February 21, 2022
5 Amazing Applications of Deep Learning in Cybersecurity
Artificial Intelligence (AI) is revolutionising almost every industry. Deep Learning (DL), an AI methodology, is propelling the high-tech industry to the future with a seemingly endless list of applications ranging from object recognition for systems in autonomous vehicles to potentially saving lives — helping doctors detect and diagnose cancer with greater accuracy.
In this article, we’ll outline some interesting applications of deep learning in cybersecurity and how you can use deep learning to improve security measures within your organisation.
What Is Deep Learning?
Deep learning is a subtype of Machine Learning (ML) and belongs to the broader category of artificial intelligence. Deep learning uses Artificial Neural Networks (ANNs), which are designed to mimic the functionality and connectivity of neurons in the human brain.
Deep learning gets its name because it uses deeper networks compared to other AI methods like ML. The number of layers within an ANN defines the depth of the network. For example, one of the most common types of ANNs is Convolutional Neural Network (CNN), which is used for many computer vision tasks.
In a DL network architecture, the first layer is fed with an input, which passes throughout the different layers of the network. Layers have different functions and scales which change the input as it passes through the layers in a certain order and eventually the network produces an output, a prediction.
Many deep learning frameworks, such as PyTorch and TensorFlow, allow you to create your own deep learning models and run deep learning experiments. If you want to get started with a deep learning framework, both TensorFlow and Pytorch frameworks offer many guides to beginners and enthusiasts starting out. This guide can help you choose which framework you should use.
However, before you start building your models, you should be aware that training a deep learning model involves highly intensive tasks. In fact, the hardware dependencies are mostly related to Graphics Processing Units (GPUs). The cost of GPUs is what held the technology back until recent years. Once these chips became stronger and cheaper, the popularity of deep learning soared.
If you want to make the most out of your deep learning experiments, you can use a deep learning platform, such as MissingLink, to help you manage and automate experiments. With a platform, you can track hyperparameters across multiple experiments, launch experiments automatically and reduce costs of expensive on-premise GPU-clusters or cloud-based GPU services while also saving time.
One of the most common and critical applications for deep learning algorithms is to improve cybersecurity solutions.
Common Cybersecurity Threats and Attacks
Before we discuss how deep learning can help combat cybersecurity threats and the importance and potential of deep learning for cybersecurity, we should first cover a few examples. Here is a list of common threats that cybersecurity teams face today:
- Malware (malicious software)—general term to describe all kinds of software created by bad actors to damage devices, systems, and networks.
- Data breach—this is when an unauthorised user gains access to valuable and confidential data such as user and credit card information.
- Social engineering—attackers use this technique to manipulate users to grant them access or critical data. Attackers can also combine this technique with other cyberattacks to trick users into downloading malware for example.
- Phishing—a form of social engineering and the most common cyber threat. Phishing is the act of sending infected emails or messages cloaked as legitimate to trick victims into giving personal and valuable data or downloading malware.
- Structured Query Language (SQL) injection—a technique used by attackers to leverage vulnerabilities within SQL servers to access the database and run malicious code. The idea behind SQL-i is to force the server to execute code and perform certain actions such as revealing critical and otherwise secret information.
- Denial-of-Service (DOS) attack—attackers use this technique to flood networks and servers with traffic, causing resource drain, and making them unavailable.
- Insider threats—an attack caused by employees or contractors employed by the company. There are many forms of insider threats. In most cases, they aim to target valuable business data.
- Advanced Persistent Threats—attacks capable of evading traditional defensive and perimeter security tools due to their stealthy nature. APTs leverage persistence mechanisms to maintain a foothold within a network, collecting information about your IT environment before executing a triggered or timed cyber attack.
5 Applications of Deep Learning in Cybersecurity
Now that we covered some of the most common threats and cyber attacks cybersecurity teams face, it’s time to explain how deep learning applications can help.
1. Intrusion Detection and Prevention Systems (IDS/IPS)
These systems detect malicious network activities and prevent intruders from accessing the systems and alerts the user. Typically, they are recognised by known signatures and generic attack forms. This is useful against threats like data breaches.
Traditionally, this task was performed by ML algorithms. However, these algorithms caused the system to generate many false-positive, creating tedious work for security teams and causing unnecessary fatigue.
Deep learning, convolutional neural networks and Recurrent Neural Networks (RNNs) can be applied to create smarter ID/IP systems by analysing the traffic with better accuracy, reducing the number of false alerts and helping security teams differentiate bad and good network activities.
Notable solutions include Next-Generation Firewall (NGFW), Web Application Firewall (WAF), and User Entity and Behavior Analytics (UEBA).
2. Dealing with Malware
Traditional malware solutions such as regular firewalls detect malware by using a signature-based detection system. A database of known threats is run by the company which updates it frequently to incorporate new threats that were introduced recently. While this technique is efficient against these threats, it struggles to deal with more advanced threats.
Deep learning algorithms are capable of detecting more advanced threats and are not reliant on remembering known signatures and common attack patterns. Instead, they learn the system and can recognise suspicious activities that might indicate the presence of bad actors or malware.
3. Spam and Social Engineering Detection
Natural Language Processing (NLP), a deep learning technique, can help you to easily detect and deal with spam and other forms of social engineering. NLP learns normal forms of communication and language patterns and uses various statistical models to detect and block spam.
You can read this post to learn how Google used TensorFlow to enhance the spam detection capabilities of Gmail.
4. Network Traffic Analysis
Deep learning ANNs are showing promising results in analysing HTTPS network traffic to look for malicious activities. This is very useful to deal with many cyber threats such as SQL injections and DOS attacks.
5. User Behavior Analytics
Tracking and analysing user activities and behaviors is an important security practice for any organisation. It is much more challenging than recognising traditional malicious activities against the networks since it bypasses security measures and often doesn’t raise any flags and alerts.
For example, when insider threats occur and employees use their legitimate access in malicious intent, they are not infiltrating the system from the outside, which renders many cyber defense tools useless against such attacks.
User and Entity Behavior Analytics (UEBA) is a great tool against such attacks. After a learning period, it can pick up normal employee behavioral patterns and recognise suspicious activities, such as accessing the system in unusual hours, that possibly indicate an insider attack and raise alerts.
Now that you know some of the applications of deep learning in cybersecurity and understand their potential, it is time to start practicing them within your Security Operations Center (SOC).