Machine Learning in Cybersecurity – Outlining the Real-life Examples
Cybercriminals compromised around 36 billion online records in Q3 of 2020. And how much time does it take to identify such breaches? – 207 days on average, according to IBM. It’s, thus, self-evident that machine learning (ML) in cybersecurity is highly desired, especially when cyber crimes have become more sophisticated than ever. Even with the employment of strong passwords and two-factor authentication solutions, cybercriminals find new ways to breach defenses.
It goes without saying that businesses need advanced protection, and ML can help better protect their assets from cybercriminals who are quickly identifying exploits in targeted attacks.
To that end, let’s evaluate the credibility of the concept of ML in cybersecurity and take a look at a few real-life implementation examples.
Machine Learning in Cybersecurity – How Valid is This Idea?
The idea of ML in cybersecurity is just as valid as any other in the field. Essentially, the idea that a machine could learn and/or predict patterns humans cannot is fascinating. It opens up the doors for potential breakthroughs in predictive analytics and enhanced threat detection.
Consider this example; k-NN is one of the most commonly used ML models to facilitate threat detection. It implements pattern recognition from data by looking for patterns between subsets of input data. Hence the term ‘k-nearest neighbour.’ This ability allows k-NN to predict whether or not an event will happen based on history, though it is important to note that it would use historical data only.
Although models like k-NN cannot predict the future, they sure can detect repeated patterns to develop a sound prevention system.
An excellent use case of this ML model in cybersecurity is looking at unstructured data threats. Such threats are difficult to detect, but perhaps this shouldn’t be a surprise. Most of the data in an organisation is unstructured (e.g., text documents, emails, etc.). So, it makes sense that a machine learning model might help identify patterns in this type of data and provide context that humans cannot always recognise.
ML can also be used for data cleansing and cleaning up noise from a dataset. For example, you may have received an email with malicious links within it. The problem is that the links point to non-malicious websites. They are what’s known as zero-day vulnerabilities. If an ML algorithm can clean up these malicious links and then decide which are the appropriate ones, it would be a significant step forward in cyber threat protection.
So yeah, the concept of ML in cybersecurity is as valid as ever.
The Three Main Applications of Machine Learning in Cybersecurity
ML in cybersecurity has three main areas of application: Detection, mitigation and prevention.
The detection category has the potential to not only detect threats for human errors and vulnerabilities but also spot potential situations that may have passed unnoticed in traditional implementations of threat detection. For example, if you configure a honeypot to monitor traffic and recognise specific patterns, you can catch malicious traffic that would have been missed using only human eyes. Machine learning models can also identify more sophisticated exploits which would go unnoticed by a human being.
The mitigation category has the potential to recognise attacks and offer suggestions for mitigation strategies. As a result, it is helpful for both humans and machines. Mitigation strategies could also offer suggestions for human prediction as a way to optimise security. For example, consider the Denial of Services attack. Here, the attacker is flooding a server with requests to the server and preventing legitimate clients from accessing the resources they require. In such a case, a mitigation strategy could offer suggestions on reducing the effects of such an attack, such as moving the servers to a different IP address.
Prevention is the most exciting application of ML in cybersecurity, as it could potentially guarantee that nothing unwanted is happening on a network. Consider network intrusion detection, where issues are detected by looking for signs of anomalous user behaviour. With ML, the designed system can understand the nature of anomalous behaviour and offer strategies to suppress it prior to any possible damage.
Machine Learning in Cybersecurity – The Real-life Examples
Facebook has implemented advanced analytics and machine learning, which has enabled the company to detect and remove 99% of the ISIS and Al-Qaeda-related terrorist content from their site. However, they are not stopping at 99%. They are working on achieving 100% detection and removal rates for these terrorist posts. Some of the techniques used to achieve this are:
a) Using a combination of ML and image recognition to spot and remove such content.
b) Implementing neural network models capable of detecting such content anywhere on their site, even in languages that they do not support.
c) Utilising anti-spam models, which could immediately recognise and remove terrorist-related spam posts as soon as they appear.
The use of machine learning is not limited to only social media sites or entertainment platforms. It is being used in the cybersecurity space as well. Google has previously announced that it had incorporated neural networks into its detection system for phishing attacks. As a result, when a cybercriminal tries to phish Google employees, they can be caught in a matter of seconds instead of hours or days. With such a fast detection system, Google can keep its users safe and protect its brand. A recent study also comments on Google’s advanced ML models that are 99.9% accurate in identifying phishing emails.
Microsoft has also announced that they are integrating machine learning models into their Office 365 platform. This means that the entire point of exploitation is moved away from the users. Moreover, it becomes possible for machines to predict when a malicious file could be introduced into the system using a known vulnerability in Office 365 or Windows 10.
Amazon has previously shed light on their use of ML models for cybersecurity. This includes banning sellers who violate the terms of service, preventing hackers from selling illegitimate software, and stopping hackers from accessing AWS servers and cloud storage.
In concrete terms, here’s what Amazon does using ML models:
a) Detect illegal file downloads by monitoring legitimate files uploaded to leak AWS information.
b) Prevent hackers from accessing the domain name system to prevent malicious DNS queries.
c) Detect and prevent DDOS attacks by monitoring incoming traffic and blocking access requests on the AWS platform.
Machine learning in cybersecurity is still developing, but it has proved to be a handy adjunct to traditional security. ML techniques offer powerful and versatile tools for detecting and mitigating cyber threats. At the same time, they hold the potential to help identify and prevent attacks in the future. This is particularly useful for organisations with numerous secure servers connected through public networks. Machine learning can help identify and remove threats before they cause any damage by recognising patterns in behaviour indicative of malicious activity.