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How Do AI-Based Cyber Tools Prevent and Mitigate Botnet Attacks?

Over the past week, Blizzard reported that their systems were repeatedly targeted with Distributed Denial of Service (DDoS attacks). Their servers went down and became available only for users in certain locations.

Players are getting frustrated because they can’t access many of the games they normally would. For many, the gaming experience has been negatively affected due to exasperating lag. Some users reported that their email addresses even got hacked in the midst of a DDoS attack.

On April 20, the company shared that its systems are still being targeted with DDoS threats on a daily basis.

Also known as botnet attacks, DDoS are common threats to businesses that rely on versatile applications and networks — meaning most companies operating today.

The longer a botnet attack compromises the company’s infrastructure, the more financial and reputational damage the business suffers in the long run.

What is a botnet attack exactly, and what is the role of artificial intelligence in avoiding, detecting, and ceasing this malicious cyber threat?

What Is a Botnet Attack?

Botnets are groups of devices that connect to the internet. Whether we talk about mobile, desktop or IoT devices, threat actors who control botnets (AKA botmasters) hijack them to initiate botnet attacks.

The users whose devices are being exploited by a botnet group are often not aware their computers and mobile phones are part of the process. Botmasters can use the same device to attack multiple networks at the same time.

Generally, botnets are deployed to spam a specific website or to crash entire servers. Hackers’ intentions can be to harm the company’s reputation, finances, or both.

How does a botnet attack happen, exactly?

A robotic army controlled by the online criminal is used to send a large volume of traffic to the victim’s network or application.

As a result, the company can lose access to its network, or their application might crash — depending on the capacity of a botnet and how much traffic is used to flood the target. 

The volume of DDoS attacks on the application level is measured in RPS (requests per second). On the network level, the attack is more severe and is measured in PPS (packets per second).

The attacks can last a few minutes, days, or even months. Depending on the hacker’s intention and the power of the botnet, the network or application can completely crash or slow down to the point where users get frustrated and leave the service.

AI-Powered Botnet Attack Protection

How to protect the network or an application from malicious botnet attacks? Due to the large volume and an increasing number of threats, cybersecurity teams delegate repetitive security tasks to artificial intelligence.

Some of the tasks that can be automated with the use of AI in cybersecurity include:

  • Detection of signs of a cyberattack
  • Analysis of data generated from the security tools
  • Blocking of traffic that is deemed malicious
  • Generating reports that depict the state of security and provide actionable tips on how security teams can mitigate the issues at hand
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With AI, analysis of traffic and mitigation are possible in real time. The processes are repeated at all times, and security analysts have an insight into the state of security 24/7.

AI-Based DDoS Protection

To fight botnet attacks, cybersecurity teams rely on cloud-based DDoS attack prevention tools — they are designed to detect and block unwanted traffic.

How does DDoS protection work in practice?

It identifies a large number of versatile DDoS attacks — which is important since hackers are developing new and more complex methods every day.

For instance, that could mean the detection of attacks that occur on the application, Domain Name System (DNS) or network levels.

The traffic is inspected before reaching the network of a user. It’s compared with the ever-growing database that lists versatile hacking techniques and malicious IP addresses. Within the network, packets are triple-checked to ensure that the traffic is legitimate.

When the botmaster targets an application, the automated DDoS solution automatically identifies the signature of the botnet to differentiate it from genuine human activity.

Only the traffic that is deemed “clean”, genuine and safe will reach the system of a company. The rest are blocked.

Layered AI Cybersecurity Architecture

In many cases, a botnet attack is just the start. Threat actors tend to team them up with other hacking techniques. It goes without saying that companies today need a layered and comprehensive security system to protect themselves from such versatile and depleting attacks.

In the case of Blizzard, players shared that their email addresses got compromised during the DDoS attack that occurred.

DDoS attacks are also often paired with ransomware. Once the file-encrypting malware is deployed on the network and the ransom is requested, criminals can initiate DDoS attacks to add more pressure on their victims.

Therefore, having other automated security solutions that can detect and mitigate threats in time is essential. Most businesses have layers of 40–90 cybersecurity solutions to protect their most valuable assets. 

Final Word

Botnet attacks are difficult to eradicate completely. These “zombie armies” tend to come back every year — on a larger scale and more advanced than the year before.

As mentioned, even major enterprises such as Blizzard aren’t immune to DDoS attacks — let alone companies that don’t have the same resources but rely on applications and networks in their day-to-day.

To prevent and stop threats such as botnet attacks today, artificial intelligence has a key role in cybersecurity. AI can keep up with the incoming data and continually scan the traffic to detect malicious activity, such as a vast amount of traffic fast.


As companies are up against more cyber attacks than ever before, and threats are getting more and more sophisticated as well as hitting the servers with more volume, organizations have to prepare beforehand — with streamlined technology that can detect issues in real-time.

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