Machine learning offers a wide range of applications in various sectors, including cybersecurity. Indeed, the cybersecurity market for artificial intelligence (AI) is anticipated to reach $38.2 billion by 2026.
Anyone working in corporate security or cyber investigations is aware that adversaries are rapidly improving their capabilities, and cyber assaults are spreading across all industries. In this rapidly changing world, cybersecurity teams are turning to machine learning to detect better and manage digital dangers.
What is machine learning, and why should you include it in your cybersecurity training toolkit?
What is Machine Learning?
Machine learning and AI are frequently used interchangeably, although machine learning is a subset of AI, which is difficult to define on its own. AI may be informally described as “computational intelligence.” However, much like “intelligence,” what defines AI varies depending on who you’re talking to.
On the other hand, machine learning is simpler to define: It’s an AI method that gives a computer the job of learning by utilizing algorithms to discover patterns in an extensive data collection. Deep understanding, one form of machine learning, is excellent at tackling various issues. Because it learns similarly to biological neural networks, the phrase “deep learning” is sometimes interchanged with “neural networks.”
Machine learning’s objective in text classification is to teach a machine to identify that a specific combination of words corresponds to a particular class of text or category, such as whether the content of a dark web post signals a data breach.
Some machine learning methods go a step further. These deep learning algorithms convert words to vector spaces, teaching the computer to recognize word order, recognize ideas, and contextualize classified data across various languages.
For end-users, this implies accurately locating material by idea or meaning rather than searching by keyword and manually evaluating results for relevancy.
Why Cybersecurity Needs Machine Learning
It’s fairly obvious how machine learning may be used to improve cybersecurity, particularly in the threat intelligence process. Cybersecurity experts use data from the surface, deep, and black web to detect risks and acquire pertinent investigative information.
That’s an enormous quantity of data to examine and evaluate effectively and adequately manually. In addition, due to a skills scarcity in the field, up to 51% of businesses are having difficulty hiring cybersecurity analysts and investigators, further fueling the demand for automation.
At each step of the threat intelligence lifecycle, machine learning can assist in automating the process of locating, contextualizing, and triaging essential data. This might entail anything from identifying suspicious network activity in real-time to spotting dark web forum postings indicating a data breach.
Organizations may examine a greater amount of data with machine learning skills to guarantee they aren’t losing out on vital information while decreasing false positives. Staff can also devote more time and effort to activities that need human intelligence rather than mechanically curating data. After all, the aim is to increase the skills of a cybersecurity team.
How is Machine Learning Used in Cybersecurity?
Machine learning may help at various phases in the threat intelligence process. However, there is no such thing as a “one-size-fits-all” digital risk prevention product in the real world. Instead, most businesses (58 percent, according to Forrester research) use two or three solutions to meet their threat intelligence needs. So what do these tools look like when it comes to machine learning?
Consider a solution platform as an example. The Platform collects and filters threat data from a wide range of social media platforms and deep and dark websites. Based on the data in these networks, machine learning models are trained to identify breaches and data leakage (among other types). This implies that instead of searching for breach-related keywords, security employees may search for the notion of a breach that is relevant to their company. Furthermore, analysts don’t have to sift through results to identify actionable breach indications since the Platform automatically pulls relevant data.
Machine learning may be used to acquire threat intelligence in various ways, including ones that aren’t related to cybersecurity. Machine learning models, for example, maybe taught to acquire business risk intelligence, such as:
- Attempts at brute force, phishing emails, and virus detection are examples of other cyber security risks.
- Hate speech, identity hatred, and toxicity are all examples of hate speech.
- Radicalization, active shooter manifestos, and other physical threat indicators are signs of physical danger.
- An entity’s or a brand’s public perception
- COVID-19, for example, is a specific type of talk or material connected to a crisis.
After the first attack, it takes approximately 30 weeks on average for companies to identify a data breach. This substantial lag is partly due to flaws and inefficiencies in obtaining actionable threat intelligence from publicly available internet sources. However, this risk may be mitigated using machine learning-based techniques.
Asking the Right Questions
For business executives, this entails asking their colleagues the proper questions in technology and cybersecurity. According to Engel, there are many main areas on which to concentrate:
- Do they have the information they need to respond to a live attack? What type of information are they gathering—do they have data on the network, endpoints, and the different clouds where data and applications are deployed?
- Is the data organized in a way that can be utilized for detection and decision-making, or is it just sitting there? Can they successfully combine data from a variety of sources?
- Are your teams sure that they would be able to identify any network attacks by analyzing their data?
There has never been a better moment for corporate security teams to include machine learning-based techniques into their risk management strategy, thanks to breakthroughs in AI, the rising number of available threat data online, and heightened cybersecurity concerns.
