Machine learning is a subset of artificial intelligence that involves the development of algorithms and statistical models that enable computers to learn and analyze patterns from large datasets without being explicitly programmed. In other words, it is a process of providing machines the ability to learn from data, identify patterns, and make decisions or predictions without human intervention.
What is Machine Learning?
In machine learning, a model is trained using a dataset that consists of input variables (features) and their corresponding output variables (labels). The model learns patterns and relationships within the data and creates a mathematical representation of this knowledge, which can then be used to make predictions or classify new, unseen data.
There are several types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning. We will go through these individually later on in the blog.
Machine learning has found applications in various fields, such as healthcare, finance, transportation, and marketing. It has the potential to improve decision-making, automate tasks, and extract valuable insights from large amounts of data. However, it also poses challenges related to data quality, bias, interpretability, and ethical considerations, which researchers and practitioners continually work to address. Overall, machine learning is a powerful tool that has revolutionized many industries and continues to advance the capabilities of artificial intelligence.
How Does Machine Learning Work?
From a technical standpoint, machine learning involves a series of steps that enable an algorithm to learn patterns and make predictions or decisions. The process typically includes the following key components:
- Data Collection: Relevant data is collected, which consists of input variables (features) and their corresponding output variables (labels). The data should be representative and diverse to ensure the algorithm learns a generalized model.
- Data Preprocessing: The collected data is processed to handle missing values, outliers, and inconsistencies. It may involve techniques such as normalization, scaling, and feature extraction to ensure the data is in a suitable format for training.
- Model Selection: A suitable machine learning model is chosen based on the problem at hand and the available data. Different algorithms, such as decision trees, support vector machines, or neural networks, may be considered depending on the nature of the task.
- Model Training: The selected model is trained using the prepared data. The algorithm learns patterns and relationships within the data by optimizing its internal parameters. This is typically done through an optimization process that minimizes the difference between predicted outputs and actual labels.
- Model Evaluation: The trained model is evaluated using separate validation data to assess its performance and generalization ability. Various metrics such as accuracy, precision, recall, and F1-score are used to measure the model’s effectiveness.
- Model Optimization: If the model’s performance is not satisfactory, adjustments are made to improve its accuracy. This may involve fine-tuning hyperparameters, changing the model architecture, or employing regularization techniques to prevent overfitting.
- Prediction/Decision Making: Once the model is trained and validated, it can be used to make predictions or decisions on new, unseen data. The model applies the learned patterns to the input variables and produces the desired output or classification.
- Model Deployment: The trained model is deployed into a production environment, where it can be integrated into software systems, applications, or devices to provide real-time predictions or decision-making capabilities.
Throughout this process, iterative cycles of training, evaluation, and optimization are often performed to continually improve the model’s performance. Machine learning algorithms can adapt and learn from new data, enabling them to make more accurate predictions as they encounter more examples.
Types of Machine Learning
Depending on who you ask, there are four different types of machine learning: supervised, semi-supervised, unsupervised and reinforcement. These types are explained in more detail below.
Supervised learning: This involves providing the machine learning algorithm with labeled data. Once the algorithm has learned the patterns and behaviors associated with the labeled data, it can be used to predict the labels for new, unseen data.
Semi-supervised learning: This involves using a combination of labeled and unlabeled data. The algorithm uses the labeled data to learn patterns and apply it to the unlabeled data to make predictions.
Unsupervised learning: This involves using unlabeled data to find patterns and relationships among the data without any prior knowledge or labels. The algorithm discovers the structure and patterns of the data and classifies it accordingly.
Reinforcement learning: This involves the use of reward-based learning. The algorithm learns by receiving feedback on its actions and the results of those actions. The algorithm then adjusts its behavior to maximize the reward or minimize the penalty associated with each action. This type of learning is often used in gaming and robotics to teach machines to adapt to different situations and environments.
The Role of Machine Learning in Cybersecurity
Machine learning can improve cybersecurity by helping teams understand previous attacks and assist in identifying, prioritizing and remediating new attacks. Below are the various domains within cybersecurity where machine learning can be used.
Automating tasks
Machine learning can automate tedious and time-consuming tasks in cyber security, such as intelligence triage, malware analysis, network log analysis, and vulnerability assessments. ML incorporation can help organizations execute tasks faster and address threats more efficiently than solely relying on human capability.
Threat detection and classification
Machine learning algorithms can be used to detect and respond to attacks by analyzing large amounts of security event data to identify patterns of malicious activity. This can be accomplished by feeding the machine learning model with Indicators of Compromise (IOCs) that can help detect and respond to threats in real time. ML classification algorithms can then be used to determine the behavior of malware.
Phishing detection
Phishing detection methods of the past are not quick and precise enough to distinguish between safe and harmful URLs. However, modern predictive URL classification models that utilize machine learning algorithms can recognize certain abnormalities found in email headers, body-content, and punctuation patterns, that signify malicious emails.
Detecting malicious WebShell
WebShell is a harmful code that can be injected into a website to allow attackers to access and manipulate the server’s root directory. Such access may enable them to obtain personal data. ML models can help distinguish between normal and malicious shopping cart behavior.
Detecting user behavior anomalies
User behavior analytics (UBA) employs ML algorithms to categorize user patterns and identify anomalous activity, such as remote access inconsistency, late-night login by employees, or excessive downloads. Such activity is assigned a risk score based on the patterns and behavior of the relevant user.
Risk scoring
Machine learning algorithms can be used to identify the sections of a network that are most susceptible to attacks. This information can be used to create risk scores which help companies to prioritize resources and minimize the chance of further attacks.
Machine Learning Challenges
Machine learning models require very large amounts of training data. For example, to create an algorithm to identify phishing emails, you need examples of both safe and malicious emails. Hundreds, if not thousands, of examples are needed to train a simple algorithm. However, obtaining access to such examples is a common challenge for machine learning specialists. Below are some other notable challenges associated with the use of machine learning for cybersecurity.
Poor data quality: The accuracy and completeness of data sets used for machine learning can significantly affect the accuracy of the results.
Scalability issues: Machine learning algorithms must be scalable in order to handle large datasets and a large number of features.
Inaccurate predictions: Machine learning models must deliver highly accurate predictions.
Transparency and interpretability issues: Machine learning models must be able to provide some degree of transparency and interpretability so that users can understand and trust the results.
Overfitting: Machine learning models can be prone to “overfitting”, which occurs when a model is too closely fitted to the training data and fails to adapt well to new data.
Algorithm selection difficulties: Choosing the right machine learning algorithm for a particular task can be a complex process, requiring experience and expert knowledge.
Negative discrimination: Biases and limitations are inherent in the algorithms used in machine learning, potentially causing harmful or unethical results.
The need for continuous improvement: Machine learning algorithms require continuous improvement to stay current and accurate.
How Lepide Helps
The Lepide Data Security Platform uses machine learning models to discover and classify sensitive data. Once it knows what sensitive data you store, and where it is located, it can identify anomalous user activity by analyzing large amounts of data and identifying patterns of behavior that deviate from normal behavior. For example, if a user typically logs in from the same device and at the same time every day, but suddenly begins logging in from a different device at a different time, that activity would be flagged as anomalous. In which case a real-time alert can be sent to your inbox or mobile app, allowing you to respond to the potential threat in a timely manner.
The Lepide software can also detect and respond to events that match a pre-defined threshold condition. For example, let’s say that a certain number of files were encrypted or renamed within a short period of time. This might suggest that a ransomware attack is underway. In this case, a custom script can be executed automatically which may disable an account or process, change some relevant settings or simply shut down the affected server.
If you’d like to see how the Lepide Data Security Platform uses machine learning to protect your critical assets, schedule a demo with one of our engineers.