The Relationship Between AI And Cybersecurity

The Relationship Between AI And Cybersecurity

As our use of computers and the internet increases, cyber threats also increase. While most cyber-attacks are human instigated and often ride on factors such as system vulnerabilities and human errors, as artificial intelligence (AI), machine learning, and the Internet of Things (IoT) advance at a fast pace, some people are worried about a Skynet—a Terminator reference—type of future. Others, however, are more optimistic about the implications of artificial intelligence on the effectiveness of cybersecurity.

Understanding how artificial intelligence is changing cybersecurity will help you determine the changes to implement in your cybersecurity program as technology develops and improves.

How We Are Presently Applying AI And Machine Learning To Cybersecurity

When simplified to its simplest forms, artificial intelligence refers to a computer’s ability to complete complex tasks that demand some form of intelligence. On the other hand, machine learning is the process through which these machines/computers learn new information and how to apply it to solve problems.

Machine learning is at the heart of present cybersecurity in that, because it helps machines learn and implement what they learn, many companies and businesses are using AI to recognize data breach patterns, how users use systems (thus ensuring that the machines can recognize hack patterns fast) as well as learn from previous hack patterns.

Thanks to the development of computing power, enterprise businesses such as Google, Amazon, and Facebook are using machine learning and artificial intelligence to gather invaluable behavioral data that is helping shape cybersecurity in many ways. For instance, by collecting this behavioral data, Facebook, Amazon, and Google can offer their users tips and strategies on how to keep their data safe.

Moreover, as these organizations analyze the consumer data they get from teaching machines how to recognize patterns, they can easily build big data frameworks as well as open-source applications that are helping other businesses recognize the same patterns and therefore, the benefits of big data collection whittle down.

Moreover, since intelligent machines are quick to recognize patterns that they have learned, and some are so complex that they are teaching themselves from experience and the patterns they have inferred, AI is developing so fast that machines are not able to protect systems from enhanced cyber threats. As AI develops further, becomes sophisticated, and as machines learn more, they will only bolster cybersecurity.

An example of the relationship between AI and cybersecurity (in reference to enhancing cybersecurity) is how machines that have learned how to detect deception technology are automatically defending systems from cyber-attacks. A great example of this is Google. When you visit some “unsafe” websites, after gathering information from users, Google is likely to inform you of the deceptive nature of the site.   

Presently, cybersecurity experts are creating and implementing machine models that are gathering past cybersecurity data, learning from it to protect the system, and that are consistently gathering information about new cyber threats that they need to protect the system from.

The other aspect of this is that by using machines (artificial intelligence) to gather and make sense of big data, the machines can generate patterns that enterprise businesses can then use to build strong cybersecurity infrastructure and security products. The risky-prone patterns recognized by these machines then act like a sort of early warning system that cybersecurity experts can use to secure the system. 

Machine Learning And AI: How AI Is Helping Us Fight Spam

Machine learning, the most important subset of artificial intelligence, is proving to be a very invaluable and effective tool against spam and phishing attacks. A great example of this is Google. Google’s Gmail uses machine learning to determine which emails to filter based on the messages users have flagged as spam and phishing attacks. Google has been using this system for more than 18 years and over that time, the system has learned a lot and become intuitive even as spam and phishing attacks on email change and develop.

Today, Google uses artificial intelligence in almost all areas of its business. It especially uses something called deep learning, a system that allows machines to recognize patterns in big data and adjust themselves intermittently as they gather new reams of data.

With deep learning, cybersecurity analysts do not have to worry about the magnitude of their data. All they have to do is program the machine to recognize certain parameters and then from there, use these parameters to learn more. This has massive implications for cybersecurity. For instance, deep learning is allowing machines to detect malware, phishing attacks, and even duplicitous payments. Google especially is using their technology to protect their Play Store and cloud.

Machine Learning And Security Training

One of the most significant relationships between AI and cybersecurity is that today, it is very easy to teach an adaptively designed and programmed server to recognize normal requests from unusual requests, what we call baseline.

This relationship is proving very central to cybersecurity because by teaching machines to detect this baseline and then flag whatever else does not meet the baseline parameters, it is becoming easier for cybersecurity analysts to determine which threats to guard against.

That stated, most researchers and analysts are also quick to point out that inasmuch as it is easy to teach computers how to recognize threat patterns, if we stop teaching them, the machine, no matter how adaptive it is, will eventually become redundant as new, sophisticated threats emerge. It, therefore, stands to reason that even today, because artificial intelligence is yet to come of age, AI plays an additive role in the toolbox of any professional cybersecurity analyst.

An example of this is currently being applied on tools such as Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). These are tools that cybersecurity professionals working on the “Blue Team” side of the fence (these are the professionals that protect the systems in a company) uses every day to detect any anomalies that could turn into a potential threat for the company.

How Hackers Are Using AI And Machine Learning

Just as cybersecurity experts are using machine learning to guard against many cyber threats and generally create effective defense mechanisms against some of the most advanced cyber threats, because hackers are talented and innovative, they are also using machine learning in their attacks. For instance, some hacking pools use machine vision to defeat defense (coincidentally, machine learning defenses) such as Captchas.

Hackers are also going as far as poisoning big data. For instance, using traditional hacking methods (human negligence and error are the most exploited), hackers are looking for ways to learn the setup of a security algorithm and where the machine gathers information from.

Once they know that, they are then “poisoning” the data by introducing defective or misleading data with the intent being to decapitate the machine to a point where for instance in the case of DoS attacks, it cannot differentiate legitimate requests from illegitimate one.

AI As a Cybersecurity Tool

Now that we have established the various ways cybersecurity and artificial intelligence relate, we cannot fail to mention that as AI develops and becomes more intuitive, it is becoming a very effective tool in the toolbox of a cybersecurity expert.

As threats increase, cybersecurity experts can teach machines how to recognize threat patterns and adaptively guard against them. As a tool, cybersecurity analysts can teach machines which baseline parameters to look out for as they read big data and once alerted to data that does not meet the baseline, they can teach the machine how to handle such data/threat. The effect of this is that as machines learn how to analyze information and solve the non-baseline data, it learns and adapts.

By teaching machines the threats, weaknesses, and exploits to guard against, cybersecurity analysts and experts are now gaining the ability to deploy solutions fast, which is helping to mitigate future attacks.

Because of their ability to sift through large amounts of data, machines are easing the pressure of the backs of cybersecurity experts to some degree because when a machine knows the threats to look for, the work of a cybersecurity analyst becomes mostly passive. In fact, by employing machine learning and big data, all a cybersecurity expert has to do is teach the machine which other intrusions to note and to respond to the machine when it alerts of something outside its normal parameters.

Bottom line

Yes, there is a real and definitive relationship between cybersecurity and machine learning (AI). As you have seen, thousands of enterprise organizations and even medium-sized businesses are using AI and machine learning to teach machines how to read patterns within big data and then guard against any potential threats.

As we go forth into the future, the relationship between AI and cybersecurity is only going to grow as big data becomes an integral part of our lives and as countries around the world work towards standardization of data laws, formats, and communication modes. Once this happens and we achieve semantic uniformity of data flow and formats, AI will become the most effective guard against system intrusions and cyber hacks.

The cybersecurity community must work towards creating a balance between adaptive machine learning and supervised learning. This will ensure that even as machines dig through reams of big data in moments to read patterns and draw analysis, cybersecurity experts can also teach these machines which threat patterns to look out for and how to handle them once they notice them in their data nodes.

by Edgar Vera, MS Cybersecurity



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