Machine learning is a subset of artificial intelligence (AI) that involves developing algorithms and statistical models enabling computer systems to learn and program themselves from experiences without being explicitly programmed. In other words, machine learning involves creating computer systems that can learn and improve on their own by analyzing data and identifying patterns, rather than being programmed to perform a specific task.

Machine learning starts with data. The more data you can feed the machine, the more it can learn and develop its systems. Here the programmers have a choice on how they want the machine to behave. They can choose a machine learning model that predicts future outcomes (supervised learning) or one that finds patterns in existing data (unsupervised learning). To do this, the algorithm needs to be fed data that is labeled or unlabeled in order to train the machine. Depending on if you use labeled or unlabeled data, you will get a different output from the machine. Labeled data will develop predictions while unlabeled data will cluster the data that was inputted.

Supervised learning

This involves training a machine learning algorithm on a labeled dataset, where the desired output is already known. The algorithm learns to make predictions based on the input data and the known output.

Unsupervised learning

‍This involves training a machine learning algorithm on an unlabeled dataset, where the desired output is not known. The algorithm learns to identify patterns and correlations in the data without any pre-existing knowledge of the desired output.

Reinforcement learning

This involves training a machine learning algorithm to make decisions based on trial-and-error feedback. The algorithm learns to take actions that maximize a reward function, based on the feedback it receives.

Explainable AI

Explainable AI is an emerging field that focuses on developing machine learning algorithms that can explain how they arrived at a particular decision. This is important for ensuring that machine learning models are transparent and trustworthy, particularly in applications such as healthcare and finance.

Generative AI

Generative AI is a type of AI that is capable of creating new and original content, such as images, videos, or text. This is achieved through the use of deep neural networks that can learn from large datasets and generate new content that is similar to the data it has learned from. Examples of generative AI include GANs (Generative Adversarial Networks) and Variational Autoencoders (VAEs).

Image recognition

A machine learning algorithm can be trained to recognize images of cats by being shown a large dataset of images labeled as either "cat" or "not cat." The algorithm analyzes the images and identifies features that are common among the cat images, such as the shape of their ears, eyes, and whiskers. Once the algorithm has been trained, it can test new images and see if the new images contain the desired visuals. As the algorithm is exposed to more data, it can improve its accuracy and become more effective at recognizing images. This type of machine learning is called supervised learning, where the algorithm is trained on labeled data to make predictions on new, unseen data. 

Natural language processing (NLP)

Businesses often incorporate chatbots on their public websites as a way to provide continuous consumer support throughout a user’s interaction with the website. are a good example of how NLP can take user input, analyze the text for keywords, intent, and other recognizable features, and generate a response. Response generation can be done with pre-defined terms or natural language generation.

Linear regression

This is a process of providing continuous outcomes for a target variable based on historical data. It is commonly used for predicting stock prices or determining the ROI for companies budgeting their spending. For example, this algorithm can be used by a marketing team to determine how their spending on ads might impact different sales campaigns. 

Logistic regression

‍This is the process by which an algorithm can determine the category of an input. For example, the machine develops an understanding of what is spam and what is not with the data it is given, it can then use this information to determine if an email is spam or legitimate.   

Decision trees

‍Decision trees can be used to classify data, but a major benefit they have is that they are easy to read, making it easy for a data scientist to understand the decision-making processes of the machine. It is often used to support human decision making; however, it requires human input to remove unwanted “branches” as it becomes overly complex.

Neural networks

Neural networks can be trained on large data sets to recognize patterns in language or images in order to classify new data. This algorithm trains computers to better recognize these patterns and provide more accurate results with limited human assistance.

Machine learning plays a significant role in cybersecurity by enabling automated detection and response to security threats. With the ever-increasing volume and sophistication of cyber threats, machine learning offers a more efficient and effective approach to detecting, responding to, and mitigating threats. Some specific ways machine learning is used in cybersecurity include:

Anomaly detection

‍Machine learning algorithms can be trained on large volumes of data to identify patterns of normal behavior on a network. When the algorithm detects behavior that falls outside of this pattern, it can alert security teams to investigate potential threats.

Malware detection

‍Machine learning algorithms can be trained to recognize patterns in code that are characteristic of malware. By analyzing the code of incoming files or emails, the algorithm can identify potential threats and block them before they can cause harm.

User behavior analysis

Machine learning algorithms can analyze user behavior on a network to identify potential security risks, such as insiders or compromised accounts. By monitoring user behavior, the algorithm can detect abnormal patterns and alert security teams to investigate further.

Fraud detection

Machine learning algorithms can be trained to recognize patterns of fraudulent behavior, such as credit card fraud or account takeovers. By analyzing large volumes of data, the algorithm can identify suspicious activity and alert security teams to potential fraud.

The Darktrace ActiveAI Security Platform is designed for your Security Operations Center to eliminate alert triage, perform investigations, and rapidly detect and respond to known and unknown threats, whilst exposing risk gaps across your technologies and processes so your team can shift to a proactive cyber approach.

The solution is built on Self-Learning AI that continuously trains from your ever-changing business data wherever it is deployed, with further enrichment from external threat intelligence and third-party alerting. This learning is not limited by yesterday’s threat data but looks at deviations of your unique business operations, revealing even the subtlest indicators of malicious intent that may pose a threat to your business, known, unknown, and never before seen.

Scalability

As the volume and complexity of cyber threats continue to increase, traditional approaches to cybersecurity are becoming less effective. Machine learning-based solutions offer a more scalable approach, allowing organizations to analyze large volumes of security data and detect potential threats more efficiently.

Speed

Cyber threats can emerge quickly and spread rapidly, leaving organizations with little time to respond. Machine learning-based solutions can analyze security data in real-time, allowing organizations to detect and respond to threats more quickly.

Accuracy

Machine learning algorithms can analyze security data with a level of accuracy that is difficult to achieve with traditional approaches. By identifying patterns and anomalies in security data, machine learning-based solutions can more accurately detect potential threats.

Automation

Machine learning-based solutions can automate many of the tasks associated with cybersecurity, freeing up security teams to focus on more complex tasks. This can help organizations to improve their overall security posture while reducing the workload on their security teams. 

Cost-effectiveness

By automating many of the tasks associated with cybersecurity, machine learning-based solutions can help organizations to reduce their overall cybersecurity costs. This can be particularly beneficial for smaller organizations that may not have the resources to invest in traditional cybersecurity approaches.