In the new era of Artificial Intelligence (AI), the game of cat and mouse in the realm of cybersecurity has reached its head. AI, from its side, gives cybersecurity added power for more intelligent detection and analysis of cyber threats. But unfortunately, hackers are also using AI as a new weapon to create new – and stronger – malicious innovations.
There are hundreds of billions of changing signals crossing networks all the time – a number that can double monthly sometimes depending on the size of your enterprise. This puts businesses in front of some serious challenges that normal human efforts can’t keep track of. Furthermore, things are only going to get worse because attackers are actually beginning to use AI and machine learning technologies for their exploits.
On top of that, businesses have to prepare for the continued emergence and adoption of IoT (Internet of Things) technology. With this addition, there are more connections between devices than there are between humans on the whole planet. With the attendant of the entire digital transition process, it also adds more critical cybersecurity challenges even at the individual level. With all of that in mind, using AI to combat threats is an imperative.
AI for Cybersecurity
The relation between AI and Cybersecurity includes various multidisciplinary angles. One specific view is to take the benefits of machine learning and use them to increase the intelligence of cybersecurity in regards to different malware classification processes and intrusion sensing techniques.
From another angle, AI itself suffers from a wide range of cyberattacks. These types of attacks seek to mislead the machine learning process or disturb AI decisions. This forces the security teams using AI to take into account using special cybersecurity defense technologies, to safeguard the privacy in machine learning and better secure access to sensitive information.
This article will focus more on the first angle and it will summarize some existing research efforts for adopting AI and deep learning solutions and methods. In addition, we will look at the role of neural networks in the construction of more solid cybersecurity systems.
What Are the Benefits of Using AI for Cybersecurity
Since there are a lot of benefits for addressing AI in the Cybersecurity field, it makes sense to make a list of the most important benefits, which include:
- Creating smarter AI learns: Over time, involving the intelligence capability of the machine direction in learning a business network’s behavior. Also, creating patterns of normal and abnormal behavior to block any possible threats in advance.
- Identifying unknown threats: AI is able to detect any new threat models even before the security communities inform it. AI has proven it can learn new social engineering innovations.
- The capacity of AI to process big data: The deep learning models, alongside modern big data analysis capabilities, empower cybersecurity to deal with trillions of bits of data transferred daily from an average midsize to an enterprise company’s network. In addition, residential proxy AI Technology can detect any masked threats in the massive amounts of chaotic traffic.
- More control for vulnerability management: By intelligent assessment of security measures using AI research, we can improve vulnerability management and strengthen weak points. We can also speed up the whole process and place more focus on important security needs as they occur in real-time.
- Better prioritize: AI helps during the times when you have to deal with multiple attacks all at once. For instance, a ransomware attack with a denial-of-service attack. AI helps to choose which to deal with first and to fix imperfections of human mistakes and negligence.
- Reduce duplicating procedures: By using limitless power for taking care of duplicative cybersecurity processes, AI can defeat the problem of many boring routine tasks for humans, which get tedious or exhaust security teams in the long term.
- Making smart and secure authentication: AI handles the ability to identify digital identities in a smart way, which facilitates making new secure passwordless authentication models.
Adopting AI for Cybersecurity
Despite the growth and adoption of Artificial Intelligence and the emergence of many subgroups of AI levels (such as machine learning, deep learning, expert systems, and neural networks) AI still needs more qualitative leaps forward to become able to emulate real human intelligence.
Because we are in a transition phase between two eras, we cannot ignore IA nor rely on it completely. As usual, transition phases are sensitive times and full of uncertainty and chaos. This makes it necessary to architect precise and dynamic solutions that can keep abreast of the developments of new threats and innovations.
Since cybersecurity is a complex problem, it’s an ideal subject for AI and machine learning. They can enhance the auto-detection of threat detection and suspicious behavior. While these processes existed before AI emerged, AI can still give them a new intrinsic ability to update and learn directly from the evolution of attackers’ persistent tactics.
Applications of AI Techniques
To make clear how AI can push cybersecurity forward and help keep systems secure in this ever-evolving world, let’s showcase some types of AI for cybersecurity and learn what exactly it can do.
- Intelligent agent net: A kind of self-sufficient computer connected in groups to share data for actualizing properly in unexpected accidents. Especially for Distributed Denial of Service (DDoS) attacks. It also helps with detecting neural network-based intrusion.
- Neural networks: The artificial neuron and neural nets are able to learn and treat some cases. Thanks to being in countless networks, they can, in parallel, replicate and duplicate their ability to learn more efficiently. They learn patterns to make decisions in DoS identification, malware classification, spam recognition, and computer worms.
- AI expert systems: This is the most common of AI tools; a self-learning software that can answer questions or inquiries. Because it has an empty knowledge base and inference engine, in cyberspace, it helps for diagnostic purposes and solving complex problems in light of current knowledge and previous knowledge about a circumstance. It also determines safety efforts and helps for the ideal use of resources.
- Machine learning: The difference between learning problems depends on their complexity and if it’s supervised or unsupervised. It has value for a large amount of data, such as gathering expansive logs. While genetic learning algorithms and neural nets are representing these learning strategies, it is used as a part of threat detection systems also.
Early AI Cybersecurity Adopters
If you want to know more about some early examples for using AI in cybersecurity, they are not difficult to find. Surprisingly, some of them we use in our everyday life, but we aren’t even aware of it. Examples include:
- Gmail: Google has been adapting machine learning to filter and scan emails for about two decades.
- IBM/Watson: This is a cognitive learning platform from IBM used for many things, including chats/messengers and also for AI threat detection.
- Juniper Networks: As a disruptive idea aiming for sustainable solutions, it’s entered the world of cybersecurity with the goal of enhancing economics.
- Balbix: As a BreachControl platform, it involves the game of AI-powered observations and analysis. It also helps cybersecurity teams to become more efficient.