IT solutions using artificial intelligence can perform functions that programmers can handle with regular programming only with extreme performance.
With the advent of the modern IT era, cyber threats have become a natural part of our lives. How to catch them in time and protect yourself from them?
It is a fight without a clear winner. Early and correct detection of a cyber threat is a very complex process influenced by several factors. For example, whether a vulnerability has been identified on any component of the infrastructure, how large the attack vector is, how easy it is possible to exploit the vulnerability, whether the security products used in the infrastructure contain a mechanism for detecting it, whether the processes within the organization are set up to adequately and timely respond to the given situation and the like.
And all this is just one possibility out of many. In recent times, the occurrence of blackmail software, so-called ransomware, is widespread, where the attacker, for example, performs an action “on behalf” of the user that makes his data unavailable – for example, by encryption. For a fee, he then offers to restore his data. The detection mechanisms, in this case, are entirely different from the first case. They focus on detecting anomalies in the user’s behavior.
Which solutions are currently the most reliable against cyber attacks? Especially for companies, institutions, or organizations that cannot afford to have their security breached.
Attackers are aware of detection techniques and try to avoid them, so successful protection primarily consists of methods that use timely information. For example, a security device manufacturer monitors what’s happening on the Internet or the dark web, and if it detects new malware, it prepares a detection and protection mechanism.
Next-generation anomaly detection technologies are products that do not have predefined rules for detecting an attack but continuously analyze various parameters of network traffic or user activity and look for anomalies that may indicate a cyber attack. This makes them very successful in detecting cyber threats.
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Artificial Intelligence Is Also Pushed To The Fore In Cyber Security. How Can It Help Detect Cyber Threats?
For example, anomaly detection is often implemented using machine learning techniques (machine learning – ML, a subset of artificial intelligence). Even though no artificial intelligence has reached the quality of human intelligence, it surpasses us, humans in selected areas.
For example, from text analysis, after meeting several conditions (availability of a sufficient volume of another author’s text), it is possible to identify the author by a simple frequency analysis of the occurrence of words and their combinations. Such an analysis would take a very long time for an ordinary person, while machine learning can decide in a few seconds.
When detecting anomalies in user behavior, many parameters are evaluated, for example, how often the user logs into individual systems, from where and what software components he uses, etc. Even if an attacker were to gain complete control over a user’s account, he would have to mimic the behavior of a particular user very precisely to avoid the detection mechanism using machine learning methods.
How Does It Look In Practice? How Do Such Solutions Work?
More and more security manufacturers are integrating machine learning methods. The manufacturer often invests a lot of money in development so that the mechanisms can be more precisely described. Such an exciting example is remote user login. With the help of machine learning, one or several models are created with information that is typical for a specific user (for example, from which IP address he connects, at what time, how often, with which device, how long since the last login or how long the connection lasts) and in if a change from the “learned” model is detected, an alarm is generated or the link is rejected.
And all this without entering information about the user into the detection system. Here we assume that users have their routine and behave more or less the same. Of course, it happens that they log in outside of their performance. Here it is possible to compare the behavior with the rest of the population, thus eliminating the occurrence of a false alarm.
Another example is phishing. To a certain extent, it is possible to recognize whether the text is characteristically phishing by analyzing the text of the email, for example, by analyzing words and the relationships between them or the frequency of their use. As part of this analysis, we work with the assumption that human-written text in business correspondence has specific characteristics that are natural to human intelligence. Still, their identification by humans is more complex and often impossible, and machine learning is beneficial. The critical phase is the so-called feature engineering, i.e., based on which features the model will be created. Here, on the contrary, human representation is irreplaceable.
Are There Any Other Examples?
An example is the protection of web portals accessible from the Internet. With the help of machine learning, it is possible to recognize the movements of the user’s mouse, whether a live person or a program is moving it. A human-mouse model is often created in advance and trained on a suitable sample of people to be sufficiently generic.
Here, too, machine learning works with the assumption that human mouse movements are specific, limited by the biological properties of the human hand and its reactions. Therefore, if an attacker creates a program that automatically accesses a web portal, it is possible to recognize that it is a programmed activity, even if he uses the correct credentials. In this way, it is possible to identify various activities leading to cyber attacks.
Who are they suitable for, and who should reach for them?
It is not limited by anything. The area in which these techniques are used must be suitable for this. For example, we’re defining a threshold value as sufficient, it does not make sense to apply machine learning.
How Can Such Artificial Intelligence Help In Business?
Humans are not the only limiting factor in cyber security. The attention of a person who analyzes the security situation is undoubtedly less at night or the end of working hours, so detection mechanisms will gradually replace people. On the contrary, in feature engineering, people are irreplaceable. This cooperation leads to high efficiency and a lower error rate, thereby improving business conditions.
Are AI-Based Solutions The New Trend In Cyber Security?
Artificial intelligence has been around for a long time, but the last few years have seen a massive increase in usage, so it is a new trend.
Will Artificial Intelligence Be Used More And More In This Direction?
All indications are that it will be used more and more. Platforms for developing artificial intelligence models and the necessary computing power are common and affordable. Increasingly, artificial intelligence is used for a particular scenario even without theoretical knowledge of mathematics, and we are then talking about black box implementations.
The broader use of artificial intelligence is also helped by transfer learning, where the created model replicates another published model with minimal changes. Last but not least, artificial intelligence, or machine learning, can be used to a certain extent even without an expert in the given field (unassisted learning). Still, the results must be validated by an expert in the provided field.
But the most important reason is that solutions using artificial intelligence can perform impossible or extremely difficult functions to implement with regular programming. All this leads me to believe that artificial intelligence will be used more often in almost all areas of IT security.
How much does such protection cost? Is it financially manageable for the company, institution, or organization? Is such an investment worth it?
Although manufacturers have different prices for products with artificial intelligence or have created unique products, over time, the functionality of artificial intelligence will be a regular part of the products without the product price increases.
Also Read: What Types of Problems Can We Solve With Machine Learning Techniques?
Cyber Threats Are Constantly Evolving, And So Are Cyber Security Solutions. What Do You Forecast For This Area?
This is a tricky question. In principle, it is similar to antibiotics, where antibiotic-resistant bacteria are created. Also, in the field of cyber security, cyber intrusion techniques are emerging that are resistant to current detection mechanisms. Therefore, development and research must continue; even the best technology will be surpassed so that it will go round and round.
Ethical discussions often arise along with artificial intelligence, which is probably irrelevant when fighting cyber threats.
It just isn’t. I often get questions about whether collected information can be misused or whether artificial intelligence can endanger a person. The human brain, even if it is less efficient than artificial intelligence in specific tasks, complexity of intelligence is so extensive that it is possible that no artificial intelligence can achieve such complexity.
Information can be misused; therefore, like any quality software component, artificial intelligence software must also protect against such misuse. But that is another area of cyber protection. However, I see ethical problems mainly in the size of the defense industry, for example, drones with artificial intelligence capable of killing a person.
