How to Detect and Block Them? Malicious Bot Defense Strategies Enterprises Must Prepare

This article is a continuation of our previous post:
In the Era of "5-Second Purchases," How Do Malicious Bots Destroy Fairness? … [Click]

Why is Existing Security Not Enough?

In our previous post, we established that implementing strict regulations doesn't automatically solve the malicious bot problem. It is crucial for enterprises to possess the technical capabilities to detect and block automated attacks on their own.

Simply blocking attack traffic is not enough to stop malicious bots. Because they mimic the behavior of normal users to access systems in highly realistic ways, businesses must be able to accurately distinguish between human users and bots, and respond differently based on those detection results.

If a system indiscriminately rejects requests by limiting the number of actions processed per unit of time—like traditional Rate Limiting—it restricts access not only for malicious bots but also for legitimate users.

Many rely on CAPTCHAs to tell humans and bots apart. However, with recent technological advancements, CAPTCHA itself has become just another target that bots can easily bypass. The traditional Web Application Firewall (WAF) approach of blocking specific IPs is also losing its effectiveness, as attackers now rotate through regular residential IPs to gain access.

Now, enterprises must go beyond asking, "How many requests are coming in?" and start asking, "Is the entity sending this request an actual user?"

How Can We Stop Malicious Bots?

So, how can you effectively distinguish between a human and a bot in a live service? The most important rule is never to rely on a single piece of information for your judgment.

Just because requests share the same IP address doesn't mean they come from the same user. Conversely, having different IP addresses doesn't guarantee they are all legitimate users. If you judge bots solely based on IP addresses, User-Agents, or simple request frequencies, attackers can easily bypass your detection by changing their IPs or disguising themselves as normal browsers.

Therefore, an effective bot defense requires a comprehensive evaluation of various signals—including user behavior patterns, connection environments, and browser characteristics.

1. User Behavior Analysis

One of the biggest differences between a human and a bot is how they behave. Humans exhibit irregular behavior patterns when using a website. The time they spend reading a page, the way they move their mouse, and the intervals between clicks and page navigation vary every single time.

In contrast, automated bots often act repeatedly according to predefined scripts. They can generate tens or hundreds of requests in a very short time using identical patterns. Therefore, a bot defense system must not only recognize that there are "too many requests," but also analyze "how the service is being interacted with."

2. Comprehensive Risk Assessment

It is vital to assess the risk level of connection requests based on various detection signals. If several of the following signals appear simultaneously, the risk of the attack is significantly higher:

• Abnormal request frequency

• Repetitive behavior patterns

• Anomalies in the browser environment

• Potential use of automation tools

• Abnormal session patterns

• Suspicious IP and network characteristics

By combining these indicators to assess risk, you avoid a binary "human vs. bot" classification and can apply varying levels of responses accordingly.

3. A Phased Approach

Another critical aspect of bot defense is how you respond after detection. Blocking all bots uniformly is not always the right approach. There are "good" bots that help run your services—like search engine crawlers—and "bad" bots designed for malicious scraping or ticket scalping.

Enterprises need to apply different policies based on detection results. Normal users should be allowed to use the service as usual, suspicious access should require secondary verification, and requests confirmed as malicious bots should be blocked entirely. The core of bot defense isn't just detection itself, but choosing the appropriate response based on the detection results.

4. A Continuous Defense System Against Evolving Bot Macros

Here lies another challenge: as businesses advance their bot defense technologies, attackers find new workarounds.

Bots are evolving from simple scripts to using actual headless browsers, from fixed IPs to utilizing proxies and distributed networks, and from simple repetitive tasks to mimicking human-like behavior patterns. With the recent integration of AI technology, automated attacks are poised to become even more sophisticated.

Therefore, simply maintaining a bot defense system once it is built is not enough. You need a system capable of continuously analyzing new attack patterns, refining detection standards, and adapting to ever-changing threats.

AI-Based Multi-Layered Bot Defense Solution: BotManager

STCLab's BotManager is an AI-based malicious bot detection and management solution. It separates abnormal access requests from legitimate user traffic to prevent the distortion of business metrics and ensures fair service access for everyone without technical inequality.

• Multi-Layered Agent Defense (Client / Server / CDN)
  A multi-tier agent architecture inspects all potential bypass routes, maximizing bot detection rates.

• 4-Step Verification System (Firewall - Bot Category - Filter - Policy)
  Malicious bots are detected through a multiple-verification system. In particular, policy analysis allows for the identification of bots under various conditions regarding access frequency and patterns through real-time behavioral analysis.

• Evidence-Based Phased Response Measures
  Abnormal access suspected of being a bot can be blocked immediately, or a CAPTCHA screen can be provided for secondary verification. Every block record is matched with a block reason code, providing clear data-backed evidence to respond to any inquiries or claims.

• AI Scoring and Policy Recommendation
  AI monitors real-time logs to assess risk levels and analyzes traffic from the past 7 days to suggest optimal blocking thresholds.

• Expert Managed Services
  STCLab's security experts conduct in-depth traffic analysis and support the continuous advancement of response policies to match evolving threats.

Additional robust policy configurations can also be set up to strengthen security capabilities, including dynamic URLs, obfuscation settings, ransomware prevention, account takeover (ATO) defense, and anti-scraping measures.

BotManager has already been deployed by major ticketing platforms in South Korea, as well as by numerous educational and public institutions, supporting fair and efficient service operations.

Furthermore, a leading intellectual property (IP) company in Japan has applied BotManager to its e-commerce store to block abnormal access, while also receiving ongoing management and optimization through our managed service contract.

Malicious Bot Defense: No Longer an Option, But a Standard

Malicious bots have become a direct threat to enterprise business logic and legitimate user opportunities. They hoard inventory, hijack reservations, take over accounts, scrape and distort data, and abuse promotions.

While malicious bots must be stopped, legitimate users should experience zero disruption. What businesses need is not just a thicker wall, but a flexible bot defense system capable of accurately distinguishing between humans and bots, and adjusting response levels according to the risk.

In an era where bots and humans compete, STCLab helps enterprises build an environment where legitimate users can experience fair and stable services through BotManager.

If you need a 'Bot Defense Strategy' that goes beyond simple traffic blocking and aligns with your business goals, check out BotManager today.

*Note
「手入力なら数分かかる」チケットのネット購入わずか５秒、１分で完売の闇…「悪性ｂｏｔ」の実態とは
https://news.yahoo.co.jp/articles/de83488c63c0a7a5c9f561efae8b34c67ac5fb31?page=1