In the network security space, is it right to implement AI and machine learning technology?
Today I will discuss implementations of AI or practical implementations of AI in the cybersecurity world. I will also mention some terminology and conclude with some insight into the future of Artificial Intelligence and machine learning in cyberspace.
So let me start with the following observation, which all of you will agree with me. We live in an era of digital transformation where data is a vastly valuable asset. Right?
According to economists, various statistics are supporting this claim. One interesting fact that I found,
- Forty-nine percent (49%) respond that analytics helps in making better decisions.
- Sixteen percent (16%) of respondents say analytics improves enterprise strategy.
- Ten percent (10%) of the responder believes that analytics enables better relationships among the customers and business holders.
The number to make use of that data, we need to analyze it. This is called big data analytics. And it is all about gaining some knowledge from those large amounts of data and doing it in the business context. The business would be:
- Cybersecurity defense
- Cybersecurity offense
Now, let’s take a look at implementations of AI or machine learning in the cybersecurity world.
1. AI & ML for detecting malware in files
You might also be aware that it’s an ongoing arms race between attackers and defenders. The attackers hide or better hide malware in files. Every day the defenders struggled to find whether those files are malicious or not.
Nowadays, companies are using the latest and hottest technology called AI & machine learning to find out those malicious files. To detect those files, they perform the task like face recognition.
The way face recognition roughly works is that theories in the face help machines identify faces, and similarly, files also have features inside them. There are numerous ways to exploit those features from files.
Machine learning technologies can do so and then determine with a high probability whether the file is malicious or not.
2. Analyzing whether the website is encrypted or not.
The second one is really interesting as you all know web traffic is becoming more and more encrypted. Artificial intelligence and machine learning are used to announce security and ask for confidentiality. But that makes it very difficult for any security engine to understand what is going on with that traffic?
Some statistics, recent Google statistics say that 81% of the most popular websites in the world are encrypted by default.
Google plans to announce on their browser:
Every unencrypted website is not secure. So they will change the definitions of the browser. We are really going towards a fully encrypted Internet Society.
Also, as professor Bannister mentioned:
There are ways to use machine learning to understand whether a file or traffic is malicious or not without decrypting it, without looking at what’s inside.
He mentioned that in the context of keeping privacy but it also is very important in the context of keeping performance. Because decrypting, encrypting is a painful task to any machine.
3. AI & ML for authentication and password protection
Authentication and password protection are the two important assets of security policy.
These two significant pieces of organization security are the formation of security strategy and helping in making a strong network topography. Ordinarily, both of these exercises are very tedious. Presently, we can utilize AI to assist these cycles, which it does by noticing and learning network traffic designs just as proposing security approaches. That doesn’t just spare time yet in addition to a great deal of exertion and assets which we can rather apply to zones of advancement and development.
4. Social Analytics with AI
Another promising improvement of security by AI comes from its social analytics capacity. This means ML algorithms can learn and make an example of your conduct by investigating how you utilize your gadget and online platform. The subtleties can incorporate everything from your normal login times and IP address to even the way you are typing and scroll down the pages.
The AI algorithms notice surprising exercises or any conduct that falls outside your standard examples, it can signal it as being finished by a dubious client or even can block the client. The exercises that tick off the AI calculations can be anything from huge online buys transported to addresses other than yours, an unexpected spike in record download from your chronicled envelopes, or an abrupt change in your composing speed.
Let’s have a quick look at the other side (enemy). The other side is all about one manipulating machine.
The example I’ve taken here is from another discipline or another world but it is the same principle or it could be done in the same principle.
1. In the cyber world, it is about using the rules of the machine against itself.
Let’s take an example of autonomous cars. As there are various signs on the road that says where to move, where to stop, and much more. In automation cars, we embed those rules. So, whenever those signboards come, it will automatically guide you.
There might be the circumstances that your car sees the rock, the signs of the road allow it to get in but they don’t allow it to get out. No machine understands the context. It will not understand the situation, just guide you to go in. It’s hard for a machine to understand that the parking lot is fully empty and available.
Every 5 years old kid would understand that but the Machine doesn’t. So this is in a way that the machine shows its setbacks. This is without any manipulation, without penetrating the machine. It was fooled so the same principles could apply also to different security engines.
Hackers can manipulate machines or make them fool with their activities. That’s why we can’t completely rely on machines. We as humans have to see the things around us that everything is on the right track.
2. The other example is about fooling people, not fooling machines.
As you all know, the social marketer is utilizing the art of manipulating people. It is an important and effective way.
Attack vector or at least a way to obtain a target. What we see according to researchers and also in tools is that machine learning is starting to play a role. Herein obtaining targets in finding the right people with the right amount of money, with the right information about them.
An attacker could utilize this tool for their betterment. This will probably always be the weakest point of human beings. This is another drawback of AI and ML in cyberspace.
Let me have a quick look into the future of AI and machine learning in cybersecurity
We’ve seen AI and a subset of AI i.e machine learning uses technology for security and it could be used as often. The question is can it be more than just the technology in the cyber world?
Will this technology have a good future? Obviously, it has a possible future, however, I don’t have my crystal ball but it has a possible future and the future is called the self-driving Network.
It is all about the infrastructure that we use.
- Digital infrastructure
- Computing infrastructure
They all are acting within an intelligent entity. AI entity to defend itself to recover from failures automatically. It eliminates the complex programming and management tasks required today to run your network.
So, without getting into too many technical details, there are some technologies today in the industry that are starting to move in this direction. This is a possible future.
Many people ask the question: do we want the machines to take over? Do we want AI to become dominant in any area?
But let’s talk about cybersecurity as an example and I like to present the companies that find a good scope of AI & Machine Learning in Cybersecurity.
61% of businesses are dependent on AI technologies to detect infringement attempts happening today. And, 48% of companies are going to increase their budgets for AI technology, especially for cybersecurity. Their budget will reach an average Of 29% in Fiscal Year (FY) 2020. (Source: Forbes)
There are various organizations that coordinate man-made brainpower(AI) in their network safety arrangements. I have checked out the business, some numerous big companies and startups are as of now utilizing AI as a component of their services and solutions. Instances of organizations previously incorporating man-made brainpower network safety tools to incorporate significant industry players like:
- Check Point
- Palo Alto Networks
Since AI is somewhat connected to human brainpower, and AI capacities are bringing in inescapable security controls. Today, almost all the apps that we are using require a security password or key to protect our private data.
It can be your bank application, your Gmail account, they all need password protection. Here we can directly say cybersecurity is important while using the app. When we talk about a professional setting, your company must need software or application that can protect you against hackers. AI has the power of providing better security like you must by using face recognition features for operating your phone.
Ultimately, we are dependent on these modern technologies in the cybersecurity world.
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Q1: Is AI in cybersecurity a blessing or a curse?
A1: Answering this question is a bit tough-we can’t directly say the use of AI technology is always a blessing for us. It too has drawbacks that other technologies have. It is a blessing to use AI & Machine Learning for:
- Providing protection against cyber attacks.
- Useful in detecting any malicious or abnormal activity.
AI Technology act as a curse:
- Sometimes it is easy to fool machines by making changes in a certain structure.
Q2: Which companies have already merged AI and cybersecurity?
A2: The list of companies who have merged AI and cybersecurity to keep themself safe are as following:
Q3: Are AI and cybersecurity together in the future?
A3: Undoubtedly, both the technology together (AI and cybersecurity) have a great future.