It seems like everyone including the U.S. government is getting hacked on a regular basis. Whether it’s enterprises or even a regular Joe, we’re all under threat. Like the British broadband provider TalkTalk recently found out, we can all do more to protect ourselves. But fighting against cyber threats seems futile as staying a step ahead of criminals seems almost impossible. So it begs the question, is Machine Learning (ML) the answer we have all been waiting for?
The amount of data that’s being generated individually and collectively is multiplying rapidly. When it comes to cybersecurity, it’s not any different. As a result, this presents a new opportunity to reevaluate IT security and management strategies.
Today, cybersecurity tools and ML have started to work in tandem to help us become more proactive, detective, and reactive. But as we grow to identify threats before they occur, can we stay one step ahead of cyber criminals?
Cybersecurity Breaches: Common Cases
If you look at most of the security breaches in recent years, they have mostly been espionage or financially motivated. Most cyber criminals are stealing identities and money daily and they’re always coming up with new ways to do it.
This leaves corporate and personal data extremely vulnerable to network breaches via phishing attacks, malware, ransomware, DDoS attacks, and much more. It also leaves organizations with a never ending struggle to keep up with the speed and quantities of these attacks.
One of the major issues in the industry is the shortage of cyber analysts, configuration alerts, and manual coding to efficiently counter these attacks. As a result, Artificial Intelligence (AI) can be the missing link.
It has also become vital now for the survival of businesses as government watchdogs and security firms name and shame companies that are failing at cybersecurity.
Machine Learning (ML) and How It Applies To Online Security
ML grew out of pattern recognition and computational learning to computers that can learn through experience rather than being explicitly programmed. The core of ML lies in programming and data science. So with new learning algorithms rapidly evolving, ML enables cybersecurity apparatuses to scale much faster.
As a result, it’s not a surprise that more than 75% of companies are investing in ML. If an algorithm can do what would take humans several hours (or even days) to accomplish, it’s more than an attractive solution to automate various business processes.
When it comes to cybersecurity, ML also has a lot of fans who see it as the last hope in the field. Their perception is that there’s too much data being generated and this makes it humanly impossible to keep up (and it will also be too expensive).
So how does ML help?
1. Machine Learning Fills the Online Security Data Analyst Skills Gap
There aren’t enough people with the skills and experiences to process and analyze the data that’s being generated. With automated processes focusing on recognizing valuable patterns in security data, companies can focus on hiring only those needed to fill critical positions.
Further, it can also be a highly cost-effective solution as you don’t have to hire a number of individuals to build teams to analyze the data.
2. ML Rapidly Identify Security Breaches
To react to a cybersecurity breach, you have to first identify it. ML will enable you to do this quickly as the algorithms can keep performing statistical analyses to identify abnormalities in the network.
It works by identifying normal vs. unusual behavior. If you signed up for Gmail’s two-step verification, you would have experienced this in action. This type of algorithm looks at the following:
- Login location
- Unusual time
- Multiple IP addresses
- Security threats
- Device failures
- Software patches
- ISP bandwidth
All of this enormous data together can generate granular industry intelligence. As a result, enterprises can now predict weaknesses in IT security infrastructure and enable rapid reaction.
3. ML Defends Against Ransomware
Malicious software that can restrict access to files or devices until a ransom is paid is activated long before big data analysis is conducted. So ML can be key to turning this major problem of profile creation into something small like anomaly detection.
As a result, AI can significantly speed up the process and react to it much quicker. If you’re maintaining a large network, it can be impossible to achieve this even if you had a legion of IT professionals working on it around the clock.
But not everyone is a fan of ML when it comes to cybersecurity. Here’s why!
ML and False Positives
ML is a great advancement in technology, but it’s not perfect. So cybersecurity professionals can breathe a sigh of relief because their jobs won’t be replaced by robots anytime soon.
You can’t yet rely on AI /ML alone to identify cyberattacks because the system tends to yield loads of false positives. So regardless of the technological advancement, we still require human intervention to make critical decisions on whether an actual breach has taken place.
Deficits of Attended Learning
So although attended learning means that humans will be needed in this field, it also means that the problem of trying to keep up with the massive volume of data that needs to be analyzed isn’t going away. As a result, there will be a large number of undetected cyberattacks and delayed reactions, even with ML.
With the current shortage of security experts in this field, you can expect the devious minds out there to continue to find new ways to breach even the most secure systems. The only solution here is to try and find the best combination of ML and IT professionals to try and combat the high volumes of security breaches.
But even with AI and an army of professionals, there’s no magic formula here to build a network fortress for enhanced protection.
Although ML will help increase cybersecurity, enterprises won’t be any closer to being free of cyberattacks and the resulting scandals. But it’s still a lot better than not having any ML in your cybersecurity apparatus, isn't it?