What is Deep Learning?

Using deep learning software and technology, we are able to identify even the slightest mutations and evasion techniques, to detect and prevent zero-days and APTs in real-time.
Deep Instinct’s solution is based on a two-phase approach; similar to the way the brain learns and then acts in an instinctive mode.

• Training phase: The training process is performed with hundreds of millions of malicious and legitimate files that take place at Deep Instinct’s headquarters. The output of this process is the prediction model.

• Prediction phase: Once a device has the deep learning prediction model (D-Brain), it becomes an autonomous analysis entity, allowing it to predict in real-time malicious intents and prevent them at a pre-execution level. There is no need for any supplementary analysis in a remote server or sandboxing appliance.

The entire analysis and the determination of whether it is malicious or benign, are done on the device within milliseconds.

Several years ago, sandboxing was the most innovative solution for detecting cyber threats. In time, hackers have learned to determine which features a sandboxing solution analyzes or detects when malware is running in a sandbox environment. 30% of zero-day attacks are sandboxing evasive, which means that malicious files can avoid sandboxing detection.

In addition, sandboxing solutions take a minimum of a few minutes to determine the maliciousness of unknown files; so the malware may cause delays when users are performing typical activities, such as receiving emails (attachments need to be analyzed by the sandbox prior to receiving the email). Deep Instinct’s solution only takes a few milliseconds to make this determination and therefore causes no discernable business interference.

Due to these delays, sandboxing solutions are typically implemented with detection-only capabilities.

Deep Instinct’s solution uses a technology that does not rely on known threat methods, such as signatures or heuristics. It detects malicious threats regardless of whether they are known or unknown. In fact, the solution does not even know whether the file is known or unknown.

Yes. Deep Instinct can detect installed malicious applications on iOS devices.

No, Deep Instinct provides the customer with a solution that has already been trained and provides immediate protection. All training is performed at Deep Instinct’s lab.

Sure. Deep Instinct can detect MitM attacks on Android and iOS devices.

Deep Instinct’s deep learning neural network algorithm sets it apart from anything offered by competitors, but creating a deep learning framework for cybersecurity isn’t easy.
First: The deep learning model needs data to train, but with more than 350K new types of malware generated daily, there are hundreds of millions of malicious data files in which to train.
Then: Finding the deep learning experts needed to develop the model is challenging because few know the technology well enough to be able to modify the algorithm for cybersecurity.
Finally: Deep Instinct creates its deep learning framework from scratch, which requires a software library that enables the writing of deep learning models, programming and running them.