Machine learning, which is a part of the larger technology called Artificial Intelligence has become the buzzword of the entire world. C-suite executives and cybersecurity wizards are betting big on the possibilities of deploying machine learning for cybersecurity.
Before we delve deeper into practical applications, what is machine learning? Why has it become a hype all of a sudden when things are happening in the world, like sending a Tesla Roadster to Mars?
Machine Learning is, “the ability (for computers) to learn without being explicitly programmed.” They are software programs that can provide predictions by continuously learning from inputs they receive. Machine learning has enormous possibilities in industries and business functions where large volumes of data are handled. That makes cybersecurity a perfect fit for machine learning implementation.
But, how does machine learning help cybersecurity?
At the heart of machine learning for cybersecurity is anomaly detection. The word ‘anomaly’ means, “something that deviates from what is standard, normal, or expected.” you should know the future needs of cybersecurity.
Cybersecurity attacks are always, or at least in most cases transactions that exhibit an abnormal characteristic.
At some point, experts have a different opinion on Machine Learning & Web Security. According to their opinion Machine learning needs the assistance of human domain expertise as well as data to function correctly. Furthermore, basic security measures like installing SSL certificate to enable HTTPS address bar for the website, etc. need to be in place so that the ML system can build data models efficiently.
What SSL does is build upon the existing security possibilities to cater to the future needs of cybersecurity. So, you will still have to invest in traditional security measures like SSL certificates for some time to come. To conclude, in the coming days, we will see more use cases that leverage machine learning as a cornerstone technology to set up a defense against cybersecurity attacks.
Now, let’s see how machine learning can arm businesses with the power to fight cybersecurity attacks.
Monitoring Exchange of Data
Until now, a firewall program is used to monitor the exchange of data across the Internet. The firewall ensures that no bots or malicious programs are finding their way into the network to infiltrate the systems. This is not an easy task to do.
Primarily because firewalls are designed to be static. They are programmed in a certain way and remain so until a new upgrade or replacement is sought. This makes firewall less competent to stall hackers who find ways to bypass the firewall system. Whereas machine learning programs improve on a regular basis. They learn from the input and the feedback received from live interactions with data. This gives them a cutting edge over static safety measures like a firewall.
Predicting Attacks Even Before they Happen
The one thing that makes machine learning such a lucrative option for cybersecurity is its ability to predict the future. Machine learning systems feed on historical data and form patterns just like our human brain does. Based on these patterns, they analyze current databases to spot situations where a cybersecurity breach might happen again.
This prediction often helps plug the leaks thus preventing the attack altogether. In fact, machine learning can go a step further and automate the entire process of running vulnerability assessment, malware scans, and security upgrades. It makes up for all the shortcomings inherent to humans that often lead to massive security breaches.
Malware & Spyware Detection
Malware and spyware programs can hide among a cluster of other genuine files. Advanced malware and spyware programs can even keep themselves hidden from the scanning of antivirus programs. This makes malware and spyware detection a perfect ground for machine learning.
Machine learning updates its knowledge about old and current malware programs on a regular basis. It also keeps a track of the attributes of this malware. Unlike antivirus programs which are updated on a periodical basis, machine learning is updated on a real-time basis. This helps it spot malware programs easily. Machine learning deploys anomaly detection to see if there are files that exhibit traits that do not adhere to normal standards.
Identifying Zero-day Threats
According to Technopedia, “A zero-day threat is a threat that exploits an unknown computer security vulnerability. The unknown status of the vulnerability makes it even difficult for the security system to prevent the attack. The risk of zero-day attack is that it affects everyone from individuals to enterprise users.
Luckily, machine learning can help prevent zero-day attacks even before they occur. In fact, a team at the Arizona State University even conducted a study leveraging the Internet traffic of the dark web to identify zero-day threats. The team devised a machine learning system that can sift through the forum messages in the dark web to return warnings about zero-day threats.
Shield Against IoT Vulnerabilities
IoT (Internet of Things) which is ushering an era of connected devices is also reeling under the pressure of cybersecurity attacks. Uday Veeramachaneni, co-founder and CEO at PatternEx opines that “The problem is that the IoT’s will be distributed massively and if there is an attack you have to react in real-time.”
Machine learning and its subsets like behavioral analysis can help detect IoT vulnerabilities and flawlessly secure them. It helps in gathering all the data, sanitizing it, build data models and spot anomalies when there are vulnerabilities. ML can also stop hackers from accessing home appliances using which they can take control of an entire network. Furthermore, ML can prevent cloud-based applications like personal assistants from accessing malicious URLs that could possibly lead to a security breach.
Machine learning is bringing a whole new level of IT capabilities to the web world. It will redefine workflows in several industries. Cybersecurity is no exception to that. It will help enterprises enhance their security quotient and stay secure even in times when hackers are growing in expertise.