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DDOS attacks are a growing threat to website operators and online publishers around the world. In 2016, there was a 125% increase in DDOS attacks from the previous year. That trend has intensified over the past couple of years. In 2019, we can expect to see more of these attacks than ever before.
Advances in artificial intelligence have played an important role in the shifting landscape of cybersecurity. Hackers are using more sophisticated AI algorithms to make their attacks more effective. However, AI cybersecurity solutions are also the best line of defense against DDOS attacks.
It is important to realize that a growing number of DDOS attacks are launched against application layers. These types of attacks can be most difficult to prevent, so it is important for cybersecurity experts to do their due diligence.
How will AI thwart application layer DDOS attacks?
These types of cyber attacks are becoming increasingly complex. DDOS attacks are being coordinated through ingenious neural networks that we have never seen before. At first glance, these types of attacks seem rudimentary. However, they use intricate schemes to take control of networks that allow them to create botnets, which are used to attack their targets.
Rod Soto, the director of security research for JASK, stated that AI has made launching cyber attacks simpler than ever. However, AI can also help security professionals fortify their defenses against hackers launching DDOS attacks. New cybersecurity solutions will depend heavily on machine learning.
“It’s a process that is best when working with data scientists, statistics and with as much data as possible,” Soto said. “You train it, it learns and extrapolates things that aren’t clear and can eventually make judgments on activity that you didn’t even train it to do.”
Machine learning will be particularly important as new types of DDOS attacks are launched. Hackers are always developing new types of attacks. They discover vulnerabilities in cyber security applications and find ways to exploit them. Machine learning helps these cybersecurity experts respond to new threats.
However, there is still a downside to using machine learning. Hackers can figure out how the machine learning algorithms work. Then they will be able to bypass them. If cyber security professionals became too reliant on machine learning based applications, they could be entirely defenseless against savvy hackers. They would at least reduce the threats against less proficient criminals, but the most sophisticated hackers would be able to freely coordinate DDOS attacks against them.
This means that cybersecurity professionals need to implement both machine learning and traditional defenses. They can have experts on staff frequently working towards new defenses against the most recent types of DDOS attacks. Meanwhile, machine learning can help provide more immediate defenses before they have a chance to conduct their own audits.
There are other ways to use AI to stop these attacks. One option is to make sure that you use neural networks to improve threat detection. An IEEE conference held in Taiwan discussed this option in 2010. It was a novel concept at the time but has since become a much more popular approach. Apache Spark is one of the neural network applications that has proven to be very effective.
A growing number of experts are developing new solutions with neural networks to improve threat detection. British AI scientists Dr. Alan Saied, Dr. Richard E Overill, and Prof. Tomasz Radzik collaborated to develop one of the most sophisticated neural networks to stop DDOS attacks. The summary of their research can be found on Science Direct. User case studies have found that it was very effective.