Nearly every industry is being revolutionized by Artificial Intelligence (AI). With a seemingly endless list of applications, including object recognition for systems in autonomous vehicles and possibly saving lives by helping doctors detect and diagnose cancer with greater accuracy, Deep Learning (DL), an AI methodology, is driving the high-tech industry into the future.

What Exactly Is Deep Learning (DL)?

Deep learning, also referred to as deep neural networks, is a subset of Machine Learning that enables networks to learn from unsupervised data and resolve challenging issues. It has a wide range of applications in cybersecurity, defending businesses against dangers like phishing, spear-phishing, drive-by attacks, password attacks, denial of service, etc.

Artificial Neural Networks (ANNs), which are used in Deep Learning, are made to function and connect like neurons in the human brain.

Because it uses more complex networks than other AI techniques like Machine Learning, Deep Learning gets its name. The depth of an ANN is determined by the number of layers in the network. Convolutional Neural Networks (CNN), for instance, are among the most popular ANN types and are employed in many computer vision tasks.

Deep Learning For Cybersecurity

Using Machine Learning methods to address computer security issues is not a novel concept. Still, the rapidly developing Deep Learning technology has recently generated significant interest in the field.

Deep Learning applications are already being used to solve computer security issues such as malware classification, system-event-based anomaly detection, control-flow integrity (CFI), achieving return-oriented programming (ROP) attack defense, defending network attack defense, memory forensics, and fuzzing for software security.

Amazing Applications Of Deep Learning for Cyber Security

The following are a few of the top applications of Deep Learning for cybersecurity:

• Intrusion Detection and Prevention Systems (IDS/IPS)

These systems alert users and stop unauthorized users from accessing the systems by detecting malicious network activity. Well-known signatures and standard attack formats typically identify them. This helps defend against risks like data breaches.

Traditionally, ML algorithms handled this task. The system generated numerous false positives due to these algorithms, which made security teams’ work tedious and added to their already excessive exhaustion.

By more accurately analyzing the traffic, lowering the number of erroneous alerts, and assisting security teams in differentiating between malicious and lawful network activity, deep learning, convolutional neural networks, and recurrent neural networks (RNNs) can be used to develop more innovative ID/IP systems.

• Spam and Social Engineering Detection

Natural Language Processing (NLP), a Deep Learning technique, can assist you in quickly identifying and dealing with spam and other types of social engineering. NLP uses various statistical models, typical communication forms, and language patterns to identify and block spam.

• Dealing with Malware

The company maintains a database of known threats, regularly updated to include brand-new threats that have recently emerged. Conventional malware solutions like standard firewalls use a signature-based detection system to find malware. Although this method is effective against these threats, it fails to counter more sophisticated threats.

Deep learning algorithms can identify more complex threats because they are not dependent on the memory of well-known signatures and typical attack patterns. Instead, they become familiar with the system and can spot odd behavior that might indicate malware or malicious actors.

• Analysis of Network Traffic

In-depth learning When analyzing HTTPS network traffic to look for malicious activity, ANNs demonstrate promising results. Dealing with numerous online threats, including SQL injections and DOS attacks, is made much easier by this.

• User Behavior Analytics 

Any organization should follow the security best practices of tracking and analyzing user actions. Since it gets around security measures and frequently doesn’t trigger any flags or alerts, it is much harder to spot than traditional malicious activities against networks.

For instance, insider threats happen when employees use their legitimate access for malicious purposes rather than breaking into the system from the outside, making many cyber defense tools useless in the face of such attacks.

One effective defense against these attacks is User and Entity Behavior Analytics (UEBA). After a period of adjustment, it can learn the typical patterns of employee behavior, identify suspicious activity that may be an insider attack, such as accessing the system at odd hours and raise alarms.

Conclusion 

Automation is essential for defending against the enormous volume of threats businesses must deal with. However, standard Machine Learning is too constrained and still needs a lot of tweaking and human involvement to produce the desired results. Deep Learning goes above and beyond to keep improving and learning over time to foresee threats and stop them before they happen.