Bots are basically computer programs that can automate and perform tasks much faster than a human. Bots can be classified as good bots and malicious bots. Good bots are search engine spiders, social bots that establish a service or connection among social networking users, etc. whereas malicious bots are used to launch attacks on websites or applications. This article will elaborate on the different types of bot attacks and bot detection techniques.
What is Bot Detection?
Bot detection is the process of identifying and distinguishing between automated bots and human users. Bot detection stops malicious bots from accessing a website, mobile app, or API. Bots are often employed by fraudsters against businesses to launch attacks. Distributed DoS bots and spambots can overload a server’s resources or post promotional content wherever possible to drive exponential traffic to a website.
Bot detection is becoming increasingly relevant these days with apps and websites emerging as the prime target for bots. Bot detection is used in web security to separate and identify malicious bots that may be used for fraud, data scraping, or launching denial of service attacks, from legitimate bots such as search engine spiders. Bot detection methods protect websites, mobile apps, and APIs from automated attacks. Security solutions designed to detect bots and mitigate fraudulent bot activity have emerged recently to aid organizations in their efforts to reduce risk from bot attacks.
Why Detect Bot Traffic?
Bots are leveraged by cybercriminals for launching malicious activities such as fraud, data scraping, and denial-of-service attacks. Malicious bots also lead to security breaches. According to several sources, 40% of internet traffic comes from bots which makes it all the more important for businesses to monitor and detect bots. Detecting bot traffic helps companies take measures to block, mitigate, or prevent bot attacks and ensure their website or app is generating the desired outcomes.
Here are a few important reasons why you must detect bot traffic:
1. Get the right numbers
Detecting bot traffic is a must if you want the right numbers on the actual traffic visiting your website. While the numbers might seem exponentially high, bots may be responsible for over half of all traffic. Bot detection is essential to avoid being misled by the wrong numbers.
2. Protect your online applications
Bots can pose security risks for applications and e-commerce businesses particularly are prone to bot attacks. Users enter confidential information such as payment details and credentials on e-commerce apps which can be misused by bots. Bots also launch spam login and purchase attempts which will tamper with the stock items displayed to the real users. E-commerce businesses can incur huge losses if bot activities are not kept in check.
3. Prevent monetary losses
Bots can cause downtime which results in financial losses for businesses. DDoS attacks and data exfiltration can result in website downtime and impact business operations. Bots steal information such as login credentials, payment information, and personal data, which can then be used for financial gain. Bots can extract data from websites and also resell it to competitors.
4. Protect brand image
Bots can compromise customer experience to a significant extent and hamper brand reputation. Bot detection is essential to deliver a positive experience to customers and instill trust among them.
4. Get ahead of attackers
Bot detection equips you with the security tools and measures to thwart bot attacks. Hackers are launching sophisticated attacks and bots have emerged to be a serious threat to businesses. Bot detection will enable companies to get ahead of hackers and be proactive with their web and app security.
How do bots attack?
Bots launch attacks in various ways. Bots empower cybercriminals to tamper with the system and mislead users and businesses into taking the wrong actions. Hackers are now equipped with sophisticated tools and technologies to dodge bot detection measures implemented by businesses. Let’s understand in brief how bots can be detrimental to your business.
1. Bots hoard inventory
Bots hoard inventory and make them unavailable for actual shoppers. This misleads legitimate users and the businesses lose revenue. Bots also lure advertisers to websites to generate fake clicks for ads. The bots then create accounts and use them to resell products for profit. This is how bots hoard inventory and bring down a business.
2. Application spoofing
Bots mimic the visual appearance of a high-performing app by deploying malicious apps. This fake app is used to generate countless clicks and send false engagement data to businesses.
3. Account Takeover (ATO)
Bots can take over accounts and initiate undesirable actions. Account Takeover can expose the data of millions of users and put the company’s reputation at stake. The legal consequences of data leaks are also significant.
Below are the three main channels that are targeted by bots:
– Attacks through APIs
Bots have now resorted to application programming interfaces or APIs to launch attacks. This is one of the commonly used methods by bots. APIs are the crucial links facilitating communication between mobile devices. Bots mimic real users and exploit APIs to hack and manipulate systems that compromise data integrity.
– Mobile apps
Mobile apps are, with no doubt, one of the prime targets for attackers. This is because mobile apps store sensitive and confidential information which can be misused for personal gains. Mobile apps often have compromised security, giving away easy access to hackers.
– Bot attacks on websites
Fake login attempts and scraping are examples of bot attacks on websites that reduce revenue and put customer data at risk.
Bots can launch attacks in several ways. It could be in the form of fake reviews, comments, login attempts, brute force attacks, or marketing fraud. The key to tackling these attacks is using sophisticated bot detection techniques.
Types of Bot Attacks
Bot attacks can be classified based on the scale of the attacks. Below are a few common types of attacks:
Basic bots scale up to launch high-volume attacks. The high volume of these attacks translates into significant financial losses for the business. These kinds of attacks can get users to click just a few malicious links and create the desired impact.
Low and slow attacks:
This is a type of attack where bots lie low and lay the groundwork to launch attacks later. Bots effectively escape security measures deployed to detect and prevent them and gradually launch attacks on customer touchpoints such as fake reviews, up-down voting videos, etc.
Bots that can effortlessly evade detection techniques deployed by the business are called sophisticated bots. These bots are capable of impersonating users with a high accuracy rate and can dodge bot management and mitigation solutions.
As the name suggests, hybrid attacks are a combination of bots and human-farm attacks. These are a group of workers that help perpetuate attacks on behalf of cybercriminals. These workers take over when bots are unable to tackle bot-prevention mechanisms.
Bot Detection Techniques
Bot detection techniques equip businesses with appropriate security tools to prevent data breaches. Let’s look at some of the commonly used bot detection techniques.
Captcha challenge is the most common way of preventing bad bots. But it is not as effective as one might think. Relying solely on Captcha challenges to detect bad bots is not the right way to go about it.
Invisible challenge bot detection technique is a mechanism used by some bot management and fraud detection systems to detect and prevent automated attacks. This technique ensures that traffic is coming from real users. This detection technique relies on a cryptographic proof-of-work which makes it difficult for bots to launch automated attacks. It involves presenting challenges that are invisible to the user, making it difficult for bots to bypass the detection mechanism.
Enterprises can also rely on manual bot-blocking methods for bot detection. This method is however slow and best employed as an interim measure.
If bot attacks are inevitable, feeding fake data to bots can prevent actual information from getting into the wrong hands. Using fake data is one of the bot detection techniques that some bot management and fraud detection systems use to detect and prevent automated attacks. When a bot requests data from a website or application, the system provides the bot with fake data. This technique can be used in conjunction with other bot detection techniques, such as invisible challenges, to create a more robust bot management solution. However, this is not a foolproof solution against bot attacks. Real data may be eventually exposed by more sophisticated bots.
Below are some of the features commonly used in most bot detection methods:
This feature enables enterprises to detect suspicious activities with the help of a device fingerprinting mechanism. This mechanism analyzes hardware and software that connect to the site. It identifies patterns in the device and browser characteristics of users that visit a website or application, to determine whether or not they are human.
This analysis involves checking for known malicious IP addresses or patterns of activity associated with bots. However, it is important to keep in mind that the use of IP addresses alone is not always enough to determine whether or not a user is a bot, as legitimate users can also exhibit similar IP address characteristics. Therefore, IP analysis is commonly used in conjunction with other bot detection techniques, such as behavioral analysis, device fingerprinting, and captcha tests, to create a more comprehensive bot management solution.
When a bot is detected, the system can generate a real-time alert, notifying security teams of the potential threat. Real-time alerts create fraud alerts and pinpoint unusual traffic spikes.
Velocity risk rules:
These rules give insights into human behavior and patterns which help understand whether traffic is generated by bots or humans. It analyzes the velocity or frequency of a user’s actions online. By measuring the frequency of specific actions, such as the number of login attempts in a short time, velocity risk rules can help identify potential bots or automated attacks. These rules are commonly used in bot detection solutions and can be configured to trigger alerts or block suspicious traffic when a certain velocity threshold is exceeded.
Several bot detection software and tools are available for preventing bot attacks. Bot detection tools can monitor networks, websites, and applications, and classify and block bot attacks. It also classifies bots appropriately and differentiates malicious bots from good bots. After monitoring and classifying the bots, the tools must take the correct action to prevent malicious bots and allow access to legitimate bots and human users.
What are the Challenges to Bot Detection?
The emergence of sophisticated bot attacks has made successful bot detection difficult for businesses. Let’s look at the different challenges facing businesses when implementing bot detection measures.
Bots attack all endpoints:
Bots target all endpoints which means protecting just the websites no longer serves the purpose. Websites, mobile apps, and APIs are the endpoints that need to be safeguarded against bot attacks with stringent security measures. If any of these endpoints are left unprotected, bot attacks can compromise the security of the entire application or website.
They replicate human users:
It is becoming increasingly difficult to differentiate bots from legitimate users. Bots rely on browsers with characteristics similar to human browsers. The fingerprints are similar to those of human browsers and a lot of other characteristics too are hard to differentiate. This resemblance with human behavior poses a major challenge to bot detection for businesses.
Attacks spanning geographies:
Bots can attack apps across countries for days with less time and effort. Such high-scale, high-volume attacks have made it challenging for businesses to detect and stop bots across applications and software.
Bots now send only one or two requests from a single IP and switch to several other IPs to continue sending requests. This makes bot detection through IP analysis extremely difficult. WAF especially relies on IPs to differentiate bots from real users.
The emergence of Bots-As-A-service (BAAS):
Bot attacks are now relatively simpler to launch. Anyone can launch a bot attack these days by setting up bots on a website or application. BAAS is incentivized so more applications and websites are targeted through it.
Several factors contribute to making bot detection challenging for businesses. Businesses require advanced solutions to prevent bots from becoming a serious menace.
Businesses must leverage advanced methodologies to counter bot attacks. Traditional approaches to bot detection have become ineffective as bots are becoming more sophisticated than before. The constantly evolving nature of botnets has compelled businesses to adapt detection methods to keep up with new tactics used by cybercriminals. Malicious bots are used to steal customer data, perform account takeover fraud, distribute malware, and engage in a range of other fraudulent activities. These activities damage a business’s reputation, cause financial losses, and lead to regulatory fines. Thus, businesses need to ensure that they have effective bot detection solutions in place to protect against such malicious activities.
Bot detection prevents bots from taking unfair advantage of business practices, such as ticket purchasing and stock market trading. Bot detection and mitigation should be an important component of the security arsenal to prevent fraudulent activities, safeguard user privacy, ensure fair business practices, prevent data breaches, and maintain business continuity. Bot detection software and tools defend against attacks or other unauthorized activity involving bots. It is crucial for businesses to carefully evaluate and choose the right bot detection tool, based on their particular requirements and budget.
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