How To Perform Effective Botnet Detection
Botnets and various forms of botnet attacks continue to be a serious cybersecurity concern for many businesses and individuals. In the second quarter of 2020 alone, there have been more than 3,500 new botnet C&C (Command and Control) servers, and we can expect that botnets will continue to be a threat.
On the other hand, botnet controllers/operators are becoming more sophisticated and resilient in launching their attacks and controlling the bot devices, making bot detection and management a much more challenging process.
In this guide, we will discuss all you need to know about bot detection and management: the challenges, different methods, and how to implement botnet detection to your system and network.
Let us begin, however, by discussing the concept of botnet itself.
What Is a Botnet?
It’s very important to understand that “botnets” are not the same as “bots” or “internet bots”.
An internet bot is a software solution that is designed to perform automated tasks over the internet. Google’s crawler bot, for example, is programmed to automatically crawl and index this website and so many others.
A botnet, on the other hand, is a group of compromised computers and devices that are now under the control of a cybercriminal. The cybercriminal that owns the bot (the botnet operator) will dedicate a C&C (Command and Control) server to control the botnet devices, typically via Internet Relay Chat commands.
In practice, the cybercriminal (botnet owner) will use the C&C server to command the botnets to launch various cyberattacks, most commonly DDoS attacks (Distributed Denial of Service), but also other types of attack vectors like brute force (credential cracking), data breach, identity theft, and others. These cyber attacks performed by botnets are called botnet attacks.
How a Device Is Converted Into a Botnet?
The botnet operator can use various methods to attack a device/machine and convert it into a zombie device as a part of a botnet, but we can generally divide the methods into two major categories:
Passive botnet infection methods would require some form of intervention from human users. For example, in a phishing attack, the target user is given a malicious link or an attachment, and the victim must click on this link before the device can be infected with malware that will effectively put the device under the control of the C&C server.
Active botnet infection methods work without needing the ‘help’ of human users. When, for example, a device has been infected with botnet malware, the device will automatically find potential victims while surfing the web. When, for instance, the malware finds vulnerabilities in a website that can be exploited, it will automatically infect this website.
Challenges of Botnet Detection
In theory, to stop these botnet attacks, the most effective approach is to find and stop these C&C servers, but doing so can be easier said than done. Many of these botnet operators use multiple servers and/or utilize various technologies to mask their identity and commands as a harmless activity.
For instance, it’s common for the C&C servers to mask the malicious botnet commands as social media traffic and normal traffic between peer-to-peer services, and other seemingly harmless activities. Also, these botnet commands are often very subtle, making detection even more difficult.
Ideally, to effectively detect the C&C server, the botnet detection solution should be able to access the communication between the C&C server and its botnet devices. However, this is obviously impossible unless it is a security solution (i.e. antivirus) that is dedicated to protecting the C&C server.
Thus, a more practical approach to botnet detection is to monitor, detect, and analyze the botnet attacks themselves. The traditional method is to use signature-based detection to analyze the requests to the website or application, and determine which requests originated from botnets.
The botnet detection solution would look for known signatures, like identified IP addresses, similar attack patterns to previous attacks, and so on. However, due to the sophistication of today’s bot operators, signature-based bot detection techniques tend to produce high false positives.
- Some payloads are widely used, even by legitimate users
- Even a randomly-generated pattern can be detected as a false positive
- Today’s botnet operators can use various technologies, including AI technologies to impersonate the behaviors of legitimate human users like randomized clicks and non-linear mouse movements
- IP-based detection is no longer effective since attackers can often change between hundreds of different IP addresses by using a residential proxy or other technologies
- Penetration test performed by legitimate users (i.e. using vulnerability scanners) can often behave similarly enough to botnets
As we can see, false positives are an important challenge in botnet detection that is also very difficult to tackle. This is why the right technology, technique, and diligence are crucial in discerning the harmless legitimate traffic from the botnet-driven traffic and botnet attacks.
An AI-based bot and botnet detection solution, for example, will use AI technologies to analyze every request to your website, application (including mobile app), and API while using machine learning techniques to determine in real-time whether the traffic is a legitimate user or a botnet.
Datadome works on autopilot and requires no intervention and management on your end. Just configure DataDome according to your preference, and it will detect the presence of both botnets and malicious bots, and manage this traffic accordingly.
Botnet attacks are now a serious issue, your devices are always at risk of being infected by botnet malware, and at the same time, your website and network may also be targeted by various botnet attacks. Thus, botnet detection is now a serious concern that is also challenging to implement.
However, by using the right technology like DataDome, as well as the right technique and diligence, we can effectively detect botnet attacks and mitigate their effects to prevent further damages.