Excerpt: In aspects of privacy and security practices, cybercrime continues to be a big challenge. Deep learning and cyber security experts collaborated to make significant progress in the sectors of vulnerability scanning, malicious payload analysis, and forensic identity recently. Deep learning, also widely recognized as Deep Neural Network, is a type of machine learning that allows a model to learn from unlabeled data and resolve problems. 

Table of contents:

  • Introduction
  • What do we understand by Deep Learning?
  • Dangers and Threats Against Cybersecurity
  • Discover the top ten deep learning applications in cybersecurity.
  • Final Thoughts


The vast majority of deep learning applications we see in society are aimed toward fields such as marketing, sales, and finance, among others. We rarely come across articles or resources that discuss how deep learning is being used to safeguard these products and the company from spyware and hacker attacks.

It is being used to protect businesses from threats such as phishing, spear-phishing, drive-by attacks, password attacks, denial of service, and so on. Today we are going to look into how deep learning techniques would be used to further cyber security goals bridging the gap between deep learning and cyber security societies.

What do we understand by Deep Learning?

Deep learning ends up getting its name from the fact that it uses more complex networks than other AI methods like machine learning. The depth of an ANN is defined by the number of layers it contains. Artificial Neural Networks (ANNs) are used in deep learning to simulate the features and interconnection of neurons in the human brain.

The first layer of a DL network architecture is nourished with an input that passes through the various layers of the network. Layers have various functions and scales that change the insight as it goes through to the layers in a specific order, and the network eventually generates the output, a prognostication. 

  • Many deep learning methodologies allow you to build your own deep learning models and experiment with deep learning. 
  • However, before you begin building your models, keep in mind that time to train a deep learning model entails a number of time-consuming tasks. 
  • In fact, the majority of the hardware dependencies are related to Graphics Processing Units (GPUs) (GPUs).

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Dangers and Threats Against Cybersecurity

We should go over a few examples before discussing how deep learning is useful to combat cybersecurity risks and the significance and possibilities of deep learning for cybersecurity. Here’s a rundown of the most potent attacks that cybersecurity professionals face today:

  • Malware (malicious software) is a broad term that encompasses all types of software created by bad actors with the intent of causing harm to devices, systems, and networks.
  • Data Breach An unauthorized user gains access to wealth and confidential data, such as user and credit card information, in a data breach.
  • Social Engineering Attackers use social engineering to persuade users to grant them access to sensitive information. Attackers can use this technique in conjunction with other cyberattacks to persuade users to download malware, for example.
  • Denial of Service Attackers uses the denial-of-service (DoS) method to deluge networks and servers with traffic, having caused resource drain and rendering them unusable.
  • Insider threats are attacks perpetrated by company employees or contractors. Insider threats can take many different forms. Most of the time, they are looking for valuable business data.
  • Advanced Persistent Threats (APTs) are attacks that, due to their stealthy nature, are able to elude traditional defending and bounding box security tools. APTs use persistence mechanisms to stay in a network and gather information about your IT surroundings before launching a provoked or synchronized cyber attack.
  •  Phishing is a type of social engineering. Phishing is the practice of sending diseased emails or messages that appear to be legitimate in order to dupe victims into handing over personal information or downloading malware.
  • SQL injection is a method used by attackers to gain access to a database and run malicious code by exploiting vulnerabilities in SQL servers. The goal of SQL-i is to force the server to run code and perform specific actions, such as revealing sensitive and otherwise secret information.

Discover the top ten deep learning applications in cybersecurity

  1. Getting Rid of Malware:

Conventional malware solutions, such as regular firewalls, use a signature-based detection system to detect malware. The company maintains a set of known threats, which is regularly updated to include new threats that have recently been introduced. While this method is effective against these threats, it has difficulty dealing with much more advanced attacks.

Deep learning algorithms can discern more advanced threats without relying on known signatures or attack patterns. Instead, they become familiar with the system and can spot suspicious activity that could confirm the existence of bad actors or malware.

  1. Systems that detect and prevent intrusions.

Deep learning, convolutional neural networks, and Recurrent Neural Networks (RNNs) can all be used to make smarter ID/IP systems by analyzing traffic more accurately, reducing false alerts, and assisting security teams in distinguishing between bad and good network activities. Next-Generation Firewalls (NGFW), Web Application Firewalls (WAF), and User Entity and Pattern Identification are examples of notable solutions (UEBA).

  • ML algorithms have traditionally handled this task. Nevertheless, these algorithms ended up causing the system to generate a large number of false positives, making security teams’ jobs more difficult and creating unnecessary fatigue.
  • Deep learning, convolutional neural networks, and Recurrent Neural Networks (RNNs) can be used to make smarter ID/IP systems by analyzing traffic more accurately, reducing false signals, and assisting security teams in distinguishing between bad and good network elements.
  1. Analyze Network Traffic

Learning at a deeper level In analyzing HTTPS network traffic to look for malicious purposes, ANNs are showing promising results. This is extremely useful in dealing with a variety of cyber threats, such as SQL injections and denial-of-service attacks.

  1. Detection of Spam and Social Engineering

Natural language processing (NLP), a deep learning method, can aid in the detection and management of spam and other forms of social engineering. NLP uses statistical models to detect and prevent spam by learning regular channels of interaction and language patterns.

  1. Analyzing User Behavior

Any organization’s security practice should include tracking and analyzing user activities and behaviours. Because it bypasses security measures and frequently does not raise any flags or alerts, it is much more difficult to detect than traditional malicious activities against networks.

  • When internal threats occur, for example, and employees utilize their legitimate access for malicious purposes, they are not trying to infiltrate the system externally, rendering many cyber defense tools useless.
  • UEBA (User and Entity Behavior Analytics) is an excellent defense against such attacks. After a period of training, it can detect normal employee behaviour patterns as well as questionable activity, such as accessing the system at odd hours, which could imply an insider attack and trigger alerts.
  1. Emails are being monitored.

To avoid any kind of cyberattack, it’s critical to keep an eye on employees’ official email accounts. Phishing attacks, for example, are frequently carried out by sending emails to employees requesting sensitive information. To avoid these types of attacks, cybersecurity software and deep learning can be used. Natural language processing could also be used to detect suspicious behaviour in emails.

  1. WebShell

WebShell is just a piece of code that would be malicious purposes loaded into a website in order to grant access to make changes to the server’s Webroot. As little more than a result, the database is accessible to attackers. Deep learning can aid in the detection of normal shopping cart behaviour, and the model could be trained to distinguish between the two.

  1. Automated Tasks

Deep learning’s primary benefit is that it might automate repetitive tasks, allowing employees to move on to more important tasks. Machine learning can be used to automate a few cybersecurity tasks. Organizations can complete tasks faster and better by trying to incorporate deep learning into the tasks.

  1. Examining Endpoints on Mobile Devices

Deep learning is already commonplace on mobile devices, and mobile assistants are driving voice-based experiences. When a venture wishes to avoid the growing number of malware on mobile devices, deep learning can be used to identify and analyze threats against mobile endpoints.

  1. Network Risk Assessment

Deep learning could be used to analyze previous cyber-attack datasets and figure out which parts of the network were targeted. This can aid in the prevention of an attack in a specific network area.

Final Thoughts

A cyber threat is defined as a successful cyber attack that aims to gain unauthorized access to, damage, disrupt, or steal an information technology asset, network node, intellectual property, or indeed any form of sensitive data. Unknown sides from remote locations, as well as trusted users within an organization, can pose a cyber threat. Cyberthreats are a difficult task that many businesses and organizations are currently dealing with. But the excellent thing is that deep learning has some solutions for overcoming these threats, thanks to a variety of applications that fall under the same category. It’s time to put some of the deep learning applications in cybersecurity into practice in your Security Operations Center now that you’ve managed to learn about them and their potential (SOC).

Author Bio

Meravath Raju is a Digital Marketer, and a passionate writer, who is working with MindMajix, a top global online training provider. He also holds in-depth knowledge of IT and demanding technologies such as Business Intelligence, Salesforce, Cybersecurity, Software Testing, QA, Data analytics, Project Management and ERP tools, etc.