6 Deep Learning Techniques for Stronger Cybersecurity

With new technological developments, Cybersecurity is turning into one of the biggest concerns of the modern era. To help with the same, some experts have advised about the usage of deep learnings for cybersecurity. Even though techniques like firewalls, antiviruses, intrusion detection systems (IDSs), etc, are existing; only one weakness is enough to make the data vulnerable to security threats.To increase the data protection probability, deep learning supported by Artificial Intelligence came into existence. Deep learning is a small but crucial part of Machine learning. It copies the human brain and follows the exact functionality and connectivity. It is expected that with the help of deep learning techniques, AI will automate the cybersecurity process shortly.Moreover, AI learning can make security protocols easier and much efficient. By feeding it multiple types of data, AI can be used to detect fraud, compromised transactions, unauthorized purchases, etc.

Types of Cyber Security Threats Users Might Face

Before we proceed further and discuss deep learning techniques to use against cybersecurity, let’s talk about a few , hackers might try to steal your source codes or even the data of patients collected through your app. Data breaching is mainly done to lock your files to ask for the ransom, or in some cases, to steal your customers’ information. However, data breaching can ruin your business if not handled properly.

4. Malware

Malware is used to lock or corrupt system files. In this case, hackers install a virus or malicious software in the system to damage files. In some cases, hackers lock files temporarily to ask for ransom in return for unlocking them back. In short, the purpose is to take control of your devices and the data.

Deep Learning Techniques for Cybersecurity

Now, after understanding a few types of security threats, I would like to talk about a few DL techniques that can give your cybersecurity policies a boost.

1. Deep Autoencoders Technique

Autoencoders use data examples to detect anomalies in the data pattern. With the help of some specific algorithms, deep autoencoders can analyze the input data and compress it to preserve it safely. Moreover, autoencoders are also capable of building new data with different properties. 

2. Vulnerability detection and management

Deep learning is capable enough to analyze the data and find vulnerabilities to remove them. It can track and keep a record of the data and its behavior. Moreover, it can also detect changes or any unsafe input in the data from any unrecognized sources. The technique can focus on servers and endpoints as well to find any distinguished and suspicious pattern.

3. Network management and security

The network makes the data most vulnerable. Deep learning can learn and set-up a few protocols to manage the same. However, if any unsafe or suspicious network activity occurs, the process can disallow it automatically before it’s too late. The deep learning network can analyze the network pattern and warn against unusual behaviors. Moreover, it is also capable of detecting bad gateways and blocking them from extracting the data.

4. AI managed data centers

Now, AI is smart enough to make sure that data storage devices are functioning properly. If there are any possible abnormalities in hardware components, AI can inform about it beforehand to protect the data. Moreover, backups stored by AI-supported systems can make sure that data retrieval is easy.

5. Cybersecurity datasets to involve in deep learning

Knowledge, Discovery, and Dissemination (KDD) was created with 4 million network traffic records. It is capable of recognizing 22 different types of attacks. Some of these attacks might fall under categories like unauthorized remote machine access, denial of services, etc.

6. Cyber Security Metrics

These metrics are classified into different segments with a lot of data like false positive rate, accuracy, precision rates, etc; the deep learning metrics can compare different classes of the data to identify any abnormalities.

Cybersecurity Protocols Are Necessary

The need for strong cybersecurity protocols is expanding rapidly due to their application in many sectors like IT, finances, banking, defence, etc. With the digital transformation of almost everything, data is more exposed to cyber-attacks. Therefore, AI with the help of deep learning techniques can have a crucial impact to fight against such threats.