How deep learning could prove useful for cybersecurity

The threat of cyberattacks has increased dramatically recently, and traditional measures now seem insufficiently effective.

Because of this, deep learning in cybersecurity is making rapid progress and may hold the key to solving all cybersecurity problems.

As technology emerges, threats to data security increase, requiring the use of cybersecurity tools to protect an organization's operations. However, businesses are struggling due to their reliance on most cybersecurity tools. Enterprises rely on signatures or evidence of compromise to detect the threat detection capabilities of the technology they use to protect their business. Because they are only useful for identifying recognized risks, these techniques are useless against unknown attacks. This is where deep learning in cybersecurity can change the course of events. Deep learning is a branch of machine learning that is good at using data analysis to solve problems. By having deep neural networks process massive amounts of data that no other machine learning in the world can process, digest, and process, we are mimicking the brain and how it works.

Application of deep learning in network security

The network security industry is facing many challenges, and deep learning technology may be its savior.

Behavioral Analysis

For any enterprise, a deep learning-based security strategy is about tracking and examining user activities and habits. Because it overrides security mechanisms and sometimes does not trigger any signals or alerts, it is harder to detect than traditional malicious behavior targeting a network. For example, insider attacks occur when employees use their legitimate access for malicious purposes rather than breaking into a system from the outside, making many network protection systems ineffective in the face of such attacks.

One effective defense against these attacks is User and Entity Behavior Analysis (UEBA). After a period of adjustment, it can learn the typical behavior patterns of employees and identify suspicious activities that may be insider attacks, such as accessing the system at abnormal times, and sound alerts.

Intrusion Detection

Intrusion detection and prevention systems (IDS/IPS) identify suspicious network activity, prevent hackers from gaining access, and notify users. They often have well-known signatures and common attack formats. This helps protect against risks such as data breaches.

Previously, ML algorithms handled this operation. However, due to these algorithms, the system generates some false positives, which makes the security team's job laborious and adds to already excessive fatigue. Deep learning, convolutional neural networks, and recurrent neural networks (RNN) can be used to develop smarter ID/IP systems by analyzing traffic more accurately, reducing the number of false alerts, and assisting security teams in distinguishing malicious from legitimate network activity.

Dealing with Malware

Traditional malware solutions, such as typical firewalls, use signature-based detection technology to find malware. The business maintains a database of known risks, which is regularly updated to include new hazards that have recently emerged. While this approach is effective against basic threats, it is not effective against more complex threats. Deep learning algorithms can identify more complex threats because they do not rely on memory of known signatures and typical attack techniques. Instead, it becomes familiar with the system and sees strange behavior that may be signs of malware or malicious activity.

Email Monitoring

In order to thwart any form of cybercrime, it is crucial to monitor employees’ official email accounts. For example, phishing attacks are often conducted by sending emails to employees and soliciting sensitive information. Deep learning and cybersecurity software can be used to prevent these types of attacks. Using natural language processing, emails can be checked for any suspicious activity.

Summary

Automation is critical to combating the multitude of risks that businesses must contend with, but plain old machine learning is too limited and still requires a lot of tuning and human involvement to produce the desired results. Deep learning in cybersecurity goes beyond continuous improvement and learning so it can anticipate dangers and stop them before they happen.

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