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Deep learning which is also known as Deep Neural Network includes machine learning techniques that enable the network to learn from unsupervised data and solve complex problems. It can be extensively used for cybersecurity to protect companies from threats like phishing, spear-phishing, drive-by attack, a password attack, denial of service, etc. Learn about the top 10 applications of deep learning in cybersecurity.
Deep learning, convolutional neural networks, and Recurrent Neural Networks (RNNs) can be applied to create smarter ID/IP systems by analyzing 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).
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 recognize suspicious activities that might indicate the presence of bad actors or malware.
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.
Deep learning ANNs are showing promising results in analyzing 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.
Tracking and analyzing user activities and behaviors is an important deep learning-based security practice for any organization. It is much more challenging than recognizing traditional malicious activities against the networks since it bypasses security measures and often doesn't raise any flags and alerts. 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 recognize suspicious activities, such as accessing the system in unusual hours, that possibly indicate an insider attack and raise alerts.
It is vital to keep an eye on the official Email accounts of the employees to prevent any kind of cyberattacks. For instance, phishing attacks are commonly caused through emails to employees and asking them for sensitive data. Cybersecurity software along with deep learning can be used to avoid these kinds of attacks. Natural language processing can also be used to scan emails for any suspicious behavior.
Deep learning is already going mainstream on mobile devices and is also driving voice-based experiences through mobile assistants. So using deep learning, one can identify and analyze threats against mobile endpoints when the enterprise wants to prevent the growing number of malware on mobile devices.
Deep learning in cybersecurity can help humans to detect malicious attacks, endpoint protection, analyze the network, and do vulnerability assessments. Through this, humans can decide on things better by bringing out ways and means to find the solutions to the problems.
The main benefit of deep learning is to automate repetitive tasks that can enable staff to focus on more important work. There are a few cybersecurity tasks that can be automated with the help of machine learning. By incorporating deep learning into the tasks, organizations can accomplish tasks faster and better.
WebShell is a piece of code that is maliciously loaded into a website to provide access to make modifications on the Webroot of the server. This allows attackers to gain access to the database. Deep learning can help in detecting the normal shopping cart behavior and the model can be trained to differentiate between normal and malicious behavior.
Deep learning can be used to analyze previous cyber-attack datasets and determine what areas of the network were involved in a particular attack. This can help in preventing the attack with respect to a given network area.
