Unsupervised Learning for Cybersecurity: Next-Gen Protection against Threats
Researchers have been working on making machine learning detect cyber threats since the late 1980s.
As the volume of cyberattacks grows, security analysts are always on their heels to provide a shield. To address this issue, developers are showing interest in using Machine Learning (ML) to automate threat-hunting. As a sub-field of machine learning, unsupervised learning is making a footprint in detecting malicious content.
Resisting cybersecurity challenges with machine learning is not a new thing. Researchers have been working on it since the late 1980s. But the progress has been slow. Today, machine learning is showing increasing promise with the advent of big data because the quality of information from which ML can learn is improving. At its simplest, machine learning is defined as the ability of a computer to learn without being explicitly programmed. In principle, machine learning can help businesses better analyze threats and respond to attacks and security incidents. It could also help to automate more menial tasks previously carried out by stretched and sometimes under-skilled security teams. Subsequently, machine learning in security is a fast-growing trend. Analysts at ABI research estimates that machine learning in cybersecurity will boost spending in big data, artificial intelligence (AI), and analytics to US$96 billion by 2021, while some of the world’s technology giants are already taking a stand to protect their customers better.
Cyber breach is a hectic issue. In many cases, it may take up to six months to even detect a breach, and on top of that, there is an average of nearly fifty days between when a breach is discovered and when it is reported. With the cybercrime industry set to cost businesses over US$2 trillion each year, cybersecurity software clearly needs to get smarter. The cyber defense industry is investing efforts in awareness and propagation of cyber resiliency methods and practices. A key driver of this shift is the rise of unsupervised learning as a critical toolset in cyber defense approaches with different degrees of sophistication and success achieved in the industry. Machine learning algorithms come in many shapes and forms, but most of them perform one of three tasks namely,
- Regression- This algorithm identifies correlations between different datasets and understands how and to what degree they are related to each other.
- Classification- Usually performed by supervised learning algorithms, trains on a dataset’s previous observation and tries to apply it to a new, unseen data. It involves taking artifacts, which may be textual or multimedia content, classifying them into one of several labels.
- Clustering- Usually performed by unsupervised learning algorithms, it works directly on new data without considering previous examples. Clustering involves identifying commonalities between artifacts and grouping them according to their common features.
However, unsupervised learning is limited in its network security abilities like finding threats because it looks for specifics that it has seen or labeled before. Unsupervised learning stands out by constantly searching the network to find anomalies.
What is unsupervised learning, and what are its functions in detecting cyber threats?
Unsupervised learning is made possible through a group of emerging technologies that allow for cybersecurity software to predict and safeguard against potential future attacks, without ever needing to experience a similar breach or an attack. In a new world of dynamic attack surfaces and threat vectors, cyber defense mechanisms must continuously evolve to work upon unlabeled data which is under the purview of unsupervised learning. This is very important on cybersecurity grounds as attackers change the methods frequently. Unsupervised learning flags anything that stands out of usual, which is the need of the hour.
With unsupervised learning, machine learning and AI-based algorithms are constantly working to discover new potential ways that they could be attacked in the future. For example, banks will be able to safeguard themselves against future attacks without ever witnessing a successful breach. This is where unsupervised learning has the wildest potential. Even though when the success rate is not 100%, it will slowly improve over the years and technology improvements.