Deep learning and machine learning are both branches of artificial intelligence (AI). They involve building algorithms to analyze and make predictions or decisions based on data. However, there are some significant differences between the two methodologies.
Deep Learning
Deep learning involves building and training artificial neural networks that are capable of learning from large amounts of data. These neural networks are modeled after the structure and function of the human brain. They utilize layers of interconnected nodes that perform mathematical operations on input data. BLOKWORX utilizes deep learning by Deep Instinct to power our endpoint protection product. Our onboarding process allows Deep Instinct’s “brain” to gather information to understand the environments it protects. This lowers false positives and keep day to day business flowing after the cutover to prevention policies.
Deep learning algorithms learn complex representations of data through networks that may have thousands of layers. This has led to breakthroughs in a variety of fields, including computer vision, natural language processing, and cybersecurity. Deep learning algorithms are first trained on a large dataset, typically using a supervised learning approach. During training, the algorithm adjusts the weights and biases of the neural network to minimize the difference between its predictions and the true labels of the training data. Once the network is trained, it can be used to make predictions on new, unseen data such as zero-day vulnerabilities.
Our favorite analogy compares deep learning to how our toddlers learn about dogs. Even if my toddler has only seen poodles, he can go to the park and make an excellent assessment that any creature with 4 legs and an elongated snout is also a dog. We don’t have to teach them every breed under the sun for them to make that conclusion. Their brain made a logical and reasonable assessment based on the initial information provided without additional input.
Deep Learning in Action
People solely utilizing whitelisting and blacklisting for application security learned the value of deep learning with the recent weaponization of a .zip application. All those that had whitelisted this application were now vulnerable to attack. However, deep learning tools, like Deep Instinct, recognized that the application was operating out of baseline and shut it down before any damage could be done. In short, deep learning is the best defense against zero-day attacks.
Machine Learning
Machine learning is a type of artificial intelligence that involves building algorithms that can automatically learn patterns from data and improve their performance on a task over time. Unlike traditional computer programs programmed to follow a set of rules, machine learning algorithms can learn from data and make predictions or decisions based on that data.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Engineers provide correct answers along with the input data to train on labeled data in supervised learning. The algorithm learns to generalize from this data and make predictions on new, unseen data. Classification, regression, and prediction tasks typically utilize supervised learning.
Unsupervised learning, trains the algorithm on unlabeled data and must find patterns and structure within that data on its own. Clustering, anomaly detection, and dimensionality reduction typically utilize this type of learning.
In reinforcement learning, the algorithm learns by interacting with an environment and receiving feedback in the form of rewards or punishments. The algorithm learns to make decisions that maximize its expected reward over time.
Image and speech recognition, natural language processing, fraud detection, and recommendation systems all utilize machine learning algorithms. Some popular machine learning algorithms include decision trees, support vector machines, k-nearest neighbors, and neural networks.
Machine learning is a powerful tool for automating complex tasks and making predictions based on data. It has many applications in business, healthcare, cybersecurity, and other fields. The only drawback to machine learning is it is limited in scope to exactly what it is taught, not making behaviorally based decisions like deep learning.
Deep Learning vs. Machine Learning
In summary, machine learning involves building algorithms that can recognize patterns in data and make predictions based on those patterns, while deep learning involves building and training neural networks to perform more complex tasks over time and has the potential to drive further breakthroughs in the cybersecurity prevention field. BLOKWORX services utilize both machine learning and deep learning, supported by our 24/7 US-Based SOC to keep our partners safe and secure.
