Almost every sector is being transformed by artificial intelligence (AI). Deep Learning (DL), an AI methodology, is boosting the high-tech industry forward with a seemingly endless list of applications spanning object recognition to systems in autonomous vehicles to possibly saving lives by assisting doctors in detecting and diagnosing cancer with better accuracy.
In this post, we’ll look at some intriguing deep learning applications in cybersecurity, as well as how you might utilize deep learning to strengthen security measures inside your business.
What Is Deep Learning?
Deep learning is a subclass of Machine Learning (ML) that falls under the umbrella of artificial intelligence. Deep learning employs Artificial Neural Networks (ANNs), which are intended to replicate the functioning and connections of nerve cells in the brain.
Deep learning gets its name from the fact that it employs larger networks than other AI approaches such as ML. The depth of a network is defined by the number of layers within an ANN. Convolutional Neural Network (CNN), for instance, is among the most prevalent forms of ANNs and is utilized for several computer vision applications.
In a DL network design, the first layer receives an input that travels through the network’s levels. Layers contain distinct functions and scales that modify the input as it travels through the levels in a specific order, and the network finally generates an output, a prediction.
Many deep learning frameworks, such as PyTorch and TensorFlow, allow you to build and test your own deep learning models. If you would like to get started with such a deep learning framework, both Pytorch and TensorFlow provide several resources for beginners and aficionados. This information can assist you in determining which framework to employ.
However, before you begin creating your models, you must be mindful that learning a deep learning model entails a number of time-consuming processes. In reality, the majority of hardware requirements are connected to Graphics Processing Units (GPUs). The expense of GPUs was what hindered the technology until recently. Deep learning gained popularity as these processors grew more powerful and less expensive.
If you want to get the most out of your deep learning research, a deep learning platform like MissingLink can help you organize and automate them. With a platform, you can manage hyperparameters across numerous trials, run experiments automatically, and save money on pricey on-premise GPU clusters or cloud-based GPU solutions while also improving efficiency.
Deep learning algorithms are used to improve cybersecurity solutions, which is one of their most prevalent and essential applications.
Common Cybersecurity Attacks and Threats
Before we get into how deep learning may assist battle cybersecurity threats, as well as the relevance and promise of deep learning for cybersecurity, let’s look at a few instances. The following are some of the most prevalent risks that cybersecurity groups face today:
Applications Of Deep Learning
Now that we’ve addressed some of the most frequent dangers and cyber assaults that cybersecurity groups face, let’s look at how deep learning applications might assist:
These systems identify harmful network activity, prevent attackers from gaining access to the systems, and notify the user. They are often identified by known signatures and generic attack types. This is important in the face of dangers such as data breaches.
ML algorithms have traditionally handled this task. However, these methods allowed the system to create a large number of false positives, making security professionals’ work laborious and incurring unneeded weariness.
Deep learning, convolutional neural networks (CNNs), and Recurrent Neural Networks (RNNs) may be used to develop smarter ID/IP systems by analyzing traffic more accurately, lowering the number of false warnings, and assisting security teams in differentiating between malicious and good network activity.
Web Application Firewall (WAF), Next-Generation Firewall (NGFW), and User Entity and Behavior Analytics(UEBA) are examples of notable solutions.
Traditional malware countermeasures, such as ordinary firewalls, use a signature-based detection approach to detect malware. The business maintains a database of known risks, which is constantly updated to include new threats that have just been disclosed. While this method is effective against basic attacks, it falls short against more sophisticated threats.
Deep learning algorithms can identify more sophisticated attacks and are not dependent on memorizing known signatures and typical attack patterns. Instead, they become acquainted with the network and are capable of detecting unusual actions that may signal the existence of rogue actors or malware.
Natural Language Processing (NLP), a deep learning approach, may assist you in detecting and dealing with spam and other types of social engineering. NLP recognizes and blocks spam by learning regular methods of communication as well as linguistic patterns.
Deep Learning ANNs are exhibiting promising outcomes when it comes to monitoring HTTPS network data for malicious activity. This is quite beneficial in dealing with numerous cyber dangers like SQL injections and denial-of-service attacks.
Tracking and analyzing user actions and behaviors is critical for any organization’s cybersecurity. It is far more difficult to detect than typical malicious network activity since it circumvents security safeguards and frequently does not raise any flags or warnings.
When insider threats arise, for example, when workers utilize their lawful access with malevolent intent, they are not entering the system externally, rendering many cyber defense solutions ineffective against such assaults.
Now that you’ve learned about some of the deep learning applications in cybersecurity and understood their potential, it’s important to put them into practice within your Security Operations Center (SOC).