Natural language understanding (NLU) is a subfield of artificial intelligence that focuses on enabling machines to understand and interact with humans in their own natural language.
Natural language understanding (NLU), a subset of Natural Language Processing (NLP), is a field of artificial intelligence that focuses on enabling machines to understand and interact with humans in their own natural language. NLU processes linguistic input from the user and interprets it into structured data that can be used by computer applications. For instance, when a user says, “What’s the weather like today?”, NLU is able to recognize that the user is asking for a particular type of information and can then provide an appropriate response. NLU systems are used in various applications such as virtual assistants, chatbots, language translation services, text-to-speech synthesis systems, and question-answering systems.
Natural Language Understanding (NLU) works by using a combination of techniques such as natural language processing (NLP), machine learning, and deep learning. NLU systems are able to understand the context of a given sentence or conversation and then interpret it in a way that is meaningful for the application. The steps in NLU include:
1. Gathering the text data
2. Pre-processing the data
3. Extracting information and features
4. Modeling the data
5. Answering queries and providing insights
Natural language understanding (NLU) can help improve the accuracy and efficiency of cybersecurity systems by automatically recognizing patterns in languages, such as slang or dialects, to categorize potential threats.
Phishing is a common type of cyberattack that uses deceptive emails to trick people into providing sensitive information or clicking malicious links. NLU can help detect 'malicious intent' within phishing attacks by analyzing both the content and context of incoming messages, commonly found in advanced Business Email Compromise (BEC) attacks. For example, NLU can analyze the text of an email to determine if it contains language associated with phishing attempts, such as requests for login information or clicking suspicious links.
Natural language understanding can also detect inconsistencies between the sender’s email address and the content of the message that could indicate a phishing attack. By detecting these anomalies, NLU can help protect users from malicious phishing attempts.
NLU powered by neural networks helps determine the intent of an email by scanning language usage for topic and sentiment. The IRONSCALES email security solution uses NLU to determine commonly fraudulent messaging used in social engineering messages, which include employee availability checks, requests for unspecific tasks, gift card requests, and solicitations for direct deposits, payments, bank details, among other key indicators.
Additionally, natural language understanding makes it easier for organizations to protect against impersonations of senior executives and high-level managers because the technology scrapes metadata to scan for words and phrases commonly associated with those roles.
While this may appear complicated to defend against in reality, the IRONSCALES platform was purposefully built to mitigate these types of attacks. And by deploying computer vision alongside NLU, the self-learning email security platform is the only one on the market able to help customers automatically identify the “what” and the “who” of a malicious message. Read our blog post to learn more.
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