In 2023, hackers have never had more opportunities to steal data and infiltrate computer systems. It is also frightening how advanced and sophisticated the techniques to achieve this have become — a combination of creativity and advanced AI prey on the unsuspecting and security conscious among us.
But as AI continues to develop rapidly and cybercrime methods become even more advanced, so do the ways in which we fight them. AI is making cybercrime easier and more challenging to fight simultaneously. This article will explore the most advanced methods that AI and machine learning are using to battle against cybercrime. But before diving into that, let’s define artificial intelligence, machine learning, and what cybercrime consists of exactly.
What is cybercrime?
In short, cybercrime is criminal activity carried out online. This type of crime is commonly referred to as scamming or hacking. Cybercriminals use advanced techniques and AI to prize information from internet users and fraudulently access information, such as private data and, eventually, money from a bank account. There are countless ways a cyber attacker can steal information, including:
- Phishing
- Ransomware
- Social engineering
- Praying on configuration errors
- Password cracking
These are just some ways a hacker could steal information, and one must follow various best practices to limit risk exposure. Thankfully, AI and machine learning are making this as easy as possible.
AI & machine learning
What is artificial intelligence?
Artificial intelligence refers to how computers and machines can mimic human cognition and behavior. AI is becoming increasingly integrated into all our lives, whether we know it or not. While the term may evoke dystopian sci-fi dreams or nightmares, depending on how one looks at it, AI is all around us. Those who have ever used Siri or Alexa or been to a website that recommends products and has a ‘things you might like’ section, that is AI in action.
We do not know to what extent AI’s ability to replicate human cognition is yet, and we are very much at the bottom of the ladder of what AI is capable of. But one of the most exciting and vital components of AI’s future is machine learning.
What is machine learning?
Machine learning is the ability of a computer/machine to learn based on historical data. It is an area of computer science concerned with teaching machines to receive and interpret information, then use it to change how they predict things. In layman’s terms, it is how a computer learns, remembers and adapts its actions and recommendations accordingly.
Netflix is a prime example of basic machine learning and website personalization. When browsing the homepage, one sees recommendations of things to watch based on what they have watched previously, how they navigate the page, what they search for, and other factors such as watch time and related video strings. This is a very basic version of machine learning. So why is all this important in the fight against cybercrime, and how is it being used right now?
Applications for artificial intelligence in cybersecurity
Here are some common uses of AI in cybersecurity:
- Detects phishing scams
- Identifies bots
- Protects passwords
- Makes networks more secure
- Behavioral analysis
- Authentication
- Incident response
- Detects fraud
Now we know some of the primary applications of AI in cybersecurity, let’s look at how it helps us in a little more detail.
How is AI being used to fight cybercrime?
Using AI to fight cybercrime is like trying to fight fire with fire, a solution that never truly gets anywhere and one in which hackers and cyber security experts continue to cancel each other out. While we develop a new way to identify digital scams, hackers find the next way to overcome them. It is an ongoing battle that will not end anytime soon. But for those concerned about their data and privacy online, here are some ways they can take comfort in how AI is winning the helping win the war:
Behavior analysis
AI software can be used to identify suspicious behavior patterns online. For instance, too many login attempts, dodgy file transfers, questionable user information and other anomalies in user behavior. AI software helps both identify this and compare it to typical online behavior.
Authentication
Providing authentication is one of the best ways AI is helping us against cybercrime. However, the authentication practices we have relied on for years, such as Google’s Captcha, may become obsolete. AI has allowed scammers to overcome complex authentication requests using optical recognition software to identify an image, replicate it and solve it, bypassing the previously solid Captcha form.
So where does that leave us on the lawful side of security? From a security perspective, we can adopt a similar approach. We can use AI to scan and recognize strange attempt patterns and online behavior reminiscent of phishing and malware attacks. AI can help us compare these patterns to baseline ‘normal’ online behavior — enabling us to identify and prevent the problem before it happens. Pretty cool, right?
Facial recognition
Facial recognition is becoming an everyday norm regarding security best practices, and thankfully, it is one of the most challenging methods of verification to compromise — unlucky hackers.
Many already use facial recognition to unlock their phone or approve a transaction from their mobile banking app. To utilize facial recognition, artificial intelligence uses biometric data to analyze points on someone’s face, unique to them, meaning they are the only ones who can access that particular data.
Credit card/online payment security
Previously, financial institutions would have to manually analyze transactions in real-time to approve them, which is impressive. However, AI is making this 100x easier. Now AI software can simultaneously support billions of transactions (165 billion per hour, to be exact) in real time. Pretty impressive, right? This same software can help identify signs of fraud and allow institutions to step in and prevent them.
Natural language processing
Natural language processing (NLP) is a machine’s ability to recognize (and replicate) human speech and natural language use — a beneficial AI asset in cyber-attack prevention. AI that understands human language detects and prevents phishing systems, particularly vishing (voice phishing).
AI with NLP capabilities can analyze the language used in phishing messages, whether vishing or an email. It can then take that data and compare it to historical data, indicating if that message is reminiscent of previous scams of the same nature.
Network security & antivirus
AI and, most notably, its ability to recognize returning traffic (and tag it as suspicious) is one of the most exciting ways AI combats cybercrime. For traffic/IP addresses that are representative or have been tagged as suspicious, AI systems can jump in and stop network security breaches before they even happen.
Improvement over time
The remarkable thing about artificial intelligence and machine learning is their ability to adapt and improve over time. While this is true, the technology and scams were looking to beat, and ML algorithms can receive data and use it to recognize future patterns, becoming more accurate and efficient. As scams become more sophisticated, so does the software that identifies them.
Automation
AI can also help us automate and shorten response times after a malicious attack. Where previously, a security team member may have had to tend to a cyber-attack breach manually, AI can now step in and prevent it automatically. Even if AI cannot solve an issue, it drastically reduces response times. It allows security personnel to step in and get it sorted quicker than if they had to first identify it.
Teaching cybersecurity awareness
The data we collect via AI software can raise cybersecurity awareness. For example, imagine software that analyzes the network traffic and identifies suspicious and potential scams. We can use this to show employees what to look for in scams.
Huge data capacity
Large companies typically handle massive data and are the most likely targets for large-scale phishing and data-breaching scams. Therefore, these organizations need to be most vigilant to it and updated with their prevention methods.
While storing, managing and tracking lots of data manually is incredibly time-consuming and costly, not to mention manually reviewing it for security concerns, AI can automatically scan for threats and detect issues.
Eliminates mundane security tasks
Even the best security personnel are susceptible to human error and complacency when security tasks are completed manually, there is always this risk, but with AI, human error and complacency is eliminated. AI software means you can implement all the latest security best practices on autopilot.
Downsides to using AI to fight against cybercrime
Like with all new technologies and company processes, for that matter, there are a handful of downsides that come with using AI in the battle against cybercrime. Some of these disadvantages include the following:
- Initial AI adoption can be time-consuming.
- Costly to implement.
- AI is just as available to cybercriminals as it is to everyone else.
- AI requires a large amount of clean data to work efficiently.
- AI is not perfect and can still produce anomalies and misinterpret data.
Despite these downsides, we are still in the early days of how AI can help us, so it is unfair to make these claims, but those looking to implement AI to beat cybercrime should just be careful of these points.
A career in cybersecurity?
Those interested in learning how to become a cybersecurity specialist, do not worry — AI is not at the level where it can replace human expertise. Instead, it is simply a super handy tool that we can use to help us streamline our security protection efforts. A Master of Science in Cybersecurity from an accredited institution like St. Bonaventure University, covers the important roles that Artificial intelligence and machine learning play in the fight against cybercrime and how they can be used alongside existing methods.
Final words
While AI is both improving online security and enabling hackers to develop sophisticated attack techniques, there are many techniques (many using Artificial intelligence) that one can use to reduce their risks online. There is no doubt that AI is helping us to identify, prevent and solve cyberattacks and security issues. Here is a recap of the main ways AI helps us fight cybercrime:
- Behavioral analysis
- Authentication
- Facial recognition
- Online payment verification
- NLP
- Network security
- Ability to learn, adapt and improve over time
- Automation
- Teaching purposes
- Large data capacity
The fight against cybercrime is an ongoing process, and who knows how long the battle will go on. It is an interesting time, and the extent to which AI can help us overcome cybercrime is yet to be seen.
