive all the benefits of protecting their online resources without the need for investment and prepayment.
