Machine Learning And Artificial Intelligence in Cybersecurity
By: Benjamin Roussey
A renaissance in Artificial Intelligence (AI) and Machine Learning (ML) has redefined traditional cybersecurity and opened up new horizons.
AI is the ability of a machine to have human-like intelligence that enables them to perform tasks ‘smartly’ without being explicitly programmed for the same.
ML is considered as a subset of AI. When new data is provided to a machine, it is through ML algorithms and mathematical models that it makes smart predictions such as will the New England Patriots get caught cheating again?
Businesses are facing massive threats from smart cyber vandals and need more water-tight cyber-security features to stay secure. Countries and states also need to pass stricter laws combatting these digital criminals as well but this is another topic. We’ve all seen the brilliant movie Blackhat! We look at how ML and AI are helping in preparing an ideal cybersecurity system.
The increasing complexity of cybercrime
When it comes to cybercrimes and their vulnerabilities, things are getting worse. Cybersecurity threats have become more multifaceted and complex, resulting in more cybercrimes than ever before.
IoT era is upon us, which means there are more devices connected together, making the cybersecurity landscape even more complex.
In fact, in 2018 alone, there were 357 million cases of cyber vandalism recorded and none these acts were committed by someone as unsophisticated as Homer Simpson you can believe that. Traditional security systems are becoming less effective, and it’s time to use the potent ML and AI technology in cybersecurity.
Why cybersecurity needs ML and AI?
Why AI and ML, you ask? The availability of big data has improved the training of ML/AI models, proving them to be more effective than ever, and they’re improving, always.
How AI and ML models can be made flawless is mentioned in the four points below that form the four pillars of cybersecurity through ML and AI.
- New threats, patterns, malware, and anomalies can be detected when the machine is taught what is bad and what is good through rigorous training.
- To make the model an effective one, you need access to malware data from the past two decades.
- Data engineers and data scientist will pipeline all this data to the system for processing and creating an effective model.
- A security domain officer will classify what’s bad and what’s good, and then provide insights.
Some examples of ML and AI in cybersecurity
- Google’s Gmail has been using ML for years to filter-out spam and safeguards against malicious emails.
- Watson cognitive learning, a Machine Learning initiative by IBM, is being used to detect threats and for developing water-tight cybersecurity features.
- Balbix is a dark-horse for providing protection against both security breaches, and data breaches.
- Google’s deep learning prevents security threats in real time by using AI-powered risk prediction.
The future of ML and AI in cybersecurity
Cybersecurity’s core principles rely on the concept of ‘defense-in-depth’ or the ability to provide layered protection through multiple layers of security. This makes MI and AI the right choices, as the ML/AI models have the ability to ‘do it all’ and keep the systems updated with the incoming data.
Scanning of files through traditional cybersecurity tools leads to latency and slows down systems. ML and AI-powered tools can do the same amount of work in 1/10th of the time and can be twice more effective due to ‘security at end-points.’
AI and ML cybersecurity will be more user-friendly and efficient than any traditional cybersecurity product.
A more common and dangerous threat, known as the ‘zero-day-threat’ has emerged, with no recognizable signature. Until today, sandboxing is used for the analysis of such threats. The process takes several minutes as it has to mimic the behavior of the malicious file.
This is where a properly trained ML/AI model kicks in! What sandboxing takes several minutes to complete, ML/AI tools can do in milliseconds, leading to faster and highly user-friendly systems.
As most of the cyber attackers borrow from previous attacks, ML/AI based software will be able to predict the behavior and detect these polymorphic variants.
There is no doubt that there are speculations about ML/AI based cybersecurity tools being more efficient. But, initial results show that it may take several years before the technology can be fully developed.
A properly trained ML/AI model that is trained by experts in cybercrimes and data scientists can be used as a part or a standalone feature in the ‘defense-in-depth’ principle of cybersecurity.